Relevant bibliographies by topics / Graph Attention Networks

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Academic literature on the topic 'Graph Attention Networks'

Author: Grafiati
Published: 26 April 2025

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Journal articles on the topic "Graph Attention Networks"

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Wu, Nan, and Chaofan Wang. "Ensemble Graph Attention Networks." Transactions on Machine Learning and Artificial Intelligence 10, no. 3 (June 12, 2022): 29–41. http://dx.doi.org/10.14738/tmlai.103.12399.

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Graph neural networks have demonstrated its success in many applications on graph-structured data. Many efforts have been devoted to elaborating new network architectures and learning algorithms over the past decade. The exploration of applying ensemble learning techniques to enhance existing graph algorithms have been overlooked. In this work, we propose a simple generic bagging-based ensemble learning strategy which is applicable to any backbone graph models. We then propose two ensemble graph neural network models – Ensemble-GAT and Ensemble-HetGAT by applying the ensemble strategy to the graph attention network (GAT), and a heterogeneous graph attention network (HetGAT). We demonstrate the effectiveness of the proposed ensemble strategy on GAT and HetGAT through comprehensive experiments with four real-world homogeneous graph datasets and three real-world heterogeneous graph datasets on node classification tasks. The proposed Ensemble-GAT and Ensemble-HetGAT outperform the state-of-the-art graph neural network and heterogeneous graph neural network models on most of the benchmark datasets. The proposed ensemble strategy also alleviates the over-smoothing problem in GAT and HetGAT.
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Murzin, M. V., I. A. Kulikov, and N. A. Zhukova. "Methods for Constructing Graph Neural Networks." LETI Transactions on Electrical Engineering & Computer Science 17, no. 10 (2024): 40–48. https://doi.org/10.32603/2071-8985-2024-17-10-40-48.

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Discusses an approach to classifying graph neural networks in terms of basic concepts. In addition, the fundamentals of convolutional graph neural networks, Graph attentional neural networks, recurrent graph neural networks, graph automatic encoders, and spatial-temporal graph neural networks are discussed. On the example of Cora dataset, a comparison of neural network models presented in TensorFlow, PyTorch libraries, as well as the model of graph neural network of attention for the task of classification of nodes of the knowledge graph, is carried out. The efficiency of using graph attention networks to solve the problem of graph node classification is shown.
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Sheng, Jinfang, Yufeng Zhang, Bin Wang, and Yaoxing Chang. "MGATs: Motif-Based Graph Attention Networks." Mathematics 12, no. 2 (January 16, 2024): 293. http://dx.doi.org/10.3390/math12020293.

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In recent years, graph convolutional neural networks (GCNs) have become a popular research topic due to their outstanding performance in various complex network data mining tasks. However, current research on graph neural networks lacks understanding of the high-order structural features of networks, focusing mostly on node features and first-order neighbor features. This article proposes two new models, MGAT and MGATv2, by introducing high-order structure motifs that frequently appear in networks and combining them with graph attention mechanisms. By introducing a mixed information matrix based on motifs, the generation process of graph attention coefficients is improved, allowing the model to capture higher-order structural features. Compared with the latest research on various graph neural networks, both MGAT and MGATv2 achieve good results in node classification tasks. Furthermore, through various experimental studies on real datasets, we demonstrate that the introduction of network structural motifs can effectively enhance the expressive power of graph neural networks, indicating that both high-order structural features and attribute features are important components of network feature learning.
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Chatzianastasis, Michail, Johannes Lutzeyer, George Dasoulas, and Michalis Vazirgiannis. "Graph Ordering Attention Networks." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (June 26, 2023): 7006–14. http://dx.doi.org/10.1609/aaai.v37i6.25856.

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Graph Neural Networks (GNNs) have been successfully used in many problems involving graph-structured data, achieving state-of-the-art performance. GNNs typically employ a message-passing scheme, in which every node aggregates information from its neighbors using a permutation-invariant aggregation function. Standard well-examined choices such as the mean or sum aggregation functions have limited capabilities, as they are not able to capture interactions among neighbors. In this work, we formalize these interactions using an information-theoretic framework that notably includes synergistic information. Driven by this definition, we introduce the Graph Ordering Attention (GOAT) layer, a novel GNN component that captures interactions between nodes in a neighborhood. This is achieved by learning local node orderings via an attention mechanism and processing the ordered representations using a recurrent neural network aggregator. This design allows us to make use of a permutation-sensitive aggregator while maintaining the permutation-equivariance of the proposed GOAT layer. The GOAT model demonstrates its increased performance in modeling graph metrics that capture complex information, such as the betweenness centrality and the effective size of a node. In practical use-cases, its superior modeling capability is confirmed through its success in several real-world node classification benchmarks.
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Li, Yu, Yuan Tian, Jiawei Zhang, and Yi Chang. "Learning Signed Network Embedding via Graph Attention." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4772–79. http://dx.doi.org/10.1609/aaai.v34i04.5911.

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Learning the low-dimensional representations of graphs (i.e., network embedding) plays a critical role in network analysis and facilitates many downstream tasks. Recently graph convolutional networks (GCNs) have revolutionized the field of network embedding, and led to state-of-the-art performance in network analysis tasks such as link prediction and node classification. Nevertheless, most of the existing GCN-based network embedding methods are proposed for unsigned networks. However, in the real world, some of the networks are signed, where the links are annotated with different polarities, e.g., positive vs. negative. Since negative links may have different properties from the positive ones and can also significantly affect the quality of network embedding. Thus in this paper, we propose a novel network embedding framework SNEA to learn Signed Network Embedding via graph Attention. In particular, we propose a masked self-attentional layer, which leverages self-attention mechanism to estimate the importance coefficient for pair of nodes connected by different type of links during the embedding aggregation process. Then SNEA utilizes the masked self-attentional layers to aggregate more important information from neighboring nodes to generate the node embeddings based on balance theory. Experimental results demonstrate the effectiveness of the proposed framework through signed link prediction task on several real-world signed network datasets.
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Wang, Bin, Yu Chen, Jinfang Sheng, and Zhengkun He. "Attributed Graph Embedding Based on Attention with Cluster." Mathematics 10, no. 23 (December 1, 2022): 4563. http://dx.doi.org/10.3390/math10234563.

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Graph embedding is of great significance for the research and analysis of graphs. Graph embedding aims to map nodes in the network to low-dimensional vectors while preserving information in the original graph of nodes. In recent years, the appearance of graph neural networks has significantly improved the accuracy of graph embedding. However, the influence of clusters was not considered in existing graph neural network (GNN)-based methods, so this paper proposes a new method to incorporate the influence of clusters into the generation of graph embedding. We use the attention mechanism to pass the message of the cluster pooled result and integrate the whole process into the graph autoencoder as the third layer of the encoder. The experimental results show that our model has made great improvement over the baseline methods in the node clustering and link prediction tasks, demonstrating that the embeddings generated by our model have excellent expressiveness.
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Li, Zitong, Xiang Cheng, Lixiao Sun, Ji Zhang, and Bing Chen. "A Hierarchical Approach for Advanced Persistent Threat Detection with Attention-Based Graph Neural Networks." Security and Communication Networks 2021 (May 4, 2021): 1–14. http://dx.doi.org/10.1155/2021/9961342.

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Advanced Persistent Threats (APTs) are the most sophisticated attacks for modern information systems. Currently, more and more researchers begin to focus on graph-based anomaly detection methods that leverage graph data to model normal behaviors and detect outliers for defending against APTs. However, previous studies of provenance graphs mainly concentrate on system calls, leading to difficulties in modeling network behaviors. Coarse-grained correlation graphs depend on handcrafted graph construction rules and, thus, cannot adequately explore log node attributes. Besides, the traditional Graph Neural Networks (GNNs) fail to consider meaningful edge features and are difficult to perform heterogeneous graphs embedding. To overcome the limitations of the existing approaches, we present a hierarchical approach for APT detection with novel attention-based GNNs. We propose a metapath aggregated GNN for provenance graph embedding and an edge enhanced GNN for host interactive graph embedding; thus, APT behaviors can be captured at both the system and network levels. A novel enhancement mechanism is also introduced to dynamically update the detection model in the hierarchical detection framework. Evaluations show that the proposed method outperforms the state-of-the-art baselines in APT detection.
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Chen, Lu, Boer Lv, Chi Wang, Su Zhu, Bowen Tan, and Kai Yu. "Schema-Guided Multi-Domain Dialogue State Tracking with Graph Attention Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 7521–28. http://dx.doi.org/10.1609/aaai.v34i05.6250.

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Dialogue state tracking (DST) aims at estimating the current dialogue state given all the preceding conversation. For multi-domain DST, the data sparsity problem is also a major obstacle due to the increased number of state candidates. Existing approaches generally predict the value for each slot independently and do not consider slot relations, which may aggravate the data sparsity problem. In this paper, we propose a Schema-guided multi-domain dialogue State Tracker with graph attention networks (SST) that predicts dialogue states from dialogue utterances and schema graphs which contain slot relations in edges. We also introduce a graph attention matching network to fuse information from utterances and graphs, and a recurrent graph attention network to control state updating. Experiment results show that our approach obtains new state-of-the-art performance on both MultiWOZ 2.0 and MultiWOZ 2.1 benchmarks.
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Liu, Jie, Lingyun Song, Li Gao, and Xuequn Shang. "MMAN: Metapath Based Multi-Level Graph Attention Networks for Heterogeneous Network Embedding (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 13005–6. http://dx.doi.org/10.1609/aaai.v36i11.21639.

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Current Heterogeneous Network Embedding (HNE) models can be roughly divided into two types, i.e., relation-aware and metapath-aware models. However, they either fail to represent the non-pairwise relations in heterogeneous graph, or only capable of capturing local information around target node. In this paper, we propose a metapath based multilevel graph attention networks (MMAN) to jointly learn node embeddings on two substructures, i.e., metapath based graphs and hypergraphs extracted from original heterogeneous graph. Extensive experiments on three benchmark datasets for node classification and node clustering demonstrate the superiority of MMAN over the state-of-the-art works.
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Wang, Rui, Bicheng Li, Shengwei Hu, Wenqian Du, and Min Zhang. "Knowledge Graph Embedding via Graph Attenuated Attention Networks." IEEE Access 8 (2020): 5212–24. http://dx.doi.org/10.1109/access.2019.2963367.

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Dissertations / Theses on the topic "Graph Attention Networks"

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Guo, Dalu. "Attention Networks in Visual Question Answering and Visual Dialog." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/25079.

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Attention is a substantial mechanism for human to process massive data. It omits the trivial parts and focuses on the important ones. For example, we only need to remember the keywords in a long sentence and the principal objects in an image for rebuilding the sources. Therefore, it is crucial to building an attention network for artificial intelligence to solve the problem as human. This mechanism has been fully explored in the text-based tasks, such as language translation, reading comprehension, and sentimental analysis, as well as the visual-based tasks, such as image recognition, object detection, and action recognition. In this work, we explore the attention mechanism in the multi-modal tasks, which involve the inputs of both text and image, i.e. visual question answering and visual dialog. It involves three vital components in both tasks, the input question (with history for visual dialog), the given image, and the generated answers. Therefore, three kinds of relationships should be investigated step by step to solve the problem. We first build the attention between words and objects for generating the representation of them, followed by the relationship between the representation and the answers if the general word embedding does not work properly, and the relationship between the representation and the attributes of answers comes last for few-shot learning. First, the bilinear graph networks revisit the relationship between the words from question and objects for image in the visual question answering task from a graph perspective. The classical bilinear attention networks build a bilinear attention map to extract the joint representation of words and objects but lack fully exploring the relationship between words for complex reasoning. In contrast, our networks model the context of the joint embeddings of words and objects. Two kinds of graphs are investigated, namely image-graph and question-graph. The image-graph transfers features of the detected objects to their related query words, enabling the output nodes to have both semantic and factual information. The question-graph exchanges information between these output nodes from image-graph to amplify the implicit yet important relationship between objects. These two kinds of graphs cooperate with each other, and thus our resulting model can model the relationship and dependency between objects, which leads to the realization of multi-step reasoning. Then, our novel image-question-answer synergistic network values the role of the answer for precise visual dialog. We extend the traditional one-stage solution to a two-stage solution. In the first stage, candidate answers are coarsely scored according to their relevance to the image and question pair. Afterward, in the second stage, answers with high probability of being correct are re-ranked by synergizing with image and question. Finally, we propose to learn the representations of attributes from the answers with enough data, which are later composed to constrain the learning of the few-shot ones. We generate the few-shot dataset of VQA with a variety of answers and extract their attributes without any human effort. With this dataset, we build our attribute network to disentangle the attributes by learning their features from parts of the image instead of the whole one.
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Dronzeková, Michaela. "Analýza polygonálních modelů pomocí neuronových sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2020. http://www.nusl.cz/ntk/nusl-417253.

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This thesis deals with rotation estimation of 3D model of human jaw. It describes and compares methods for direct analysis od 3D models as well as method to analyze model using rasterization. To evaluate perfomance of proposed method, a metric that computes number of cases when prediction was less than 30° from ground truth is used. Proposed method that uses rasterization, takes  three x-ray views of model as an input and processes it with convolutional network. It achieves best preformance, 99% with described metric. Method to directly analyze polygonal model as a sequence uses attention mechanism to do so and was inspired by transformer architecture. A special pooling function was proposed for this network that decreases memory requirements of the network. This method achieves 88%, but does not use rasterization and can process polygonal model directly. It is not as good as rasterization method with x-ray display, byt it is better than rasterization method with model not rendered as x-ray.  The last method uses graph representation of mesh. Graph network had problems with overfitting, that is why it did not get good results and I think this method is not very suitable for analyzing plygonal model.
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Lee, John Boaz T. "Deep Learning on Graph-structured Data." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-dissertations/570.

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In recent years, deep learning has made a significant impact in various fields – helping to push the state-of-the-art forward in many application domains. Convolutional Neural Networks (CNN) have been applied successfully to tasks such as visual object detection, image super-resolution, and video action recognition while Long Short-term Memory (LSTM) and Transformer networks have been used to solve a variety of challenging tasks in natural language processing. However, these popular deep learning architectures (i.e., CNNs, LSTMs, and Transformers) can only handle data that can be represented as grids or sequences. Due to this limitation, many existing deep learning approaches do not generalize to problem domains where the data is represented as graphs – social networks in social network analysis or molecular graphs in chemoinformatics, for instance. The goal of this thesis is to help bridge the gap by studying deep learning solutions that can handle graph data naturally. In particular, we explore deep learning-based approaches in the following areas. 1. Graph Attention. In the real-world, graphs can be both large – with many complex patterns – and noisy which can pose a problem for effective graph mining. An effective way to deal with this issue is to use an attention-based deep learning model. An attention mechanism allows the model to focus on task-relevant parts of the graph which helps the model make better decisions. We introduce a model for graph classification which uses an attention-guided walk to bias exploration towards more task-relevant parts of the graph. For the task of node classification, we study a different model – one with an attention mechanism which allows each node to select the most task-relevant neighborhood to integrate information from. 2. Graph Representation Learning. Graph representation learning seeks to learn a mapping that embeds nodes, and even entire graphs, as points in a low-dimensional continuous space. The function is optimized such that the geometric distance between objects in the embedding space reflect some sort of similarity based on the structure of the original graph(s). We study the problem of learning time-respecting embeddings for nodes in a dynamic network. 3. Brain Network Discovery. One of the fundamental tasks in functional brain analysis is the task of brain network discovery. The brain is a complex structure which is made up of various brain regions, many of which interact with each other. The objective of brain network discovery is two-fold. First, we wish to partition voxels – from a functional Magnetic Resonance Imaging scan – into functionally and spatially cohesive regions (i.e., nodes). Second, we want to identify the relationships (i.e., edges) between the discovered regions. We introduce a deep learning model which learns to construct a group-cohesive partition of voxels from the scans of multiple individuals in the same group. We then introduce a second model which can recover a hierarchical set of brain regions, allowing us to examine the functional organization of the brain at different levels of granularity. Finally, we propose a model for the problem of unified and group-contrasting edge discovery which aims to discover discriminative brain networks that can help us to better distinguish between samples from different classes.
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You, Di. "Attributed Multi-Relational Attention Network for Fact-checking URL Recommendation." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-theses/1321.

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To combat fake news, researchers mostly focused on detecting fake news and journalists built and maintained fact-checking sites (e.g., Snopes.com and Politifact.com). However, fake news dissemination has been greatly promoted by social media sites, and these fact-checking sites have not been fully utilized. To overcome these problems and complement existing methods against fake news, in this thesis, we propose a deep-learning based fact-checking URL recommender system to mitigate impact of fake news in social media sites such as Twitter and Facebook. In particular, our proposed framework consists of a multi-relational attentive module and a heterogeneous graph attention network to learn complex/semantic relationship between user-URL pairs, user-user pairs, and URL-URL pairs. Extensive experiments on a real-world dataset show that our proposed framework outperforms seven state-of-the-art recommendation models, achieving at least 3~5.3% improvement.
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Mazzieri, Diego. "Machine Learning for combinatorial optimization: the case of Vehicle Routing." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24688/.

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The Vehicle Routing Problem (VRP) is one of the most intensively studied combinatorial optimization problems in the Operations Research (OR) community. Its relevance is not only related to the various real-world applications it deals with, but to its inherent complexity being an NP-hard problem. From its original formulation more than 60 years ago, numerous mathematical models and algorithms have been proposed to solve VRP. The most recent trend is to leverage Machine Learning (ML) in conjunction with these traditional approaches to enhance their performance. In particular, this work investigates the use of ML-driven components as destroy or repair methods inside the Large Neighborhood Search (LNS) metaheuristic, trying to understand if, where, and when it is effective to apply them in the context of VRP. For these purposes, we propose NeuRouting, an open-source hybridization framework aimed at facilitating the integration between ML and LNS. Regarding the destroy phase, we adopt a Graph Neural Network (GNN) assisted heuristic, which we hybridize with a neural repair methodology taken from the literature. We investigate this integration both on its own and as part of an Adaptive Large Neighborhood Search (ALNS), performing an empirical study on instances of various sizes and against some traditional solvers.
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Gullstrand, Mattias, and Stefan Maraš. "Using Graph Neural Networks for Track Classification and Time Determination of Primary Vertices in the ATLAS Experiment." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-288505.

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Starting in 2027, the high-luminosity Large Hadron Collider (HL-LHC) will begin operation and allow higher-precision measurements and searches for new physics processes between elementary particles. One central problem that arises in the ATLAS detector when reconstructing event information is to separate the rare and interesting hard scatter (HS) interactions from uninteresting pileup (PU) interactions in a spatially compact environment. This problem becomes even harder to solve at higher luminosities. This project relies on leveraging the time dimension and determining a time of the HS interactions to separate them from PU interactions by using information measured by the upcoming High-Granularity Timing Detector (HGTD). The current method relies on using a boosted decision tree (BDT) together with the timing information from the HGTD to determine a time. We suggest a novel approach of utilizing a graph attentional network (GAT) where each bunch-crossing is represented as a graph of tracks and the properties of the GAT are applied on a track level to inspect if such a model can outperform the current BDT. Our results show that we are able to replicate the results of the BDT and even improve some metrics at the expense of increasing the uncertainty of the time determination. We conclude that although there is potential for GATs to outperform the BDT, a more complex model should be applied. Finally, we provide some suggestions for improvement and hope to inspire further study and advancements in this direction which shows promising potential.
Från och med 2027 kommer \textit{high-luminosity Large Hadron Collider} (HL-LHC) att tas i drift och möjliggöra mätningar med högre precision och utforskningar av nya fysikprocesser mellan elementarpartiklar. Ett centralt problem som uppstår i ATLAS-detektorn vid rekonstruktionen av partikelkollisioner är att separera sällsynta och intressanta interaktioner, så kallade \textit{hard-scatters} (HS) från ointressanta \textit{pileup}-interaktioner (PU) i den kompakta rumsliga dimensionen. Svårighetsgraden för detta problem ökar vid högre luminositeter. Med hjälp av den kommande \textit{High-Granularity Timing-detektorns} (HGTD) mätningar kommer även tidsinformation relaterat till interaktionerna att erhållas. I detta projekt används denna information för att beräkna tiden för enskillda interaktioner vilket därmed kan användas för att separera HS-interaktioner från PU-interaktioner. Den nuvarande metoden använder en trädregressionsmetod, s.k. boosted decision tree (BDT) tillsammans med tidsinformationen från HGTD för att bestämma en tid. Vi föreslår ett nytt tillvägagångssätt baserat på ett s.k. uppvaktande grafnätverk (GAT), där varje protonkollision representeras som en graf över partikelspåren och där GAT-egenskaperna tillämpas på spårnivå. Våra resultat visar att vi kan replikera de BDT-baserade resultaten och till och med förbättra resultaten på bekostnad av att öka osäkerheten i tidsbestämningarna. Vi drar slutsatsen att även om det finns potential för GAT-modeller att överträffa BDT-modeller, bör mer komplexa versioner av de förra tillämpas. Vi ger slutligen några förbättringsförslag som vi hoppas ska kunna inspirera till ytterligare studier och framsteg inom detta område, vilket visar lovande potential.
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Breckel, Thomas P. K. [Verfasser], Christiane [Akademischer Betreuer] Thiel, and Stefan [Akademischer Betreuer] Debener. "Insights into brain networks from functional MRI and graph analysis during and following attentional demand / Thomas P. K. Breckel. Betreuer: Christiane Thiel ; Stefan Debener." Oldenburg : BIS der Universität Oldenburg, 2013. http://d-nb.info/1050299434/34.

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Breckel, Thomas [Verfasser], Christiane Akademischer Betreuer] Thiel, and Stefan [Akademischer Betreuer] [Debener. "Insights into brain networks from functional MRI and graph analysis during and following attentional demand / Thomas P. K. Breckel. Betreuer: Christiane Thiel ; Stefan Debener." Oldenburg : BIS der Universität Oldenburg, 2013. http://nbn-resolving.de/urn:nbn:de:gbv:715-oops-15262.

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Amor, Yasmine. "Ιntelligent apprοach fοr trafic cοngestiοn predictiοn." Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMR129.

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La congestion routière constitue un défi majeur pour les zones urbaines, car le volume de véhicules continue de croître plus rapidement que la capacité globale du réseau routier. Cette croissance a des répercussions sur l'activité économique, la durabilité environnementale et la qualité de vie. Bien que des stratégies visant à atténuer la congestion routière ont connu des améliorations au cours des dernières décennies, de nombreux pays ont encore du mal à la gérer efficacement.Divers modèles ont été développés pour aborder ce problème. Cependant, les approches existantes peinent souvent à fournir des prédictions en temps réel et localisées qui peuvent s'adapter à des conditions de trafic complexes et dynamiques. La plupart de ces approches s'appuient sur des horizons de prédiction fixes et manquent de l'infrastructure intelligente nécessaire à la flexibilité. Cette thèse comble ces lacunes en proposant une approche intelligente, décentralisée et basée sur l'infrastructure pour l'estimation et la prédiction de la congestion routière.Nous commençons par étudier l'Estimation du Trafic. Nous examinons les mesures de congestion possibles et les sources de données requises pour différents contextes pouvant être étudiés. Nous établissons une relation tridimensionnelle entre ces axes. Un système de recommandation basé sur des règles est développé pour aider les chercheurs et les opérateurs du trafic à choisir les mesures de congestion les plus appropriées en fonction du contexte étudié.Nous passons ensuite à la Prédiction du Trafic, où nous introduisons notre approche DECOTRIVMS. Cette dernière utilise des panneaux intelligents à messages variables pour collecter des données detrafic en temps réel et fournir des prédictions à court terme avec des horizons de prédiction variables.Nous avons utilisé des Réseaux de Graphes avec Attention en raison de leur capacité à capturer des relations complexes et à gérer des données structurées en graphes. Ils sont bien adaptés pour modéliser les interactions entre différents segments routiers étudiés.Nous avons aussi employé des méthodes d'apprentissage en ligne, spécifiquement la Descente de Gradient Stochastique et la Descente de Gradient Adaptative. Bien que ces méthodes ont été utilisées avec succès dans divers autres domaines, leur application à la prédiction de la congestion routière reste sous-explorée. Dans notre thèse, nous visons à combler cette lacune en explorant leur efficacité dans le contexte de la prédiction de la congestion routière en temps réel.Enfin, nous validons l'efficacité de notre approche à travers deux études de cas réalisées à Mascate, Oman, et à Rouen, France. Une analyse comparative est effectuée, évaluant divers modèles de prédiction, y compris les Réseaux de Graphes avec Attention, les Réseaux de Graphes Convolutionnels et des méthodes d'apprentissage en ligne. Les résultats obtenus soulignent le potentiel de DECOTRIVMS, démontrant son efficacité pour une prédiction précise et efficace de la congestion routière dans divers contextes urbains
Traffic congestion presents a critical challenge to urban areas, as the volume of vehicles continues to grow faster than the system’s overall capacity. This growth impacts economic activity, environmental sustainability, and overall quality of life. Although strategies for mitigating traffic congestion have seen improvements over the past few decades, many cities still struggle to manage it effectively. While various models have been developed to tackle this issue, existing approaches often fall short in providing real-time, localized predictions that can adapt to complex and dynamic traffic conditions. Most rely on fixed prediction horizons and lack the intelligent infrastructure needed for flexibility. This thesis addresses these gaps by proposing an intelligent, decentralized, infrastructure-based approach for traffic congestion estimation and prediction.We start by studying Traffic Estimation. We examine the possible congestion measures and data sources required for different contexts that may be studied. We establish a three-dimensional relationship between these axes. A rule-based system is developed to assist researchers and traffic operators in recommending the most appropriate congestion measures based on the specific context under study. We then proceed to Traffic Prediction, introducing our DECentralized COngestion esTimation and pRediction model using Intelligent Variable Message Signs (DECOTRIVMS). This infrastructure-based model employs intelligent Variable Message Signs (VMSs) to collect real-time traffic data and provide short-term congestion predictions with variable prediction horizons.We use Graph Attention Networks (GATs) due to their ability to capture complex relationships and handle graph-structured data. They are well-suited for modeling interactions between different road segments. In addition to GATs, we employ online learning methods, specifically, Stochastic Gradient Descent (SGD) and ADAptive GRAdient Descent (ADAGRAD). While these methods have been successfully used in various other domains, their application in traffic congestion prediction remains under-explored. In our thesis, we aim to bridge that gap by exploring their effectiveness within the context of real-time traffic congestion forecasting.Finally, we validate our model’s effectiveness through two case studies conducted in Muscat, Oman, and Rouen, France. A comprehensive comparative analysis is performed, evaluating various prediction techniques, including GATs, Graph Convolutional Networks (GCNs), SGD and ADAGRAD. The achieved results underscore the potential of DECOTRIVMS, demonstrating its potential for accurate and effective traffic congestion prediction across diverse urban contexts
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Blini, Elvio A. "Biases in Visuo-Spatial Attention: from Assessment to Experimental Induction." Doctoral thesis, Università degli studi di Padova, 2016. http://hdl.handle.net/11577/3424480.

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In this work I present several studies, which might appear rather heterogeneous for both experimental questions and methodological approaches, and yet are linked by a common leitmotiv: spatial attention. I will address issues related to the assessment of attentional asymmetries, in the healthy individual as in patients with neurological disorders, their role in various aspects of human cognition, and their neural underpinning, driven by the deep belief that spatial attention plays an important role in various mental processes that are not necessarily confined to perception. What follows is organized into two distinct sections. In the first I will focus on the evaluation of visuospatial asymmetries, starting from the description of a new paradigm particularly suitable for this purpose. In the first chapter I will describe the effects of multitasking in a spatial monitoring test; the main result shows a striking decreasing in detection performance as a function of the introduced memory load. In the second chapter I will apply the same paradigm to a clinical population characterized by a brain lesion affecting the left hemisphere. Despite a standard neuropsychological battery failed to highlight any lateralized attentional deficit, I will show that exploiting concurrent demands might lead to enhanced sensitivity of diagnostic tests and consequently positive effects on patients’ diagnostic and therapeutic management. Finally, in the third chapter I will suggest, in light of preliminary data, that attentional asymmetries also occur along the sagittal axis; I will argue, in particular, that more attentional resources appear to be allocated around peripersonal space, the resulting benefits extending to various tasks (i.e., discrimination tasks). Then, in the second section, I will follow a complementary approach: I will seek to induce attentional shifts in order to evaluate their role in different cognitive tasks. In the fourth and fifth chapters this will be pursued exploiting sensory stimulations: visual optokinetic stimulation and galvanic vestibular stimulation, respectively. In the fourth chapter I will show that spatial attention is highly involved in numerical cognition, this relationship being bidirectional. Specifically, I will show that optokinetic stimulation modulates the occurrence of procedural errors during mental arithmetics, and that calculation itself affects oculomotor behaviour in turn. In the fifth chapter I will examine the effects of galvanic vestibular stimulation, a particularly promising technique for the rehabilitation of lateralized attention disorders, on spatial representations. I will discuss critically a recent account for unilateral spatial neglect, suggesting that vestibular stimulations or disorders might indeed affect the metric representation of space, but not necessarily resulting in spatial unawareness. Finally, in the sixth chapter I will describe an attentional capture phenomenon by intrinsically rewarding distracters. I will seek, in particular, to predict the degree of attentional capture from resting-state functional magnetic resonance imaging data and the related brain connectivity pattern; I will report preliminary data focused on the importance of the cingulate-opercular network, and discuss the results through a parallel with clinical populations characterized by behavioural addictions.
In questo lavoro presenterò una serie di ricerche che possono sembrare piuttosto eterogenee per quesiti sperimentali e approcci metodologici, ma sono tuttavia legate da un filo conduttore comune: i costrutti di ragionamento e attenzione spaziale. Affronterò in particolare aspetti legati alla valutazione delle asimmetrie attenzionali, nell'individuo sano come nel paziente con disturbi neurologici, il loro ruolo in vari aspetti della cognizione umana, e i loro substrati neurali, guidato dalla convinzione che l’attenzione spaziale giochi un ruolo importante in svariati processi mentali non necessariamente limitati alla percezione. Quanto segue è stato dunque organizzato in due sezioni distinte. Nella prima mi soffermerò sulla valutazione delle asimmetrie visuospaziali, iniziando dalla descrizione di un nuovo paradigma particolarmente adatto a questo scopo. Nel primo capitolo descriverò gli effetti del doppio compito e del carico attenzionale su un test di monitoraggio spaziale; il risultato principale mostra un netto peggioramento nella prestazione al compito di detezione spaziale in funzione del carico di memoria introdotto. Nel secondo capitolo applicherò lo stesso paradigma ad una popolazione clinica contraddistinta da lesione cerebrale dell’emisfero sinistro. Nonostante una valutazione neuropsicologica standard non evidenziasse alcun deficit lateralizzato dell’attenzione, mostrerò che sfruttare un compito accessorio può portare ad una spiccata maggiore sensibilità dei test diagnostici, con evidenti ricadute benefiche sull'iter clinico e terapeutico dei pazienti. Infine, nel terzo capitolo suggerirò, tramite dati preliminari, che asimmetrie attenzionali possono essere individuate, nell'individuo sano, anche lungo l’asse sagittale; argomenterò, in particolare, che attorno allo spazio peripersonale sembrano essere generalmente concentrate più risorse attentive, e che i benefici conseguenti si estendono a compiti di varia natura (ad esempio compiti di discriminazione). Passerò dunque alla seconda sezione, in cui, seguendo una logica inversa, indurrò degli spostamenti nel focus attentivo in modo da valutarne il ruolo in compiti di varia natura. Nei capitoli quarto e quinto sfrutterò delle stimolazioni sensoriali: la stimolazione visiva optocinetica e la stimolazione galvanico vestibolare, rispettivamente. Nel quarto capitolo mostrerò che l’attenzione spaziale è coinvolta nella cognizione numerica, con cui intrattiene rapporti bidirezionali. Nello specifico mostrerò da un lato che la stimolazione optocinetica può modulare l’occorrenza di errori procedurali nel calcolo mentale, dall'altro che il calcolo stesso ha degli effetti sull'attenzione spaziale e in particolare sul comportamento oculomotorio. Nel quinto capitolo esaminerò gli effetti della stimolazione galvanica vestibolare, una tecnica particolarmente promettente per la riabilitazione dei disturbi attentivi lateralizzati, sulle rappresentazioni mentali dello spazio. Discuterò in modo critico un recente modello della negligenza spaziale unilaterale, suggerendo che stimolazioni e disturbi vestibolari possano sì avere ripercussioni sulle rappresentazioni metriche dello spazio, ma senza comportare necessariamente inattenzione per lo spazio stesso. Infine, nel sesto capitolo descriverò gli effetti di cattura dell’attenzione visuospaziale che stimoli distrattori intrinsecamente motivanti possono esercitare nell'adulto sano. Cercherò, in particolare, di predire l’entità di questa cattura attenzionale partendo da immagini di risonanza magnetica funzionale a riposo: riporterò dati preliminari focalizzati sull'importanza del circuito cingolo-opercolare, effettuando un parallelismo con popolazioni cliniche caratterizzate da comportamenti di dipendenza.
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Books on the topic "Graph Attention Networks"

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Dorogovtsev, Sergey N., and José F. F. Mendes. The Nature of Complex Networks. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780199695119.001.0001.

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Abstract The researchers studying complex networks will acquire from this advanced modern book a number of new issues and ideas, not yet touched upon in other reference volumes. The book considers a wide range of networks and processes taking place on them, paying particular attention to the recently developed directions, methods, and techniques. It proposes a statistical mechanics view of random networks based on the concept of statistical ensembles, but approaches and methods of modern graph theory, concerning random graphs, overlap strongly with statistical physics. Hence mathematicians have a good chance to discover interesting things in this book, even though it does not contain mathematical proofs and trades off rigour for comprehension, brevity, and relevance. The book combines features of an advanced textbook, a reference book and a detailed review of the current state of the art. This book will be useful for undergraduate, master, and PhD students and young researchers from physics, multidisciplinary studies, computer science, and applied mathematics wishing to gain a serious insight into the principles of complex networks. The book can be used as a text in university courses on complex networks. It proposes to determined students not only a brief trip to the land of complex networks but an option to stay there forever.
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Bianconi, Ginestra. Synchronization, Non-linear Dynamics and Control. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.003.0015.

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This chapter is entirely devoted to characterizing non-linear dynamics on multilayer networks. Special attention is given to recent results on the stability of synchronization that extend the Master Stability Function approach to the multilayer networks scenario. Discontinous synchronization transitions on multiplex networks recently reported in the literature are also discussed, and their application discussed in the context of brain networks. This chapter also presents an overview of the major results regarding pattern formation in multilayer networks, and the proposed characterization of multivariate time series using multiplex visibility graphs. Finally, the chapter discusses several approaches for multiplex network control where the dynamical state of a multiplex network needs to be controlled by eternal signals placed on replica nodes satisfying some structural constraints.
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Book chapters on the topic "Graph Attention Networks"

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Liu, Zhiyuan, and Jie Zhou. "Graph Attention Networks." In Introduction to Graph Neural Networks, 39–41. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-031-01587-8_7.

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Huang, Junjie, Huawei Shen, Liang Hou, and Xueqi Cheng. "Signed Graph Attention Networks." In Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions, 566–77. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30493-5_53.

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Abdel-Basset, Mohamed, Nour Moustafa, Hossam Hawash, and Zahir Tari. "Graph Attention Networks: A Journey from Start to End." In Responsible Graph Neural Networks, 131–58. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003329701-6.

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Frey, Christian M. M., Yunpu Ma, and Matthias Schubert. "SEA: Graph Shell Attention in Graph Neural Networks." In Machine Learning and Knowledge Discovery in Databases, 326–43. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26390-3_20.

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Ma, Liheng, Reihaneh Rabbany, and Adriana Romero-Soriano. "Graph Attention Networks with Positional Embeddings." In Advances in Knowledge Discovery and Data Mining, 514–27. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75762-5_41.

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Tao, Ye, Ying Li, and Zhonghai Wu. "Revisiting Attention-Based Graph Neural Networks for Graph Classification." In Lecture Notes in Computer Science, 442–58. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14714-2_31.

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Wang, Yubin, Zhenyu Zhang, Tingwen Liu, and Li Guo. "SLGAT: Soft Labels Guided Graph Attention Networks." In Advances in Knowledge Discovery and Data Mining, 512–23. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47426-3_40.

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Jyoti, Divya, Akhil Sharma, Shaswat Gupta, Prashant Manhar, Jyoti Srivastava, and Dharamendra Prasad Mahato. "Abstractive Summarization Using Gated Graph Attention Networks." In Lecture Notes on Data Engineering and Communications Technologies, 95–106. Cham: Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-87766-7_9.

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Zhao, Ming, Weijia Jia, and Yusheng Huang. "Attention-Based Aggregation Graph Networks for Knowledge Graph Information Transfer." In Advances in Knowledge Discovery and Data Mining, 542–54. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47436-2_41.

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Wang, Jingqi, Cui Zhu, and Wenjun Zhu. "Dynamic Embedding Graph Attention Networks for Temporal Knowledge Graph Completion." In Knowledge Science, Engineering and Management, 722–34. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10983-6_55.

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Conference papers on the topic "Graph Attention Networks"

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Kato, Jun, Airi Mita, Keita Gobara, and Akihiro Inokuchi. "Deep Graph Attention Networks." In 2024 Twelfth International Symposium on Computing and Networking Workshops (CANDARW), 170–74. IEEE, 2024. https://doi.org/10.1109/candarw64572.2024.00034.

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Ishtiaq, Mariam, Jong-Un Won, and Sangchan Park. "GATreg - Graph Attention Networks with Regularization." In 2025 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 1022–26. IEEE, 2025. https://doi.org/10.1109/icaiic64266.2025.10920663.

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Buschmann, Fernando Vera, Zhihui Du, and David Bader. "Enhanced Knowledge Graph Attention Networks for Efficient Graph Learning." In 2024 IEEE High Performance Extreme Computing Conference (HPEC), 1–7. IEEE, 2024. https://doi.org/10.1109/hpec62836.2024.10938526.

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Gao, Yiwei, and Qing Gao. "Graph Structure Adversarial Attack Design Based on Graph Attention Networks." In 2024 43rd Chinese Control Conference (CCC), 9028–33. IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10661862.

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Li, Min, Xuejun Li, and Jing Liao. "Graph Attention Networks Fusing Semantic Information for Knowledge Graph Completion." In 2024 17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 1–6. IEEE, 2024. https://doi.org/10.1109/cisp-bmei64163.2024.10906235.

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Vu, Tuan Hoang, Anh Nguyen, and Minh Tuan Nguyen. "Graph Attention Networks Based Cardiac Arrest Diagnosis." In 2024 International Conference on Advanced Technologies for Communications (ATC), 762–67. IEEE, 2024. https://doi.org/10.1109/atc63255.2024.10908172.

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Rustamov, Zahiriddin, Ayham Zaitouny, Rafat Damseh, and Nazar Zaki. "GAIS: A Novel Approach to Instance Selection with Graph Attention Networks." In 2024 IEEE International Conference on Knowledge Graph (ICKG), 309–16. IEEE, 2024. https://doi.org/10.1109/ickg63256.2024.00046.

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Wang, Shuowei, Sifan Ding, and William Zhu. "Deep Graph Matching with Improved Graph-Context Networks based by Attention." In 2024 International Conference on Artificial Intelligence and Power Systems (AIPS), 156–60. IEEE, 2024. http://dx.doi.org/10.1109/aips64124.2024.00041.

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Brynte, Lucas, José Pedro Iglesias, Carl Olsson, and Fredrik Kahl. "Learning Structure-From-Motion with Graph Attention Networks." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 4808–17. IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.00460.

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Zhang, Shuya, Tao Yang, and Kongfa Hu. "Multi-Label Syndrome Classification with Graph Attention Networks." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 4802–6. IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822526.

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Reports on the topic "Graph Attention Networks"

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Fait, Aaron, Grant Cramer, and Avichai Perl. Towards improved grape nutrition and defense: The regulation of stilbene metabolism under drought. United States Department of Agriculture, May 2014. http://dx.doi.org/10.32747/2014.7594398.bard.

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The goals of the present research proposal were to elucidate the physiological and molecular basis of the regulation of stilbene metabolism in grape, against the background of (i) grape metabolic network behavior in response to drought and of (ii) varietal diversity. The specific objectives included the study of the physiology of the response of different grape cultivars to continuous WD; the characterization of the differences and commonalities of gene network topology associated with WD in berry skin across varieties; the study of the metabolic response of developing berries to continuous WD with specific attention to the stilbene compounds; the integration analysis of the omics data generated; the study of isolated drought-associated stress factors on the regulation of stilbene biosynthesis in plantaand in vitro. Background to the topic Grape quality has a complex relationship with water input. Regulated water deficit (WD) is known to improve wine grapes by reducing the vine growth (without affecting fruit yield) and boosting sugar content (Keller et al. 2008). On the other hand, irregular rainfall during the summer can lead to drought-associated damage of fruit developmental process and alter fruit metabolism (Downey et al., 2006; Tarara et al., 2008; Chalmers et al., 792). In areas undergoing desertification, WD is associated with high temperatures. This WD/high temperature synergism can limit the areas of grape cultivation and can damage yields and fruit quality. Grapes and wine are the major source of stilbenes in human nutrition, and multiple stilbene-derived compounds, including isomers, polymers and glycosylated forms, have also been characterized in grapes (Jeandet et al., 2002; Halls and Yu, 2008). Heterologous expression of stilbenesynthase (STS) in a variety of plants has led to an enhanced resistance to pathogens, but in others the association has not been proven (Kobayashi et al., 2000; Soleas et al., 1995). Tomato transgenic plants harboring a grape STS had increased levels of resveratrol, ascorbate, and glutathione at the expense of the anthocyanin pathways (Giovinazzo et al. 2005), further emphasizing the intermingled relation among secondary metabolic pathways. Stilbenes are are induced in green and fleshy parts of the berries by biotic and abiotic elicitors (Chong et al., 2009). As is the case for other classes of secondary metabolites, the biosynthesis of stilbenes is not very well understood, but it is known to be under tight spatial and temporal control, which limits the availability of these compounds from plant sources. Only very few studies have attempted to analyze the effects of different environmental components on stilbene accumulation (Jeandet et al., 1995; Martinez-Ortega et al., 2000). Targeted analyses have generally shown higher levels of resveratrol in the grape skin (induced), in seeded varieties, in varieties of wine grapes, and in dark-skinned varieties (Gatto et al., 2008; summarized by Bavaresco et al., 2009). Yet, the effect of the grape variety and the rootstock on stilbene metabolism has not yet been thoroughly investigated (Bavaresco et al., 2009). The study identified a link between vine hydraulic behavior and physiology of stress with the leaf metabolism, which the PIs believe can eventually lead to the modifications identified in the developing berries that interested the polyphenol metabolism and its regulation during development and under stress. Implications are discussed below.
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