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Artículos de revistas sobre el tema "Graph-based input representation"

Autor: Grafiati

Publicado: 16 de noviembre de 2024

Última modificación: 19 de julio de 2025

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1

Lu, Fangbo, Zhihao Zhang, and Changsheng Shui. "Online trajectory anomaly detection model based on graph neural networks and variational autoencoder." Journal of Physics: Conference Series 2816, no. 1 (2024): 012006. http://dx.doi.org/10.1088/1742-6596/2816/1/012006.

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Abstract To efficiently determine whether an entire trajectory exhibits abnormal behavior, we introduce an online trajectory anomaly detection model known as GeoGNFTOD, which employs graph neural networks for road segment representation, creating a directed graph by mapping trajectories onto the road network. The graph representation is constructed based on the road segments in this directed graph. By utilizing Transformer sequence encoding, the trajectory representation is derived and hierarchical geographic encoding captures the GPS mapping of the original trajectories. Merging these two rep

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2

Aljanabi, Ehssan, and İlker Türker. "Connectogram-COH: A Coherence-Based Time-Graph Representation for EEG-Based Alzheimer’s Disease Detection." Diagnostics 15, no. 11 (2025): 1441. https://doi.org/10.3390/diagnostics15111441.

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Background: Alzheimer’s disease (AD) is a neurological disorder that affects the brain in the elderly, resulting in memory loss, mental deterioration, and loss of the ability to think and act, while being a cause of death, with its rates increasing dramatically. A popular method to detect AD is electroencephalography (EEG) signal analysis thanks to its ability to reflect neural activity, which helps to identify abnormalities associated with the disorder. Originating from its multivariate nature, EEG signals are generally handled as multidimensional time series, and the related methodology is e

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3

Yu, Xingtong, Zemin Liu, Yuan Fang, and Xinming Zhang. "Learning to Count Isomorphisms with Graph Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 4845–53. http://dx.doi.org/10.1609/aaai.v37i4.25610.

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Subgraph isomorphism counting is an important problem on graphs, as many graph-based tasks exploit recurring subgraph patterns. Classical methods usually boil down to a backtracking framework that needs to navigate a huge search space with prohibitive computational cost. Some recent studies resort to graph neural networks (GNNs) to learn a low-dimensional representation for both the query and input graphs, in order to predict the number of subgraph isomorphisms on the input graph. However, typical GNNs employ a node-centric message passing scheme that receives and aggregates messages on nodes,

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4

Bauer, Daniel. "Understanding Descriptions of Visual Scenes Using Graph Grammars." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (2013): 1656–57. http://dx.doi.org/10.1609/aaai.v27i1.8498.

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Automatic generation of 3D scenes from descriptions has applications in communication, education, and entertainment, but requires deep understanding of the input text. I propose thesis work on language understanding using graph-based meaning representations that can be decomposed into primitive spatial relations. The techniques used for analyzing text and transforming it into a scene representation are based on context-free graph grammars. The thesis develops methods for semantic parsing with graphs, acquisition of graph grammars, and satisfaction of spatial and world-knowledge constraints dur

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5

Wu, Xinyue, and Huilin Chen. "Augmented Feature Diffusion on Sparsely Sampled Subgraph." Electronics 13, no. 16 (2024): 3249. http://dx.doi.org/10.3390/electronics13163249.

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Link prediction is a fundamental problem in graphs. Currently, SubGraph Representation Learning (SGRL) methods provide state-of-the-art solutions for link prediction by transforming the task into a graph classification problem. However, existing SGRL solutions suffer from high computational costs and lack scalability. In this paper, we propose a novel SGRL framework called Augmented Feature Diffusion on Sparsely Sampled Subgraph (AFD3S). The AFD3S first uses a conditional variational autoencoder to augment the local features of the input graph, effectively improving the expressive ability of d

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6

Cooray, Thilini, and Ngai-Man Cheung. "Graph-Wise Common Latent Factor Extraction for Unsupervised Graph Representation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (2022): 6420–28. http://dx.doi.org/10.1609/aaai.v36i6.20593.

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Unsupervised graph-level representation learning plays a crucial role in a variety of tasks such as molecular property prediction and community analysis, especially when data annotation is expensive. Currently, most of the best-performing graph embedding methods are based on Infomax principle. The performance of these methods highly depends on the selection of negative samples and hurt the performance, if the samples were not carefully selected. Inter-graph similarity-based methods also suffer if the selected set of graphs for similarity matching is low in quality. To address this, we focus on

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7

Gildea, Daniel, Giorgio Satta, and Xiaochang Peng. "Ordered Tree Decomposition for HRG Rule Extraction." Computational Linguistics 45, no. 2 (2019): 339–79. http://dx.doi.org/10.1162/coli_a_00350.

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We present algorithms for extracting Hyperedge Replacement Grammar (HRG) rules from a graph along with a vertex order. Our algorithms are based on finding a tree decomposition of smallest width, relative to the vertex order, and then extracting one rule for each node in this structure. The assumption of a fixed order for the vertices of the input graph makes it possible to solve the problem in polynomial time, in contrast to the fact that the problem of finding optimal tree decompositions for a graph is NP-hard. We also present polynomial-time algorithms for parsing based on our HRGs, where th

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8

Miao, Fengyu, Xiuzhuang Zhou, Shungen Xiao, and Shiliang Zhang. "A Graph Similarity Algorithm Based on Graph Partitioning and Attention Mechanism." Electronics 13, no. 19 (2024): 3794. http://dx.doi.org/10.3390/electronics13193794.

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In recent years, graph similarity algorithms have been extensively developed based on neural networks. However, with an increase in the node count in graphs, these models either suffer from a reduced representation ability or face a significant increase in the computational cost. To address this issue, a graph similarity algorithm based on graph partitioning and attention mechanisms was proposed in this study. Our method first divided each input graph into the subgraphs to directly extract the local structural features. The residual graph convolution and multihead self-attention mechanisms wer

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9

Zhang, Dong, Suzhong Wei, Shoushan Li, Hanqian Wu, Qiaoming Zhu, and Guodong Zhou. "Multi-modal Graph Fusion for Named Entity Recognition with Targeted Visual Guidance." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 16 (2021): 14347–55. http://dx.doi.org/10.1609/aaai.v35i16.17687.

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Multi-modal named entity recognition (MNER) aims to discover named entities in free text and classify them into pre-defined types with images. However, dominant MNER models do not fully exploit fine-grained semantic correspondences between semantic units of different modalities, which have the potential to refine multi-modal representation learning. To deal with this issue, we propose a unified multi-modal graph fusion (UMGF) approach for MNER. Specifically, we first represent the input sentence and image using a unified multi-modal graph, which captures various semantic relationships between

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10

Coşkun, Kemal Çağlar, Muhammad Hassan, and Rolf Drechsler. "Equivalence Checking of System-Level and SPICE-Level Models of Linear Circuits." Chips 1, no. 1 (2022): 54–71. http://dx.doi.org/10.3390/chips1010006.

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Due to the increasing complexity of analog circuits and their integration into System-on-Chips (SoC), the analog design and verification industry would greatly benefit from an expansion of system-level methodologies using SystemC AMS. These can provide a speed increase of over 100,000× in comparison to SPICE-level simulations and allow interoperability with digital tools at the system-level. However, a key barrier to the expansion of system-level tools for analog circuits is the lack of confidence in system-level models implemented in SystemC AMS. Functional equivalence of single Laplace Trans

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11

Ren, Min, Yunlong Wang, Zhenan Sun, and Tieniu Tan. "Dynamic Graph Representation for Occlusion Handling in Biometrics." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 11940–47. http://dx.doi.org/10.1609/aaai.v34i07.6869.

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The generalization ability of Convolutional neural networks (CNNs) for biometrics drops greatly due to the adverse effects of various occlusions. To this end, we propose a novel unified framework integrated the merits of both CNNs and graphical models to learn dynamic graph representations for occlusion problems in biometrics, called Dynamic Graph Representation (DGR). Convolutional features onto certain regions are re-crafted by a graph generator to establish the connections among the spatial parts of biometrics and build Feature Graphs based on these node representations. Each node of Featur

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12

Yin, Yongjing, Shaopeng Lai, Linfeng Song, et al. "An External Knowledge Enhanced Graph-based Neural Network for Sentence Ordering." Journal of Artificial Intelligence Research 70 (January 28, 2021): 545–66. http://dx.doi.org/10.1613/jair.1.12078.

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As an important text coherence modeling task, sentence ordering aims to coherently organize a given set of unordered sentences. To achieve this goal, the most important step is to effectively capture and exploit global dependencies among these sentences. In this paper, we propose a novel and flexible external knowledge enhanced graph-based neural network for sentence ordering. Specifically, we first represent the input sentences as a graph, where various kinds of relations (i.e., entity-entity, sentence-sentence and entity-sentence) are exploited to make the graph representation more expressiv

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Malhi, Umar Subhan, Junfeng Zhou, Abdur Rasool, and Shahbaz Siddeeq. "Efficient Visual-Aware Fashion Recommendation Using Compressed Node Features and Graph-Based Learning." Machine Learning and Knowledge Extraction 6, no. 3 (2024): 2111–29. http://dx.doi.org/10.3390/make6030104.

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In fashion e-commerce, predicting item compatibility using visual features remains a significant challenge. Current recommendation systems often struggle to incorporate high-dimensional visual data into graph-based learning models effectively. This limitation presents a substantial opportunity to enhance the precision and effectiveness of fashion recommendations. In this paper, we present the Visual-aware Graph Convolutional Network (VAGCN). This novel framework helps improve how visual features can be incorporated into graph-based learning systems for fashion item compatibility predictions. T

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14

Lim, Bo-Young, Jeong-Ha Park, Kisung Lee, and Hyuk-Yoon Kwon. "Multi-Level Graph Representation Learning Through Predictive Community-based Partitioning." Proceedings of the ACM on Management of Data 3, no. 1 (2025): 1–27. https://doi.org/10.1145/3711115.

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Graph representation learning (GRL) aims to map a graph into a low-dimensional vector space while preserving graph topology and node properties. This study proposes a novel GRL model, Multi-Level GRL (simply, ML-GRL), that recursively partitions input graphs by selecting the most appropriate community detection algorithm at each graph or partitioned subgraph. To preserve the relationship between subgraphs, ML-GRL incorporates global graphs that effectively maintain the overall topology. ML-GRL employs a prediction model, which is pre-trained using graph-based features and covers a wide range o

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Christensen, Andrew J., Ananya Sen Gupta, and Ivars Kirsteins. "Graph representation learning on braid manifolds." Journal of the Acoustical Society of America 152, no. 4 (2022): A39. http://dx.doi.org/10.1121/10.0015466.

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The accuracy of autonomous sonar target recognition systems is usually hindered by morphing target features, unknown target geometry, and uncertainty caused by waveguide distortions to signal. Common “black-box” neural networks are not effective in addressing these challenges since they do not produce physically interpretable features. This work seeks to use recent advancements in machine learning to extract braid features that can be interpreted by a domain expert. We utilize Graph Neural Networks (GNNs) to discover braid manifolds in sonar ping spectra data. This approach represents the sona

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Yong, Jiu, Jianguo Wei, Xiaomei Lei, Jianwu Dang, Wenhuan Lu, and Meijuan Cheng. "A Learning Resource Recommendation Method Based on Graph Contrastive Learning." Electronics 14, no. 1 (2025): 142. https://doi.org/10.3390/electronics14010142.

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The existing learning resource recommendation systems suffer from data sparsity and missing data labels, leading to the insufficient mining of the correlation between users and courses. To address these issues, we propose a learning resource recommendation method based on graph contrastive learning, which uses graph contrastive learning to construct an auxiliary recommendation task combined with a main recommendation task, achieving the joint recommendation of learning resources. Firstly, the interaction bipartite graph between the user and the course is input into a lightweight graph convolut

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17

Jiang, Linhua, Wenbiao Ye, Wei Long, Wenbo Guo, and Lingxi Hu. "Drug-Target Affinity Prediction Based on Graph Representation and Attention Fusion Mechanism." Scientific Journal of Technology 7, no. 4 (2025): 117–31. https://doi.org/10.54691/pz2qge05.

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Predicting drug-target affinity is crucial in the field of drug discovery. To further improve the accuracy of predictions, this paper proposes a drug-target affinity prediction model, GRAM-DTA, based on graph representation and attention fusion mechanisms. The model represents the input features of drugs and targets as graph data and utilizes deep graph isomorphism networks and graph neural network modules combining graph convolutional networks and graph attention networks to process the feature information of drugs and targets, respectively. In the feature fusion stage, an attention mechanism

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18

Xu, Jiarong, Yang Yang, Junru Chen, et al. "Unsupervised Adversarially Robust Representation Learning on Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (2022): 4290–98. http://dx.doi.org/10.1609/aaai.v36i4.20349.

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Unsupervised/self-supervised pre-training methods for graph representation learning have recently attracted increasing research interests, and they are shown to be able to generalize to various downstream applications. Yet, the adversarial robustness of such pre-trained graph learning models remains largely unexplored. More importantly, most existing defense techniques designed for end-to-end graph representation learning methods require pre-specified label definitions, and thus cannot be directly applied to the pre-training methods. In this paper, we propose an unsupervised defense technique

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19

Lin, Mugang, Kunhui Wen, Xuanying Zhu, Huihuang Zhao, and Xianfang Sun. "Graph Autoencoder with Preserving Node Attribute Similarity." Entropy 25, no. 4 (2023): 567. http://dx.doi.org/10.3390/e25040567.

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The graph autoencoder (GAE) is a powerful graph representation learning tool in an unsupervised learning manner for graph data. However, most existing GAE-based methods typically focus on preserving the graph topological structure by reconstructing the adjacency matrix while ignoring the preservation of the attribute information of nodes. Thus, the node attributes cannot be fully learned and the ability of the GAE to learn higher-quality representations is weakened. To address the issue, this paper proposes a novel GAE model that preserves node attribute similarity. The structural graph and th

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Ramezani, Majid, Mohammad-Reza Feizi-Derakhshi, and Mohammad-Ali Balafar. "Knowledge Graph-Enabled Text-Based Automatic Personality Prediction." Computational Intelligence and Neuroscience 2022 (June 20, 2022): 1–18. http://dx.doi.org/10.1155/2022/3732351.

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How people think, feel, and behave primarily is a representation of their personality characteristics. By being conscious of the personality characteristics of individuals whom we are dealing with or deciding to deal with, one can competently ameliorate the relationship, regardless of its type. With the rise of Internet-based communication infrastructures (social networks, forums, etc.), a considerable amount of human communications takes place there. The most prominent tool in such communications is the language in written and spoken form that adroitly encodes all those essential personality

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Sarunya, Kanjanawattana, and Kimura Masaomi. "BRAIN Journal - Novel Ontologies-based Optical Character Recognition-error Correction Cooperating with Graph Component Extraction." BRAIN - Broad Research in Artificial Intelligence and Neuroscience 7, no. 4 (2016): 69–83. https://doi.org/10.5281/zenodo.1044990.

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ABSTRACT A graph is a data representation visually presenting significant information in the academic literature. Extracting graph information clearly contributes to readers, who are interested in graph information interpretation, because we can obtain significant information presenting in the graph. A typical tool used to transform image-based characters to computer editable characters is optical character recognition (OCR). Unfortunately, OCR cannot guarantee perfect results, because it is sensitive to noise and input quality. This becomes a serious problem because misrecognition provides mi

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Kuropiatnyk, O. S., and B. M. Yakovenko. "Identification of the Program Text and Algorithm Correspondence Based on the Control Graph Constructive-Synthesizing Model." Science and Transport Progress. Bulletin of Dnipropetrovsk National University of Railway Transport, no. 4(94) (August 17, 2021): 12–24. http://dx.doi.org/10.15802/stp2021/245666.

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Purpose.The main article purpose is to develop and implement the method for identifying the correspondence between the text and the program algorithm represented in the form of a flowchart. As part of the method work conversion of the input data in the graph representation is performed by means of constructive-synthesizing modelling. Methodology. To compare the program text and flowchart, we constructed a mathematical model for converting the program code into a graphical representation on the basis of control structures. To build the model, the apparatus of constructive-synthesizing modeling

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Song, Zhiwei, Brittany Baur, and Sushmita Roy. "Benchmarking graph representation learning algorithms for detecting modules in molecular networks." F1000Research 12 (August 7, 2023): 941. http://dx.doi.org/10.12688/f1000research.134526.1.

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Background: A common task in molecular network analysis is the detection of community structures or modules. Such modules are frequently associated with shared biological functions and are often disrupted in disease. Detection of community structure entails clustering nodes in the graph, and many algorithms apply a clustering algorithm on an input node embedding. Graph representation learning offers a powerful framework to learn node embeddings to perform various downstream tasks such as clustering. Deep embedding methods based on graph neural networks can have substantially better performance

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Sarfraz, Mubashar, Sheraz Alam, Sajjad A. Ghauri, et al. "Random Graph-Based M-QAM Classification for MIMO Systems." Wireless Communications and Mobile Computing 2022 (April 15, 2022): 1–10. http://dx.doi.org/10.1155/2022/9419764.

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Automatic modulation classification (AMC) has been identified to perform a key role to realize technologies such as cognitive radio, dynamic spectrum management, and interference identification that are arguably pivotal to practical SG communication networks. Random graphs (RGs) have been used to better understand graph behavior and to tackle combinatorial challenges in general. In this research article, a novel modulation classifier is presented to recognize M-Quadrature Amplitude Modulation (QAM) signals using random graph theory. The proposed method demonstrates improved recognition rates f

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Zhang, Dehai, Anquan Ren, Jiashu Liang, Qing Liu, Haoxing Wang, and Yu Ma. "Improving Medical X-ray Report Generation by Using Knowledge Graph." Applied Sciences 12, no. 21 (2022): 11111. http://dx.doi.org/10.3390/app122111111.

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In clinical diagnosis, radiological reports are essential to guide the patient’s treatment. However, writing radiology reports is a critical and time-consuming task for radiologists. Existing deep learning methods often ignore the interplay between medical findings, which may be a bottleneck limiting the quality of generated radiology reports. Our paper focuses on the automatic generation of medical reports from input chest X-ray images. In this work, we mine the associations between medical discoveries in the given texts and construct a knowledge graph based on the associations between medica

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Gao, Peng, and Hao Zhang. "Long-Term Loop Closure Detection through Visual-Spatial Information Preserving Multi-Order Graph Matching." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 06 (2020): 10369–76. http://dx.doi.org/10.1609/aaai.v34i06.6604.

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Loop closure detection is a fundamental problem for simultaneous localization and mapping (SLAM) in robotics. Most of the previous methods only consider one type of information, based on either visual appearances or spatial relationships of landmarks. In this paper, we introduce a novel visual-spatial information preserving multi-order graph matching approach for long-term loop closure detection. Our approach constructs a graph representation of a place from an input image to integrate visual-spatial information, including visual appearances of the landmarks and the background environment, as

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Nisslbeck, Tim N., and Wouter M. Kouw. "Factor Graph-Based Online Bayesian Identification and Component Evaluation for Multivariate Autoregressive Exogenous Input Models." Entropy 27, no. 7 (2025): 679. https://doi.org/10.3390/e27070679.

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We present a Forney-style factor graph representation for the class of multivariate autoregressive models with exogenous inputs, and we propose an online Bayesian parameter-identification procedure based on message passing within this graph. We derive message-update rules for (1) a custom factor node that represents the multivariate autoregressive likelihood function and (2) the matrix normal Wishart distribution over the parameters. The flow of messages reveals how parameter uncertainty propagates into predictive uncertainty over the system outputs and how individual factor nodes and edges co

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Hao, Yajie, Xing Chen, Ailu Fei, et al. "SG-ATT: A Sequence Graph Cross-Attention Representation Architecture for Molecular Property Prediction." Molecules 29, no. 2 (2024): 492. http://dx.doi.org/10.3390/molecules29020492.

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Existing formats based on the simplified molecular input line entry system (SMILES) encoding and molecular graph structure are designed to encode the complete semantic and structural information of molecules. However, the physicochemical properties of molecules are complex, and a single encoding of molecular features from SMILES sequences or molecular graph structures cannot adequately represent molecular information. Aiming to address this problem, this study proposes a sequence graph cross-attention (SG-ATT) representation architecture for a molecular property prediction model to efficiently

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Tian, Luogeng, Bailong Yang, Xinli Yin, Kai Kang, and Jing Wu. "Multipath Cross Graph Convolution for Knowledge Representation Learning." Computational Intelligence and Neuroscience 2021 (December 28, 2021): 1–13. http://dx.doi.org/10.1155/2021/2547905.

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In the past, most of the entity prediction methods based on embedding lacked the training of local core relationships, resulting in a deficiency in the end-to-end training. Aiming at this problem, we propose an end-to-end knowledge graph embedding representation method. It involves local graph convolution and global cross learning in this paper, which is called the TransC graph convolutional network (TransC-GCN). Firstly, multiple local semantic spaces are divided according to the largest neighbor. Secondly, a translation model is used to map the local entities and relationships into a cross v

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Bunke, H., and B. T. Messmer. "Recent Advances in Graph Matching." International Journal of Pattern Recognition and Artificial Intelligence 11, no. 01 (1997): 169–203. http://dx.doi.org/10.1142/s0218001497000081.

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A powerful and universal data structure with applications invarious subfields of science and engineering is graphs. In computer vision and image analysis, graphs are often used for the representation of structured objects. For example, if the problem is to recognize instances of known objects in an image, then often models, or prototypes, of the known objects are represented by means of graphs and stored in a database. The unknown objects in the input image are extracted by means of suitable preprocessing and segmentation algorithms, and represented by graphs that are analogous to the model gr

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Li, Linqing, and Zhifeng Wang. "Knowledge Graph-Enhanced Intelligent Tutoring System Based on Exercise Representativeness and Informativeness." International Journal of Intelligent Systems 2023 (October 16, 2023): 1–19. http://dx.doi.org/10.1155/2023/2578286.

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In the realm of online tutoring intelligent systems, e-learners are exposed to a substantial volume of learning content. The extraction and organization of exercises and skills hold significant importance in establishing clear learning objectives and providing appropriate exercise recommendations. Presently, knowledge graph-based recommendation algorithms have garnered considerable attention among researchers. However, these algorithms solely consider knowledge graphs with single relationships and do not effectively model exercise-rich features, such as exercise representativeness and informat

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Xu, Jiakun, Bowen Xu, Gui-Song Xia, Liang Dong, and Nan Xue. "Patched Line Segment Learning for Vector Road Mapping." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 6 (2024): 6288–96. http://dx.doi.org/10.1609/aaai.v38i6.28447.

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This paper presents a novel approach to computing vector road maps from satellite remotely sensed images, building upon a well-defined Patched Line Segment (PaLiS) representation for road graphs that holds geometric significance. Unlike prevailing methods that derive road vector representations from satellite images using binary masks or keypoints, our method employs line segments. These segments not only convey road locations but also capture their orientations, making them a robust choice for representation. More precisely, given an input image, we divide it into non-overlapping patches and

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Chuang, S. H. F., and M. R. Henderson. "Using Subgraph Isomorphisms to Recognize and Decompose Boundary Representation Features." Journal of Mechanical Design 116, no. 3 (1994): 793–800. http://dx.doi.org/10.1115/1.2919452.

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A method using subgraph isomorphisms is presented for both computer recognition of shape features and feature-based decomposition of a solid from a boundary representation (B-rep). Prior to the recognition process, the face-edge graph of an object is extracted from a B-rep and is labeled by shape elements as a shape graph, which is an abridged B-rep and is labeled by shape elements as a shape graph, which is an abridged B-rep input to the recognition system. A feature is defined by a user as a feature graph, which is conceptualized from a regional surface shape on a valid solid. Feature recogn

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Feng, Zijin, Miao Qiao, Chengzhi Piao, and Hong Cheng. "On Graph Representation for Attributed Hypergraph Clustering." Proceedings of the ACM on Management of Data 3, no. 1 (2025): 1–26. https://doi.org/10.1145/3709741.

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Attributed Hypergraph Clustering (AHC) aims at partitioning a hypergraph into clusters such that nodes in the same cluster are close to each other with both high connectedness and homogeneous attributes. Existing AHC methods are all based on matrix factorization which may incur a substantial computation cost; more importantly, they inherently require a prior knowledge of the number of clusters as an input which, if inaccurately estimated, shall lead to a significant deterioration in the clustering quality. In this paper, we propose <u>A</u>ttributed <u>H</u>ypergraph &l

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Yang, Liang, Chuan Wang, Junhua Gu, Xiaochun Cao, and Bingxin Niu. "Why Do Attributes Propagate in Graph Convolutional Neural Networks?" Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (2021): 4590–98. http://dx.doi.org/10.1609/aaai.v35i5.16588.

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Many efforts have been paid to enhance Graph Convolutional Network from the perspective of propagation under the philosophy that ``Propagation is the essence of the GCNNs". Unfortunately, its adverse effect is over-smoothing, which makes the performance dramatically drop. To prevent the over-smoothing, many variants are presented. However, the perspective of propagation can't provide an intuitive and unified interpretation to their effect on prevent over-smoothing. In this paper, we aim at providing a novel explanation to the question of "Why do attributes propagate in GCNNs?''. which not only

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Sun, Guofei, Yongkang Wong, Mohan S. Kankanhalli, Xiangdong Li, and Weidong Geng. "Enhanced 3D Shape Reconstruction With Knowledge Graph of Category Concept." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 3 (2022): 1–20. http://dx.doi.org/10.1145/3491224.

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Reconstructing three-dimensional (3D) objects from images has attracted increasing attention due to its wide applications in computer vision and robotic tasks. Despite the promising progress of recent deep learning–based approaches, which directly reconstruct the full 3D shape without considering the conceptual knowledge of the object categories, existing models have limited usage and usually create unrealistic shapes. 3D objects have multiple forms of representation, such as 3D volume, conceptual knowledge, and so on. In this work, we show that the conceptual knowledge for a category of objec

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Sasaki, Shunsuke, Tasuku Nishihara, Daisuke Ando, and Masahiro Fujita. "Hardware/Software Co-design and Verification Methodology from System Level Based on System Dependence Graph." JUCS - Journal of Universal Computer Science 13, no. (13) (2007): 1972–2001. https://doi.org/10.3217/jucs-013-13-1972.

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System Dependence Graph (SDG) is a graph representation which shows dependencies among statements / expressions in a design. In this paper, we propose a new HW/SW co-design methodology based on SDG. In our method, any combination of C / C++ / SpecC descriptions is acceptable as input designs so that design functions can be specified flexibly. First, the input descriptions are analyzed and verified with static but partially dynamic program checking methods by traversing SDG. With those methods, large descriptions can be processed. Next, those designs are divided into HW and SW parts. In this st

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Ling, Shi Yong, and Jin Hong Gong. "Research of Composite Ontology Mapping Strategy on the Parsing Graph." Advanced Materials Research 765-767 (September 2013): 1068–72. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.1068.

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According to complex context relation of ontology, considering different input schema, ontology graph is created with general environment. Based on ontology structure, the paper constructs multiple level graph representation, By introducing similarity propagation of structural and instance level on context relation and rapid mapping with a rapid matching algorithm, a composite ontology mapping strategy is proposed, which iteratively achieves ontology mapping result with reused idea. Finally feasibility and effectiveness of the strategy is proved by complexity analysis to algorithm and some con

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You, Peiting, Xiang Li, Fan Zhang, and Quanzheng Li. "Connectivity-based Cortical Parcellation via Contrastive Learning on Spatial-Graph Convolution." BME Frontiers 2022 (April 1, 2022): 1–11. http://dx.doi.org/10.34133/2022/9814824.

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Objective. Objective of this work is the development and evaluation of a cortical parcellation framework based on tractography-derived brain structural connectivity. Impact Statement. The proposed framework utilizes novel spatial-graph representation learning methods for solving the task of cortical parcellation, an important medical image analysis and neuroscientific problem. Introduction. The concept of “connectional fingerprint” has motivated many investigations on the connectivity-based cortical parcellation, especially with the technical advancement of diffusion imaging. Previous studies

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Oh, Dongsuk, Jungwoo Lim, Kinam Park, and Heuiseok Lim. "Semantic Representation Using Sub-Symbolic Knowledge in Commonsense Reasoning." Applied Sciences 12, no. 18 (2022): 9202. http://dx.doi.org/10.3390/app12189202.

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The commonsense question and answering (CSQA) system predicts the right answer based on a comprehensive understanding of the question. Previous research has developed models that use QA pairs, the corresponding evidence, or the knowledge graph as an input. Each method executes QA tasks with representations of pre-trained language models. However, the ability of the pre-trained language model to comprehend completely remains debatable. In this study, adversarial attack experiments were conducted on question-understanding. We examined the restrictions on the question-reasoning process of the pre

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Li, Dan, and Qian Gao. "Session Recommendation Model Based on Context-Aware and Gated Graph Neural Networks." Computational Intelligence and Neuroscience 2021 (October 13, 2021): 1–10. http://dx.doi.org/10.1155/2021/7266960.

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The graph neural network (GNN) based approach has been successfully applied to session-based recommendation tasks. However, in the face of complex and changing real-world situations, the existing session recommendation algorithms do not fully consider the context information in user decision-making; furthermore, the importance of context information for the behavior model has been widely recognized. Based on this, this paper presents a session recommendation model based on context-aware and gated graph neural networks (CA-GGNNs). First, this paper presents the session sequence as data of graph

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Zou, Shuilong, Zhaoyang Liu, Kaiqi Wang, et al. "A study on pharmaceutical text relationship extraction based on heterogeneous graph neural networks." Mathematical Biosciences and Engineering 21, no. 1 (2023): 1489–507. http://dx.doi.org/10.3934/mbe.2024064.

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<abstract> <p>Effective information extraction of pharmaceutical texts is of great significance for clinical research. The ancient Chinese medicine text has streamlined sentences and complex semantic relationships, and the textual relationships may exist between heterogeneous entities. The current mainstream relationship extraction model does not take into account the associations between entities and relationships when extracting, resulting in insufficient semantic information to form an effective structured representation. In this paper, we propose a heterogeneous graph neural ne

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Fan, Zhiqiang, Fangyue Chen, Xiaokai Xia, and Yu Liu. "EEG Emotion Classification Based on Graph Convolutional Network." Applied Sciences 14, no. 2 (2024): 726. http://dx.doi.org/10.3390/app14020726.

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EEG-based emotion recognition is a task that uses scalp-EEG data to classify the emotion states of humans. The study of EEG-based emotion recognition can contribute to a large spectrum of application fields including healthcare and human–computer interaction. Recent studies in neuroscience reveal that the brain regions and their interactions play an essential role in the processing of different stimuli and the generation of corresponding emotional states. Nevertheless, such regional interactions, which have been proven to be critical in recognizing emotions in neuroscience, are largely overloo

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Wang, Ge, Zikai Sun, Weiyang HU, and MengHuan Cai. "Author name disambiguation based on heterogeneous graph neural network." PLOS ONE 20, no. 2 (2025): e0310992. https://doi.org/10.1371/journal.pone.0310992.

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With the dramatic increase in the number of published papers and the continuous progress of deep learning technology, the research on name disambiguation is at a historic peak, the number of paper authors is increasing every year, and the situation of authors with the same name is intensifying, therefore, it is a great challenge to accurately assign the newly published papers to their respective authors. The current mainstream methods for author disambiguation are mainly divided into two methods: feature-based clustering and connection-based clustering, but none of the current mainstream metho

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Orlikowski, Cezary, and Rafał Hein. "Port-Based Modeling of Distributed-Lumped Parameter Systems." Solid State Phenomena 164 (June 2010): 183–88. http://dx.doi.org/10.4028/www.scientific.net/ssp.164.183.

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This paper presents a uniform, port-based approach for modeling of both lumped and distributed parameter systems. Port-based model of the distributed system has been defined by application of bond graph methodology and distributed transfer function method (DTFM). The proposed approach combines versatility of port-based modeling and accuracy of distributed transfer function method. A concise representation of lumped-distributed systems has been obtained. The proposed method of modeling enables to formulate input data for computer analysis by application of DTFM.

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Chen, Zhen, Jia Huang, Shengzheng Liu, and Haixia Long. "Multiscale Feature Fusion and Graph Convolutional Network for Detecting Ethereum Phishing Scams." Electronics 13, no. 6 (2024): 1012. http://dx.doi.org/10.3390/electronics13061012.

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With the emergence of blockchain technology, the cryptocurrency market has experienced significant growth in recent years, simultaneously fostering environments conducive to cybercrimes such as phishing scams. Phishing scams on blockchain platforms like Ethereum have become a grave economic threat. Consequently, there is a pressing demand for effective detection mechanisms for these phishing activities to establish a secure financial transaction environment. However, existing methods typically utilize only the most recent transaction record when constructing features, resulting in the loss of

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Yan, Zhaokun, Xiangquan Yang, and Yu Jin. "Considerate motion imagination classification method using deep learning." PLOS ONE 17, no. 10 (2022): e0276526. http://dx.doi.org/10.1371/journal.pone.0276526.

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In order to improve the classification accuracy of motion imagination, a considerate motion imagination classification method using deep learning is proposed. Specifically, based on a graph structure suitable for electroencephalography as input, the proposed model can accurately represent the distribution of electroencephalography electrodes in non-Euclidean space and fully consider the spatial correlation between electrodes. In addition, the spatial-spectral-temporal multi-dimensional feature information was extracted from the spatial-temporal graph representation and spatial-spectral graph r

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Ryazanov, Yu D., and S. V. Nazina. "Building parsers based on syntax diagrams with multiport components." Prikladnaya Diskretnaya Matematika, no. 55 (2022): 102–19. http://dx.doi.org/10.17223/20710410/55/8.

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The problem of constructing parsers from syntax diagrams with multiport components (SD) is solved. An algorithm for constructing a parser based on the GLL algorithm is proposed, which results in the compact representation of the input chain parse forest. The proposed algorithm makes it possible to build parsers based on the SD of an arbitrary structure and does not require preliminary SD transformations. We introduce the concepts of “inference tree” and “parsing forest” for SD and describe the data structures used by the parser, such as a graph-structured stack, a parser descriptor, and a comp

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Hao, Yiran, Yiqiang Sheng, and Jinlin Wang. "A Graph Representation Learning Algorithm for Low-Order Proximity Feature Extraction to Enhance Unsupervised IDS Preprocessing." Applied Sciences 9, no. 20 (2019): 4473. http://dx.doi.org/10.3390/app9204473.

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Most existing studies on an unsupervised intrusion detection system (IDS) preprocessing ignore the relationship among packets. According to the homophily hypothesis, the local proximity structure in the similarity relational graph has similar embedding after preprocessing. To improve the performance of IDS by building a relationship among packets, we propose a packet2vec learning algorithm that extracts accurate local proximity features based on graph representation by adding penalty to node2vec. In this algorithm, we construct a relational graph G’ by using each packet as a node, calculate th

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50

Zou, Jun, Jing Wan, Hao Zhang, and Yunbing Zhang. "A Multi-hop Path Query Answering Model for Knowledge Graph based on Neighborhood Aggregation and Transformer." Journal of Physics: Conference Series 2560, no. 1 (2023): 012049. http://dx.doi.org/10.1088/1742-6596/2560/1/012049.

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Abstract Multi-hop path query answering is a complex task in which a path needs to be inferred from a knowledge graph that contains a head entity and multi-hop relations. The objective is to identify the corresponding tail entity accurately. The representation of the path is a critical factor in this task. However, existing methods do not adequately consider the context information of the entities and relations in the path. To address this issue, this paper proposes a novel multi-hop path query answering model that utilizes an enhanced reasoning path feature representation to incorporate inter

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