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Artykuły w czasopismach na temat „Graph embedding framework”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

Liang, Jiongqian, Saket Gurukar, and Srinivasan Parthasarathy. "MILE: A Multi-Level Framework for Scalable Graph Embedding." Proceedings of the International AAAI Conference on Web and Social Media 15 (May 22, 2021): 361–72. http://dx.doi.org/10.1609/icwsm.v15i1.18067.

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Recently there has been a surge of interest in designing graph embedding methods. Few, if any, can scale to a large-sized graph with millions of nodes due to both computational complexity and memory requirements. In this paper, we relax this limitation by introducing the MultI-Level Embedding (MILE) framework – a generic methodology allowing contemporary graph embedding methods to scale to large graphs. MILE repeatedly coarsens the graph into smaller ones using a hybrid matching technique to maintain the backbone structure of the graph. It then applies existing embedding methods on the coarses

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Zhou, Houquan, Shenghua Liu, Danai Koutra, Huawei Shen, and Xueqi Cheng. "A Provable Framework of Learning Graph Embeddings via Summarization." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 4946–53. http://dx.doi.org/10.1609/aaai.v37i4.25621.

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Given a large graph, can we learn its node embeddings from a smaller summary graph? What is the relationship between embeddings learned from original graphs and their summary graphs? Graph representation learning plays an important role in many graph mining applications, but learning em-beddings of large-scale graphs remains a challenge. Recent works try to alleviate it via graph summarization, which typ-ically includes the three steps: reducing the graph size by combining nodes and edges into supernodes and superedges,learning the supernode embedding on the summary graph and then restoring th

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Duong, Chi Thang, Trung Dung Hoang, Hongzhi Yin, Matthias Weidlich, Quoc Viet Hung Nguyen, and Karl Aberer. "Scalable robust graph embedding with Spark." Proceedings of the VLDB Endowment 15, no. 4 (2021): 914–22. http://dx.doi.org/10.14778/3503585.3503599.

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Graph embedding aims at learning a vector-based representation of vertices that incorporates the structure of the graph. This representation then enables inference of graph properties. Existing graph embedding techniques, however, do not scale well to large graphs. While several techniques to scale graph embedding using compute clusters have been proposed, they require continuous communication between the compute nodes and cannot handle node failure. We therefore propose a framework for scalable and robust graph embedding based on the MapReduce model, which can distribute any existing embeddin

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Fang, Peng, Arijit Khan, Siqiang Luo, et al. "Distributed Graph Embedding with Information-Oriented Random Walks." Proceedings of the VLDB Endowment 16, no. 7 (2023): 1643–56. http://dx.doi.org/10.14778/3587136.3587140.

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Graph embedding maps graph nodes to low-dimensional vectors, and is widely adopted in machine learning tasks. The increasing availability of billion-edge graphs underscores the importance of learning efficient and effective embeddings on large graphs, such as link prediction on Twitter with over one billion edges. Most existing graph embedding methods fall short of reaching high data scalability. In this paper, we present a general-purpose, distributed, information-centric random walk-based graph embedding framework, DistGER, which can scale to embed billion-edge graphs. DistGER incrementally

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Yang, Tong, Yifei Wang, Long Sha, Jan Engelbrecht, and Pengyu Hong. "Knowledgebra: An Algebraic Learning Framework for Knowledge Graph." Machine Learning and Knowledge Extraction 4, no. 2 (2022): 432–45. http://dx.doi.org/10.3390/make4020019.

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Knowledge graph (KG) representation learning aims to encode entities and relations into dense continuous vector spaces such that knowledge contained in a dataset could be consistently represented. Dense embeddings trained from KG datasets benefit a variety of downstream tasks such as KG completion and link prediction. However, existing KG embedding methods fell short to provide a systematic solution for the global consistency of knowledge representation. We developed a mathematical language for KG based on an observation of their inherent algebraic structure, which we termed as Knowledgebra. B

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Makarov, Ilya, Andrey Savchenko, Arseny Korovko, et al. "Temporal network embedding framework with causal anonymous walks representations." PeerJ Computer Science 8 (January 20, 2022): e858. http://dx.doi.org/10.7717/peerj-cs.858.

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Many tasks in graph machine learning, such as link prediction and node classification, are typically solved using representation learning. Each node or edge in the network is encoded via an embedding. Though there exists a lot of network embeddings for static graphs, the task becomes much more complicated when the dynamic (i.e., temporal) network is analyzed. In this paper, we propose a novel approach for dynamic network representation learning based on Temporal Graph Network by using a highly custom message generating function by extracting Causal Anonymous Walks. We provide a benchmark pipel

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Cheng, Kewei, Xian Li, Yifan Ethan Xu, Xin Luna Dong, and Yizhou Sun. "PGE." Proceedings of the VLDB Endowment 15, no. 6 (2022): 1288–96. http://dx.doi.org/10.14778/3514061.3514074.

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Although product graphs (PGs) have gained increasing attentions in recent years for their successful applications in product search and recommendations, the extensive power of PGs can be limited by the inevitable involvement of various kinds of errors. Thus, it is critical to validate the correctness of triples in PGs to improve their reliability. Knowledge graph (KG) embedding methods have strong error detection abilities. Yet, existing KG embedding methods may not be directly applicable to a PG due to its distinct characteristics: (1) PG contains rich textual signals, which necessitates a jo

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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 (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

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Zhu, Shijie, Jianxin Li, Hao Peng, Senzhang Wang, and Lifang He. "Adversarial Directed Graph Embedding." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (2021): 4741–48. http://dx.doi.org/10.1609/aaai.v35i5.16605.

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Node representation learning for directed graphs is critically important to facilitate many graph mining tasks. To capture the directed edges between nodes, existing methods mostly learn two embedding vectors for each node, source vector and target vector. However, these methods learn the source and target vectors separately. For the node with very low indegree or outdegree, the corresponding target vector or source vector cannot be effectively learned. In this paper, we propose a novel Directed Graph embedding framework based on Generative Adversarial Network, called DGGAN. The main idea is t

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10

Hong, Xiaobin, Tong Zhang, Zhen Cui, et al. "Graph Game Embedding." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (2021): 7711–20. http://dx.doi.org/10.1609/aaai.v35i9.16942.

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Graph embedding aims to encode nodes/edges into low-dimensional continuous features, and has become a crucial tool for graph analysis including graph/node classification, link prediction, etc. In this paper we propose a novel graph learning framework, named graph game embedding, to learn discriminative node representation as well as encode graph structures. Inspired by the spirit of game learning, node embedding is converted to the selection/searching process of player strategies, where each node corresponds to one player and each edge corresponds to the interaction of two players. Then, a uti

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Park, Chanyoung, Donghyun Kim, Jiawei Han, and Hwanjo Yu. "Unsupervised Attributed Multiplex Network Embedding." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5371–78. http://dx.doi.org/10.1609/aaai.v34i04.5985.

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Nodes in a multiplex network are connected by multiple types of relations. However, most existing network embedding methods assume that only a single type of relation exists between nodes. Even for those that consider the multiplexity of a network, they overlook node attributes, resort to node labels for training, and fail to model the global properties of a graph. We present a simple yet effective unsupervised network embedding method for attributed multiplex network called DMGI, inspired by Deep Graph Infomax (DGI) that maximizes the mutual information between local patches of a graph, and t

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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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13

Chang, Heng, Yu Rong, Tingyang Xu, et al. "A Restricted Black-Box Adversarial Framework Towards Attacking Graph Embedding Models." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 3389–96. http://dx.doi.org/10.1609/aaai.v34i04.5741.

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With the great success of graph embedding model on both academic and industry area, the robustness of graph embedding against adversarial attack inevitably becomes a central problem in graph learning domain. Regardless of the fruitful progress, most of the current works perform the attack in a white-box fashion: they need to access the model predictions and labels to construct their adversarial loss. However, the inaccessibility of model predictions in real systems makes the white-box attack impractical to real graph learning system. This paper promotes current frameworks in a more general and

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14

Schab, Esteban, Carla Casanova, and Fabiana Piccoli. "Graph Representations for Reinforcement Learning." Journal of Computer Science and Technology 24, no. 1 (2024): e03. http://dx.doi.org/10.24215/16666038.24.e03.

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Graph analysis is becoming increasingly important due to the expressive power of graph models and the efficient algorithms available for processing them. Reinforcement Learning is one domain that could benefit from advancements in graph analysis, given that a learning agent may be integrated into an environment that can be represented as a graph. Nevertheless, the structural irregularity of graphs and the lack of prior labels make it difficult to integrate such a model into modern Reinforcement Learning frameworks that rely on artificial neural networks. Graph embedding enables the learning of

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Hu, Shengze, Weixin Zeng, Pengfei Zhang, and Jiuyang Tang. "Neural Graph Similarity Computation with Contrastive Learning." Applied Sciences 12, no. 15 (2022): 7668. http://dx.doi.org/10.3390/app12157668.

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Computing the similarity between graphs is a longstanding and challenging problem with many real-world applications. Recent years have witnessed a rapid increase in neural-network-based methods, which project graphs into embedding space and devise end-to-end frameworks to learn to estimate graph similarity. Nevertheless, these solutions usually design complicated networks to capture the fine-grained interactions between graphs, and hence have low efficiency. Additionally, they rely on labeled data for training the neural networks and overlook the useful information hidden in the graphs themsel

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16

Guo, Zihao, Qingyun Sun, Haonan Yuan, et al. "GraphMoRE: Mitigating Topological Heterogeneity via Mixture of Riemannian Experts." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 11 (2025): 11754–62. https://doi.org/10.1609/aaai.v39i11.33279.

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Real-world graphs have inherently complex and diverse topological patterns, known as topological heterogeneity. Most existing works learn graph representation in a single constant curvature space that is insufficient to match the complex geometric shapes, resulting in low-quality embeddings with high distortion. This also constitutes a critical challenge for graph foundation models, which are expected to uniformly handle a wide variety of diverse graph data. Recent studies have indicated that product manifold gains the possibility to address topological heterogeneity. However, the product mani

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Wu, Xueyi, Yuanyuan Xu, Wenjie Zhang, and Ying Zhang. "Billion-Scale Bipartite Graph Embedding: A Global-Local Induced Approach." Proceedings of the VLDB Endowment 17, no. 2 (2023): 175–83. http://dx.doi.org/10.14778/3626292.3626300.

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Bipartite graph embedding (BGE), as the fundamental task in bipartite network analysis, is to map each node to compact low-dimensional vectors that preserve intrinsic properties. The existing solutions towards BGE fall into two groups: metric-based methods and graph neural network-based (GNN-based) methods. The latter typically generates higher-quality embeddings than the former due to the strong representation ability of deep learning. Nevertheless, none of the existing GNN-based methods can handle billion-scale bipartite graphs due to the expensive message passing or complex modelling choice

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Baumslag, Marc, and Bojana Obrenić. "Index-Shuffle Graphs." International Journal of Foundations of Computer Science 08, no. 03 (1997): 289–304. http://dx.doi.org/10.1142/s0129054197000197.

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Index-shuffle graphs are introduced as candidate interconnection networks for parallel computers. The comparative advantages of index-shuffle graphs over the standard bounded-degree "approximations" of the hypercube, namely butterfly-like and shuffle-like graphs, are demonstrated in the theoretical framework of graph embedding and network emulations. An N-node index-shuffle graph emulates: • an N-node shuffle-exchange graph with no slowdown, which the currently best emulations of shuffle-like graphs by hypercubes and butterflies incur a slowdown of Ω( log N). • its like-sized butterfly graph w

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Zhang, Kainan, Zhipeng Cai, and Daehee Seo. "Privacy-Preserving Federated Graph Neural Network Learning on Non-IID Graph Data." Wireless Communications and Mobile Computing 2023 (February 3, 2023): 1–13. http://dx.doi.org/10.1155/2023/8545101.

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Since the concept of federated learning (FL) was proposed by Google in 2017, many applications have been combined with FL technology due to its outstanding performance in data integration, computing performance, privacy protection, etc. However, most traditional federated learning-based applications focus on image processing and natural language processing with few achievements in graph neural networks due to the graph’s nonindependent identically distributed (IID) nature. Representation learning on graph-structured data generates graph embedding, which helps machines understand graphs effecti

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Sun, Jiankai, Bortik Bandyopadhyay, Armin Bashizade, Jiongqian Liang, P. Sadayappan, and Srinivasan Parthasarathy. "ATP: Directed Graph Embedding with Asymmetric Transitivity Preservation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 265–72. http://dx.doi.org/10.1609/aaai.v33i01.3301265.

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Directed graphs have been widely used in Community Question Answering services (CQAs) to model asymmetric relationships among different types of nodes in CQA graphs, e.g., question, answer, user. Asymmetric transitivity is an essential property of directed graphs, since it can play an important role in downstream graph inference and analysis. Question difficulty and user expertise follow the characteristic of asymmetric transitivity. Maintaining such properties, while reducing the graph to a lower dimensional vector embedding space, has been the focus of much recent research. In this paper, we

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Sun, Ke, Zhouchen Lin, and Zhanxing Zhu. "Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labeled Nodes." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5892–99. http://dx.doi.org/10.1609/aaai.v34i04.6048.

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Graph Convolutional Networks (GCNs) play a crucial role in graph learning tasks, however, learning graph embedding with few supervised signals is still a difficult problem. In this paper, we propose a novel training algorithm for Graph Convolutional Network, called Multi-Stage Self-Supervised (M3S) Training Algorithm, combined with self-supervised learning approach, focusing on improving the generalization performance of GCNs on graphs with few labeled nodes. Firstly, a Multi-Stage Training Framework is provided as the basis of M3S training method. Then we leverage DeepCluster technique, a pop

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Song, Yumeng, Xiaohua Li, Fangfang Li, and Ge Yu. "Learning from Feature and Global Topologies: Adaptive Multi-View Parallel Graph Contrastive Learning." Mathematics 12, no. 14 (2024): 2277. http://dx.doi.org/10.3390/math12142277.

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To address the limitations of existing graph contrastive learning methods, which fail to adaptively integrate feature and topological information and struggle to efficiently capture multi-hop information, we propose an adaptive multi-view parallel graph contrastive learning framework (AMPGCL). It is an unsupervised graph representation learning method designed to generate task-agnostic node embeddings. AMPGCL constructs and encodes feature and topological views to mine feature and global topological information. To encode global topological information, we introduce an H-Transformer to decoupl

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Ye, Yutong, Xiang Lian, and Mingsong Chen. "Efficient Exact Subgraph Matching via GNN-Based Path Dominance Embedding." Proceedings of the VLDB Endowment 17, no. 7 (2024): 1628–41. http://dx.doi.org/10.14778/3654621.3654630.

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The classic problem of exact subgraph matching returns those subgraphs in a large-scale data graph that are isomorphic to a given query graph, which has gained increasing importance in many real-world applications such as social network analysis, knowledge graph discovery in the Semantic Web, bibliographical network mining, and so on. In this paper, we propose a novel and effective graph neural network (GNN)-based path embedding framework (GNN-PE), which allows efficient exact subgraph matching without introducing false dismissals. Unlike traditional GNN-based graph embeddings that only produc

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Xu, You-Wei, Hong-Jun Zhang, Kai Cheng, Xiang-Lin Liao, Zi-Xuan Zhang, and Yun-Bo Li. "Knowledge graph embedding with entity attributes using hypergraph neural networks." Intelligent Data Analysis 26, no. 4 (2022): 959–75. http://dx.doi.org/10.3233/ida-216007.

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Knowledge graph embedding is aimed at capturing the semantic information of entities by modeling the structural information between entities. For long-tail entities which lack sufficient structural information, general knowledge graph embedding models often show relatively low performance in link prediction. In order to solve such problems, this paper proposes a general knowledge graph embedding framework to learn the structural information as well as the attribute information of the entities simultaneously. Under this framework, a H-AKRL (Hypergraph Neural Networks based Attribute-embodied Kn

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Cheng, Minjie, Dixin Luo, and Hongteng Xu. "WatE: A Wasserstein t-distributed Embedding Method for Information-enriched Graph Visualization." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 15 (2025): 16010–18. https://doi.org/10.1609/aaai.v39i15.33758.

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As a fundamental problem of graph analysis, graph visualization aims to embed a set of graphs in a low-dimensional (e.g., 2D) space and provide insights into their distribution and clustering structure. Focusing on this problem, we propose a novel Wasserstein t-distributed embedding (WatE) method, leading to an information-enriched graph visualization paradigm. Our method learns a graph neural network to represent each graph as the mean and covariance of its node embedding distribution. Accordingly, our method can visualize each graph as an ellipse (determined by the mean and the covariance) r

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Peng, Yun, Byron Choi, and Jianliang Xu. "Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art." Data Science and Engineering 6, no. 2 (2021): 119–41. http://dx.doi.org/10.1007/s41019-021-00155-3.

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AbstractGraphs have been widely used to represent complex data in many applications, such as e-commerce, social networks, and bioinformatics. Efficient and effective analysis of graph data is important for graph-based applications. However, most graph analysis tasks are combinatorial optimization (CO) problems, which are NP-hard. Recent studies have focused a lot on the potential of using machine learning (ML) to solve graph-based CO problems. Most recent methods follow the two-stage framework. The first stage is graph representation learning, which embeds the graphs into low-dimension vectors

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Yoon, Jisung, Kai-Cheng Yang, Woo-Sung Jung, and Yong-Yeol Ahn. "Persona2vec: a flexible multi-role representations learning framework for graphs." PeerJ Computer Science 7 (March 30, 2021): e439. http://dx.doi.org/10.7717/peerj-cs.439.

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Graph embedding techniques, which learn low-dimensional representations of a graph, are achieving state-of-the-art performance in many graph mining tasks. Most existing embedding algorithms assign a single vector to each node, implicitly assuming that a single representation is enough to capture all characteristics of the node. However, across many domains, it is common to observe pervasively overlapping community structure, where most nodes belong to multiple communities, playing different roles depending on the contexts. Here, we propose persona2vec, a graph embedding framework that efficien

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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 Grap

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Wang, YueQun, LiYan Dong, YongLi Li, and Hao Zhang. "Multitask feature learning approach for knowledge graph enhanced recommendations with RippleNet." PLOS ONE 16, no. 5 (2021): e0251162. http://dx.doi.org/10.1371/journal.pone.0251162.

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Introducing a knowledge graph into a recommender system as auxiliary information can effectively solve the sparse and cold start problems existing in traditional recommender systems. In recent years, many researchers have performed related work. A recommender system with knowledge graph embedding learning characteristics can be combined with a recommender system of the following three forms: one-by-one learning, joint learning, and alternating learning. For current knowledge graph embedding, a deep learning framework only has one embedding mode, which fails to excavate the potential informatio

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Zhou, Jingya, Ling Liu, Wenqi Wei, and Jianxi Fan. "Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding." ACM Computing Surveys 55, no. 2 (2023): 1–35. http://dx.doi.org/10.1145/3491206.

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Network representation learning (NRL) advances the conventional graph mining of social networks, knowledge graphs, and complex biomedical and physics information networks. Dozens of NRL algorithms have been reported in the literature. Most of them focus on learning node embeddings for homogeneous networks, but they differ in the specific encoding schemes and specific types of node semantics captured and used for learning node embedding. This article reviews the design principles and the different node embedding techniques for NRL over homogeneous networks. To facilitate the comparison of diffe

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K. Dinesh Kumar, Et al. "Visual Storytelling: A Generative Adversarial Networks (GANs) and Graph Embedding Framework." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 1899–906. http://dx.doi.org/10.17762/ijritcc.v11i9.9184.

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Visual storytelling is a powerful educational tool, using image sequences to convey complex ideas and establish emotional connections with the audience. A study at the Chinese University of Hong Kong found that 92.7% of students prefer visual storytelling through animation over text alone [21]. Our approach integrates dual coding and propositional theory to generate visual representations of text, such as graphs and images, thereby enhancing students' memory retention and visualization skills. We use Generative Adversarial Networks (GANs) with graph data to generate images while preserving sem

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Sun, Jiankai, and Srinivasan Parthasarathy. "Symmetrization for Embedding Directed Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 10043–44. http://dx.doi.org/10.1609/aaai.v33i01.330110043.

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In this paper, we propose to solve the directed graph embedding problem via a two stage approach: in the first stage, the graph is symmetrized in one of several possible ways, and in the second stage, the so-obtained symmetrized graph is embeded using any state-of-the-art (undirected) graph embedding algorithm. Note that it is not the objective of this paper to propose a new (undirected) graph embedding algorithm or discuss the strengths and weaknesses of existing ones; all we are saying is that whichever be the suitable graph embedding algorithm, it will fit in the above proposed symmetrizati

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Cong, Kai, Tao Li, Beibei Li, Zhan Gao, Yanbin Xu, and Fei Gao. "KGDetector: Detecting Chinese Sensitive Information via Knowledge Graph-Enhanced BERT." Security and Communication Networks 2022 (May 19, 2022): 1–9. http://dx.doi.org/10.1155/2022/4656837.

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The Bidirectional Encoder Representations from Transformers (BERT) technique has been widely used in detecting Chinese sensitive information. However, existing BERT-based frameworks usually fail to emphasize key entities in the texts that contribute significantly to knowledge inference. To meet this gap, we propose a BERT and knowledge graph-based novel framework to detect Chinese sensitive information (named KGDetector). Specifically, we first train a pretrained knowledge graph-based Chinese entity embedding model to characterize entities in the Chinese textual inputs. Finally, we propose an

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Guo, Wenjie, Wenbiao Du, Xiuqi Yang, et al. "MalHAPGNN: An Enhanced Call Graph-Based Malware Detection Framework Using Hierarchical Attention Pooling Graph Neural Network." Sensors 25, no. 2 (2025): 374. https://doi.org/10.3390/s25020374.

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While deep learning techniques have been extensively employed in malware detection, there is a notable challenge in effectively embedding malware features. Current neural network methods primarily capture superficial characteristics, lacking in-depth semantic exploration of functions and failing to preserve structural information at the file level. Motivated by the aforementioned challenges, this paper introduces MalHAPGNN, a novel framework for malware detection that leverages a hierarchical attention pooling graph neural network based on enhanced call graphs. Firstly, to ensure semantic rich

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Yao, Zhen, Wen Zhang, Mingyang Chen, Yufeng Huang, Yi Yang, and Huajun Chen. "Analogical Inference Enhanced Knowledge Graph Embedding." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 4801–8. http://dx.doi.org/10.1609/aaai.v37i4.25605.

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Knowledge graph embedding (KGE), which maps entities and relations in a knowledge graph into continuous vector spaces, has achieved great success in predicting missing links in knowledge graphs. However, knowledge graphs often contain incomplete triples that are difficult to inductively infer by KGEs. To address this challenge, we resort to analogical inference and propose a novel and general self-supervised framework AnKGE to enhance KGE models with analogical inference capability. We propose an analogical object retriever that retrieves appropriate analogical objects from entity-level, relat

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Wang, Lijing, Aniruddha Adiga, Jiangzhuo Chen, Adam Sadilek, Srinivasan Venkatramanan, and Madhav Marathe. "CausalGNN: Causal-Based Graph Neural Networks for Spatio-Temporal Epidemic Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (2022): 12191–99. http://dx.doi.org/10.1609/aaai.v36i11.21479.

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Infectious disease forecasting has been a key focus in the recent past owing to the COVID-19 pandemic and has proved to be an important tool in controlling the pandemic. With the advent of reliable spatiotemporal data, graph neural network models have been able to successfully model the inter-relation between the cross-region signals to produce quality forecasts, but like most deep-learning models they do not explicitly incorporate the underlying causal mechanisms. In this work, we employ a causal mechanistic model to guide the learning of the graph embeddings and propose a novel learning fram

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Sumet Mehta. "Generalized Multi-manifold Graph Ensemble Embedding for Multi-View Dimensionality Reduction." Lahore Garrison University Research Journal of Computer Science and Information Technology 4, no. 4 (2020): 55–72. http://dx.doi.org/10.54692/lgurjcsit.2020.0404109.

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In this paper, we propose a new dimension reduction (DR) algorithm called ensemble graph-based locality preserving projections (EGLPP); to overcome the neighborhood size k sensitivity in locally preserving projections (LPP). EGLPP constructs a homogeneous ensemble of adjacency graphs by varying neighborhood size k and finally uses the integrated embedded graph to optimize the low-dimensional projections. Furthermore, to appropriately handle the intrinsic geometrical structure of the multi-view data and overcome the dimensionality curse, we propose a generalized multi-manifold graph ensemble em

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Chen, Qianyu, Xin Li, Kunnan Geng, and Mingzhong Wang. "Context-Aware Safe Medication Recommendations with Molecular Graph and DDI Graph Embedding." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (2023): 7053–60. http://dx.doi.org/10.1609/aaai.v37i6.25861.

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Molecular structures and Drug-Drug Interactions (DDI) are recognized as important knowledge to guide medication recommendation (MR) tasks, and medical concept embedding has been applied to boost their performance. Though promising performance has been achieved by leveraging Graph Neural Network (GNN) models to encode the molecular structures of medications or/and DDI, we observe that existing models are still defective: 1) to differentiate medications with similar molecules but different functionality; or/and 2) to properly capture the unintended reactions between drugs in the embedding space.

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Li, Hongchan, Zhuang Zhu, Haodong Zhu, and Baohua Jin. "Fusing Attribute Character Embeddings with Truncated Negative Sampling for Entity Alignment." Electronics 12, no. 8 (2023): 1947. http://dx.doi.org/10.3390/electronics12081947.

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Finding pairs of entities from two different knowledge graphs that reflect the same real-world object is the purpose of entity alignment for knowledge graphs. In recent years, techniques that use entity alignment for knowledge fusion have received widespread attention. In this article, we suggest a method for entity alignment using truncated negative sampling with attribute character embedding. The method mainly makes use of the relationship and attribute data in heterogeneous knowledge graphs to fulfil the entity alignment task. Firstly, the framework uses relationship mapping to unify the na

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YOU, QUBO, NANNING ZHENG, LING GAO, SHAOYI DU, and YANG WU. "ANALYSIS OF SOLUTION FOR SUPERVISED GRAPH EMBEDDING." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 07 (2008): 1283–99. http://dx.doi.org/10.1142/s021800140800679x.

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Recently, Graph Embedding Framework has been proposed for feature extraction. However, it is still an open issue on how to compute robust discriminant transformation for this purpose. In this paper, we show that supervised graph embedding algorithms share a general criterion. Based on the analysis of this criterion, we propose a general solution, called General Solution for Supervised Graph Embedding (GSSGE), for extracting the robust discriminant transformation of Supervised Graph Embedding. Then, we analyze the superiority of our algorithm over traditional algorithms. Extensive experiments o

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Ramasinghe, Sameera, and Simon Lucey. "A Learnable Radial Basis Positional Embedding for Coordinate-MLPs." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (2023): 2137–45. http://dx.doi.org/10.1609/aaai.v37i2.25307.

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We propose a novel method to enhance the performance of coordinate-MLPs (also referred to as neural fields) by learning instance-specific positional embeddings. End-to-end optimization of positional embedding parameters along with network weights leads to poor generalization performance. Instead, we develop a generic framework to learn the positional embedding based on the classic graph-Laplacian regularization, which can implicitly balance the trade-off between memorization and generalization. This framework is then used to propose a novel positional embedding scheme, where the hyperparameter

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Kochsiek, Adrian, and Rainer Gemulla. "Parallel training of knowledge graph embedding models." Proceedings of the VLDB Endowment 15, no. 3 (2021): 633–45. http://dx.doi.org/10.14778/3494124.3494144.

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Knowledge graph embedding (KGE) models represent the entities and relations of a knowledge graph (KG) using dense continuous representations called embeddings. KGE methods have recently gained traction for tasks such as knowledge graph completion and reasoning as well as to provide suitable entity representations for downstream learning tasks. While a large part of the available literature focuses on small KGs, a number of frameworks that are able to train KGE models for large-scale KGs by parallelization across multiple GPUs or machines have recently been proposed. So far, the benefits and dr

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Hu, Zhichao, Likun Liu, Haining Yu, and Xiangzhan Yu. "Using Graph Representation in Host-Based Intrusion Detection." Security and Communication Networks 2021 (December 7, 2021): 1–13. http://dx.doi.org/10.1155/2021/6291276.

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Cybersecurity has become an important part of our daily lives. As an important part, there are many researches on intrusion detection based on host system call in recent years. Compared to sentences, a sequence of system calls has unique characteristics. It contains implicit pattern relationships that are less sensitive to the order of occurrence and that have less impact on the classification results when the frequency of system calls varies slightly. There are also various properties such as resource consumption, execution time, predefined rules, and empirical weights of system calls. Common

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Fan, Liuyi, Wei Chen, and Xiaoyan Jiang. "Cross-Correlation Fusion Graph Convolution-Based Object Tracking." Symmetry 15, no. 3 (2023): 771. http://dx.doi.org/10.3390/sym15030771.

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Most popular graph attention networks treat pixels of a feature map as individual nodes, which makes the feature embedding extracted by the graph convolution lack the integrity of the object. Moreover, matching between a template graph and a search graph using only part-level information usually causes tracking errors, especially in occlusion and similarity situations. To address these problems, we propose a novel end-to-end graph attention tracking framework that has high symmetry, combining traditional cross-correlation operations directly. By utilizing cross-correlation operations, we effec

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Chen, Libin, Luyao Wang, Chengyi Zeng, Hongfu Liu, and Jing Chen. "DHGEEP: A Dynamic Heterogeneous Graph-Embedding Method for Evolutionary Prediction." Mathematics 10, no. 22 (2022): 4193. http://dx.doi.org/10.3390/math10224193.

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Current graph-embedding methods mainly focus on static homogeneous graphs, where the entity type is the same and the topology is fixed. However, in real networks, such as academic networks and shopping networks, there are typically various types of nodes and temporal interactions. The dynamical and heterogeneous components of graphs in general contain abundant information. Currently, most studies on dynamic graphs do not sufficiently consider the heterogeneity of the network in question, and hence the semantic information of the interactions between heterogeneous nodes is missing in the graph

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Chen, Binghui, Pengyu Li, Zhaoyi Yan, Biao Wang, and Lei Zhang. "Deep Metric Learning with Graph Consistency." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (2021): 982–90. http://dx.doi.org/10.1609/aaai.v35i2.16182.

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Deep Metric Learning (DML) has been more attractive and widely applied in many computer vision tasks, in which a discriminative embedding is requested such that the image features belonging to the same class are gathered together and the ones belonging to different classes are pushed apart. Most existing works insist to learn this discriminative embedding by either devising powerful pair-based loss functions or hard-sample mining strategies. However, in this paper, we start from an another perspective and propose Deep Consistent Graph Metric Learning (CGML) framework to enhance the discriminat

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Liu, Ying, Zengyu Wei, Long Chen, Cai Xu, and Ziyu Guan. "Multi-Modal Temporal Dynamic Graph Construction for Stock Rank Prediction." Mathematics 13, no. 5 (2025): 845. https://doi.org/10.3390/math13050845.

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Stock rank prediction is an important and challenging task. Recently, graph-based prediction methods have emerged as a valuable approach for capturing the complex relationships between stocks. Existing works mainly construct static undirected relational graphs, leading to two main drawbacks: (1) overlooking the bidirectional asymmetric effects of stock data, i.e., financial messages affect each other differently when they occur at different nodes of the graph; and (2) failing to capture the dynamic relationships of stocks over time. In this paper, we propose a Multi-modal Temporal Dynamic Grap

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Xu, Deng, Chao Zhang, Cong Guo, Chunlin Chen, and Huaxiong Li. "Fast Incomplete Multi-view Clustering with Adaptive Similarity Completion and Reconstruction." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 20 (2025): 21734–42. https://doi.org/10.1609/aaai.v39i20.35478.

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Recently, anchor-based incomplete multi-view clustering (IMVC) has been widely adopted for fast clustering, but most existing approaches still encounter some issues: (1) They generally rely on the observed samples to construct anchor graphs, ignoring the potentially useful information of missing instances. (2) Most methods attempt to learn a consensus anchor graph, failing to fully excavate the complementary information and high-order correlations across views. (3) They generally apply post-processing on learned anchor graph to seek latent embeddings, making them not globally-optimal. To addre

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Li, Haohao, Mingliang Gao, Huibing Wang, and Gwanggil Jeon. "Multi-View Projection Learning via Adaptive Graph Embedding for Dimensionality Reduction." Electronics 12, no. 13 (2023): 2934. http://dx.doi.org/10.3390/electronics12132934.

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In order to explore complex structures and relationships hidden in data, plenty of graph-based dimensionality reduction methods have been widely investigated and extended to the multi-view learning field. For multi-view dimensionality reduction, the key point is extracting the complementary and compatible multi-view information to analyze the complex underlying structure of the samples, which is still a challenging task. We propose a novel multi-view dimensionality reduction algorithm that integrates underlying structure learning and dimensionality reduction for each view into one framework. B

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Li, Gen, Tri-Hai Nguyen, and Jason J. Jung. "Traffic Incident Detection Based on Dynamic Graph Embedding in Vehicular Edge Computing." Applied Sciences 11, no. 13 (2021): 5861. http://dx.doi.org/10.3390/app11135861.

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With a large of time series dataset from the Internet of Things in Ambient Intelligence-enabled smart environments, many supervised learning-based anomaly detection methods have been investigated but ignored the correlation among the time series. To address this issue, we present a new idea for anomaly detection based on dynamic graph embedding, in which the dynamic graph comprises the multiple time series and their correlation in each time interval. We propose an entropy for measuring a graph’s information injunction with a correlation matrix to define similarity between graphs. A dynamic gra

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