An Order-Independent Algorithm for Learning Chain Graphs by Mohammad Ali Javidian, Marco Valtorta, Pooyan Jamshidi

“…LWF chain graphs combine directed acyclic graphs and undirected graphs. …”
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Measuring the Inferential Values of Relations in Knowledge Graphs by Xu Zhang, Xiaojun Kang, Hong Yao, Lijun Dong

“…Knowledge graphs, as an important research direction in artificial intelligence, have been widely applied in many fields and tasks. …”
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Edge and Node Enhancement Graph Convolutional Network: Imbalanced Graph Node Classification Method Based on Edge-Node Collaborative Enhancement by Jiadong Tian, Jiali Lin, Dagang Li

“…Experimental comparisons on four public graph datasets reveal that, compared to baseline methods, our proposed method achieves notable improvements in Recall and AUC metrics, particularly in sparsely connected datasets.…”
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Improving drug-induced liver injury prediction using graph neural networks with augmented graph features from molecular optimisation by Taeyeub Lee, Joram M. Posma

“…Methods We evaluated several GNN architectures, including Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), Graph Sample and Aggregation (GraphSAGE), and Graph Isomorphism Networks (GINs), using the latest FDA DILI dataset and other molecular property prediction datasets. …”
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Generating graph perturbations to enhance the generalization of GNNs by Sofiane Ennadir, Giannis Nikolentzos, Michalis Vazirgiannis, Henrik Boström

“…This work aims to improve the generalization ability of GNNs by increasing the size of the training set of a given problem. The new samples are generated using an iterative contrastive learning procedure that augments the dataset during the training, in a task-relevant approach, by manipulating the graph topology. …”
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MalHAPGNN: An Enhanced Call Graph-Based Malware Detection Framework Using Hierarchical Attention Pooling Graph Neural Network by Wenjie Guo, Wenbiao Du, Xiuqi Yang, Jingfeng Xue, Yong Wang, Weijie Han, Jingjing Hu

“…Firstly, to ensure semantic richness, a Bidirectional Encoder Representations from Transformers-based (BERT) attribute-enhanced function embedding method is proposed for the extraction of node attributes in the function call graph. Subsequently, this work designs a hierarchical graph neural network that integrates attention mechanisms and pooling operations, complemented by function node sampling and structural learning strategies. …”
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Pattern Recognition in Urban Maps Based on Graph Structures by Xiaomin Lu, Zhiyi Zhang, Haoran Song, Haowen Yan

“…Experimental validation utilized building and road network data from multiple cities, constructing a dataset of 600 samples divided into two subsets: Sample Set 1 (for parameter threshold calibration and rule generation) and Sample Set 2 (for algorithm performance validation and transferability testing). …”
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Property Graph Framework for Geographical Routes in Sports Training by Alen Rajšp, Iztok Fister

“…The research concludes by presenting a case study in which a property graph that enables cycling route generation was created for the country of Slovenia, and a sample training route was generated.…”
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Graph Contrastive Pre-training for Anti-money Laundering by Hanbin Lu, Haosen Wang

“…At present, many studies model the AML task as the graph and leverage graph neural network (GNN) for node/edge classification. …”
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Constructing ancestral recombination graphs through reinforcement learning by Mélanie Raymond, Marie-Hélène Descary, Cédric Beaulac, Fabrice Larribe

“…IntroductionOver the years, many approaches have been proposed to build ancestral recombination graphs (ARGs), graphs used to represent the genetic relationship between individuals. …”
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Spatio-Temporal Graphs in Transportation: Challenges, Optimization, and Prospects by Aleksandr Rakhmangulov, Nikita Osintsev, Pavel Mishkurov

“…The purpose of the study is to identify trends in the use of spatio-temporal graphs for solving various problems in transportation, as well as the most common methods of optimization of such graphs. …”
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Active Learning Framework for Improving Knowledge Graph Accuracy by Donghyun Kim, Hyeongjun Yang, Seokju Hwang, Kyuhwan Yeom, Midan Shim, Kyong-Ho Lee

“…Extensive experimental results demonstrate the effectiveness of the proposed active learning framework and sampling strategies in improving knowledge graph accuracy. …”
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Nonequilibrium steady-state dynamics of Markov processes on graphs by Stefano Crotti, Thomas Barthel, Alfredo Braunstein

“…The method provides access to precise temporal correlations, which, in some regimes, would be virtually impossible to estimate by sampling.…”
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Stealthy graph backdoor attack based on feature trigger by Yang Chen, Zhou Bin, Haixing Zhao

“…Abstract Recent studies have shown that Graph Neural Networks (GNNs) are vulnerable to backdoor attacks. …”
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Double difference earthquake location with graph neural networks by Ian W. McBrearty, Gregory C. Beroza

“…Our architecture uses one graph to represent the stations, a second graph to represent the sources, and creates the Cartesian product graph between the two graphs to capture the relationships between the sources and stations (e.g., the residuals and travel time partial derivatives). …”
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BERT-GraphSAGE: hybrid approach to spam detection by F. Zouak, O. El Beqqali, J. Riffi

“…This enables GraphSAGE to generalize to unseen emails by sampling and aggregating the characteristics of neighboring emails to produce robust node representations. …”
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One-Step Graph Fusion Fuzzy Clustering Network by Bin Tang, Zhongyang Zhou, Xuewen Liu, Feiyu Chen

“…Graph clustering plays a crucial role in uncovering implicit information within data which can be used to rationally classify potential data samples in unsupervised scenarios. …”
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Graph contrastive learning with node-level accurate difference by Pengfei Jiao, Kaiyan Yu, Qing Bao, Ying Jiang, Xuan Guo, Zhidong Zhao

“…Therefore, we argue that it is necessary to design a method that can quantify the dissimilarity between the original and augmented graphs to more accurately capture the relationships between samples. …”
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Code vulnerability detection method based on graph neural network by Hao CHEN, Ping YI

“…The schemes of using neural networks for vulnerability detection are mostly based on traditional natural language processing ideas, processing the code as array samples and ignoring the structural features in the code, which may omit possible vulnerabilities.A code vulnerability detection method based on graph neural network was proposed, which realized function-level code vulnerability detection through the control flow graph feature of the intermediate language.Firstly, the source code was compiled into an intermediate representation, and then the control flow graph containing structural information was extracted.At the same time, the word vector embedding algorithm was used to initialize the vector of basic block to extract the code semantic information.Then both of above were spliced to generate the graph structure sample data.The multilayer graph neural network model was trained and tested on graph structure data features.The open source vulnerability sample data set was used to generate test data to evaluate the method proposed.The results show that the method effectively improves the vulnerability detection ability.…”
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Kolmogorov–Smirnov-Based Edge Centrality Measure for Metric Graphs by Christina Durón, Hannah Kravitz, Moysey Brio

“…We compare the proposed measure with eight vertex centrality measures applied to a line graph representation of each metric graph, as well as with two edge centrality measures applied directly to each metric graph. …”
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