Interdependent-path Recurrent Embedding for Knowledge Graph-aware Recommendation by Xiao Sha, Jianwen Wang, Xiaoran Xu, Jianchuan Ding

“…Knowledge graphs (KGs) have demonstrated their effectiveness in providing high-quality recommendations by incorporating rich semantic relationships between entities. …”
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Encrypted traffic classification encoder based on lightweight graph representation by ZhenWei Chen, XiaoXu Wei, YongSheng Wang

“…The lightweight graph representation serves as the network input, and the design mainly includes an embedding layer, a traffic encoder layer based on graph neural networks, and a time information extraction layer, which can separately embed headers and payloads. …”
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MEN: leveraging explainable multimodal encoding network for precision prediction of CYP450 inhibitors by Abena Achiaa Atwereboannah, Wei-Ping Wu, Mugahed A. Al-antari, Sophyani B. Yussif, Chukwuebuka J. Ejiyi, Edwin K. Tenagyei, Grace-Mercure B. Kissanga, Gyarteng S. A. Emmanuel, Yeong Hyeon Gu, Emmanuel Ahene

“…Specifically, the Fingerprint Encoder Network (FEN) processes molecular fingerprints, the Graph Encoder Network (GEN) extracts structural features from graph-based representations, and the Protein Encoder Network (PEN) captures sequential patterns from protein sequences. …”
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Hierarchical clustering methods in a task to find abnormal observations based on groups with broken symmetry by A. N. Kislyakov, S. V. Polyakov

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LiSENCE: A Hybrid Ligand and Sequence Encoder Network for Predicting CYP450 Inhibitors in Safe Multidrug Administration by Abena Achiaa Atwereboannah, Wei-Ping Wu, Sophyani B. Yussif, Muhammed Amin Abdullah, Edwin K. Tenagyei, Chiagoziem C. Ukuoma, Yeong Hyeon Gu, Mugahed A. Al-antari

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Efficient resource allocation for D2D-enabled social IoT networks: A tripartite and time-scale optimization approach by Saurabh Chandra, Rajeev Arya, Maheshwari Prasad Singh

“…The problem is solved in two stages: a tripartite graph-based resource allocation stage and a time-scale optimization stage. …”
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A Flexible and Configurable System to Author Name Disambiguation by Natan de Souza Rodrigues, Celia Ghedini Ralha

“…This paper introduces a configurable and scalable AND system that combines transformer-based embeddings (MiniLM), Graph Convolutional Networks (GCN), and hierarchical clustering. …”
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Integrating IT and OT for Cybersecurity: A Stochastic Optimization Approach via Attack Graphs by Gonzalo Martinez Medina, Krystel K. Castillo-Villar, Tanveer Hossain Bhuiyan

“…This work addresses this gap by presenting an approach to represent a manufacturing IT and OT network as an attack graph that captures vulnerabilities in components, such as the motion control system, spindle, tool changer, sensors, network interfaces, and connectivity through potential vectors. …”
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The Laplacian Energy of an Intuitionistic Fuzzy Rough Graph and its Utilisation in Decision-Making by Shaik Noorjahan, Shaik Sharief Basha

“…It uses lower and upper approximation spaces in various fields, including science, technology, database systems, computer networks, and expert system architecture. The matrix of adjacency of an intuitionistic fuzzy rough graph is described in the article. …”
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Stochastic and Percolating Models of Blocking Computer Networks Dynamics during Distribution of Epidemics of Evolutionary Computer Viruses by S. A. Lesko, A. S. Alyoshkin, V. V. Filatov

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A LEO Satellite Handover Strategy Based on Graph and Multiobjective Multiagent Path Finding by Zhiyun Jiang, Wei Li, Xiangtong Wang, Binbin Liang

“…Low earth orbit (LEO) satellite network can provide services to users anywhere on the earth. …”
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Assessing the interactions between time series signals using weighted horizontal visibility graphs by Arya Teymourlouei, James Reggia, Rodolphe Gentili

“…The visibility graph algorithm is used to map recorded time series signals to complex networks. …”
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GBsim: A Robust GCN-BERT Approach for Cross-Architecture Binary Code Similarity Analysis by Jiang Du, Qiang Wei, Yisen Wang, Xingyu Bai

“…Recent advances in graph neural networks have transformed structural pattern learning in domains ranging from social network analysis to biomolecular modeling. …”
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Contrastive learning of similarity meta-path clustering for multi-behavior recommendation by Juan Liao, Aman Jantan, Zhe Liu, Himanshu Dhumras, Omed Hassan Ahmed

“…Finally, CSMC jointly optimizes multi-behavior and meta-path contrastive objectives to extract both local and high-order semantic signals within a heterogeneous information network graph. Extensive experiments conducted on three real-world benchmark datasets—including ablation and sparsity analyses—demonstrate the superiority of CSMC, achieving average performance gains of 22.27% in Recall and 21.42% in NDCG compared to the strongest baselines.…”
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GNN assisted frequency constrained unit commitment of multi-region power systems with high penetration of renewable energy sources by Mingjian Tuo, Cunzhi Zhao, Mulan Zhang, Tao Gao, Xuequan Shang

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D2D cooperative caching strategy based on graph collaborative filtering model by Ningjiang CHEN, Linming LIAN, Pingjie OU, Xuemei YUAN

“…A D2D cooperative caching strategy based on graph collaborative filtering model was proposed for the problem of difficulty in obtaining sufficient data to predict user preferences in device-to-device (D2D) caching due to the limited signal coverage of base stations.Firstly, a graph collaborative filtering model was constructed, which captured the higher-order connectivity information in the user-content interaction graph through a multilayer graph convolutional neural network, and a multilayer perceptron was used to learn the nonlinear relationship between users and content to predict user preferences.Secondly, in order to minimize the average access delay, considering user preference and cache delay benefit, the cache content placement problem was modeled as a Markov decision process model, and a cooperative cache algorithm based on deep reinforcement learning was designed to solve it.Simulation experiments show that the proposed caching strategy achieves optimal performance compared with existing caching strategies for different content types, user densities, and D2D communication distance parameters.…”
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Design of an Iterative Method for Time Series Forecasting Using Temporal Attention and Hybrid Deep Learning Architectures by Yuvaraja Boddu, A. Manimaran

“…Additionally, by representing the multivariate time series data as a graph in which variables are nodes connected by edges denoting temporal relationships, TGAMTSA leverages Graph Neural Networks (GNNs) to decode complex inter-variable dependencies, resulting in a 20% improvement in prediction accuracy over traditional methods. …”
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GAT-Enhanced YOLOv8_L with Dilated Encoder for Multi-Scale Space Object Detection by Haifeng Zhang, Han Ai, Donglin Xue, Zeyu He, Haoran Zhu, Delian Liu, Jianzhong Cao, Chao Mei

“…Ablation studies further validate the synergistic effect between the graph attention network (GAT) and the Dilated Encoder. …”
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STGLR: A Spacecraft Anomaly Detection Method Based on Spatio-Temporal Graph Learning by Yi Lai, Ye Zhu, Li Li, Qing Lan, Yizheng Zuo

“…It then constructs a spatio-temporal feature extraction module to capture complex spatio-temporal dependencies among variables, leveraging a graph sample and aggregation network to learn embedded features and incorporating an attention mechanism to adaptively select salient features. …”
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Optimal Honeypot Allocation Using Core Attack Graph in Partially Observable Stochastic Games by Achile Leonel Nguemkam, Ahmed Hemida Anwar, Vianney Kengne Tchendji, Deepak K. Tosh, Charles Kamhoua

“…This technique reduces the belief and action spaces, making it possible to manage large-scale networks more efficiently. By focusing the analysis on the core attack graph, our approach minimizes the necessity to process the entire network, leading to substantial reductions in time and memory requirements while maintaining solution accuracy. …”
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