Graph Attention Networks

Also known as GAT

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4 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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Multi-Level Graph Attention Network Contrastive Learning for Knowledge-Aware Recommendation
May 8, 2026Zhifei Hu, Feng XiaGraph Attention Networks Multi-View Learning
Durham University, Durham, United Kingdom · University of Sheffield, Sheffield, United Kingdom
Inductive Power Grid Cascading Failure Analysis with GRU-Gated Graph Attention
May 7, 2026Tianxin Zhou, Xiang Li, Haibing LuGraph Attention Networks Graph Representation Learning
Department of Computer Science & Engineering, Santa Clara University, Santa Clara, USA · Dept. of Information Systems & Analytics, Santa Clara University, Santa Clara, USA
Scalable Context-Aware Graph Attention for Unsupervised Anomaly Detection in Large-Scale Mobile Networks
May 1, 2026Sara Malacarne, Eirik Hoel-Høiseth, Erlend Aune +2Graph Attention Networks Reconstruction-Based Anomaly Detection
Telenor Research and Innovation, Norway · Norwegian University of Science and Technology (NTNU), Norway · Hance, Norway +2
Context-Aware Graph Attention for Unsupervised Telco Anomaly Detection
Apr 29, 2026Sara Malacarne, Eirik Hoel-Høiseth, Erlend Aune +2Graph Attention Networks Reconstruction-Based Anomaly Detection
Telenor Research and Innovation, Norway · Norwegian University of Science and Technology (NTNU), Norway · Hance, Norway +2
Distance-Misaligned Training in Graph Transformers and Adaptive Graph-Aware Control
Apr 24, 2026Qinhan Hou, Jing TangGraph Attention Networks Stochastic Block Model
University of Helsinki Helsinki, Finland
Robustness of Spatio-temporal Graph Neural Networks for Fault Location in Partially Observable Distribution Grids
Apr 22, 2026Burak Karabulut, Carlo Manna, Chris DevelderGraph Attention Networks Graph Neural Networks
Ghent University – imec · VITO
FOCAL-Attention for Heterogeneous Multi-Label Prediction
Apr 21, 2026Chenghao Zhang, Qingqing Long, Ludi Wang +3Graph Attention Networks Multi-Label Classification
Computer Network Information Center, Chinese Academy of Sciences, Beijing, China · University of Chinese Academy of Sciences, Beijing, China
DuConTE: Dual-Granularity Text Encoder with Topology-Constrained Attention for Text-attributed Graphs
Apr 19, 2026Lexuan Liang, Tao Zou, Xuxiang Ta +1Graph Attention Networks Transformer Attention
School of Computer Science and Engineering Beihang University Beijing, China 100191
Attention Enhanced Entity Recommendation for Intelligent Monitoring in Cloud Systems
Oct 23, 2025Fiza Husain, Anson Bastos, Anjaly Parayil +4Graph Attention Networks Heterogeneous GNNs
Microsoft
AttentionViG: Cross-Attention-Based Dynamic Neighbor Aggregation in Vision GNNs
Sep 29, 2025Hakan Emre Gedik, Andrew Martin, Mustafa Munir +4Graph Attention Networks Cross-Attention
The University of Texas at Austin
Physics-informed GNN for medium-high voltage AC power flow with edge-aware attention and line search correction operator
Sep 26, 2025Changhun Kim, Timon Conrad, Redwanul Karim +6Efficient Neural Network Inference Graph Attention Networks
Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany · Institute of Electrical Energy Systems, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany

May 8, 2026, cs.IRJ/K move · Enter open · S save

Multi-Level Graph Attention Network Contrastive Learning for Knowledge-Aware Recommendation

Zhifei Hu, Feng Xia

Durham University, Durham, United Kingdom · University of Sheffield, Sheffield, United Kingdom

Graph Attention Networks Multi-View Learning Recommender Systems Graph Contrastive Learning Knowledge Graphs

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In recent years, the use of edge information provided by knowledge graphs together with the advantages of higher-order connectivity in graph neural networks for recommendation systems has become an important research direction. However, existing approaches are often limited by sparse labels, insufficient graph structure learning, and noisy entities in the knowledge graph, which reduce recommendation accuracy. To address these limitations, we propose a multi-view graph contrastive learning framework. The proposed method enhances user representations through multi-view knowledge graph distillation, enabling more accurate modeling of user preferences over entities and relations. The network aggregates neighborhood entity information to construct informative item representations. Furthermore, we design a multi-level self-supervised contrastive learning module that performs comparisons across three perspectives: Inter-Level, Intra-Level, and Interaction-Level. This design improves the model's ability to generalize across intra-class samples while increasing discrimination between inter-class samples, thereby enabling more effective multi-dimensional feature modeling. We conduct extensive experiments on three public datasets using both baseline and ablation settings. Experimental results demonstrate that the proposed framework consistently outperforms existing state-of-the-art methods. Ablation studies further verify the effectiveness of each module in the proposed model.

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