3 papers
cs.IR2026
Edge-Aware Curvature Modeling for Graph Understanding in Large Language Models
Zhenghong Lin, Zhibin Shi, Hongyang Dong +3
Recently, graph-aware Large Language Models (LLMs) have shown promising capabilities in jointly modeling graph-structured data and textual information. Existing approaches typicall…
cs.CV2026
Multi-view Graph Convolutional Network with Fully Leveraging Consistency via Granular-ball-based Topology Construction, Feature Enhancement and Interactive Fusion
Chengjie Cui, Taihua Xu, Shuyin Xia +3
The effective utilization of consistency is crucial for multi-view learning. GCNs leverage node connections to propagate information across the graph, facilitating the exploitation…
cs.LG2025
Simplifying Graph Convolutional Networks with Redundancy-Free Neighbors
Jielong Lu, Zhihao Wu, Zhiling Cai +2
In recent years, Graph Convolutional Networks (GCNs) have gained popularity for their exceptional ability to process graph-structured data. Existing GCN-based approaches typically…