2 citations · 6 across the 16 of their papers we have counts for
8 papers · 1 filter
GraphMAS: A Systematic Benchmark of Multi-Agent Coordination for Graph Learning
Jiayi Yang, Yifang Chen, Yuanfu Sun +2
LLM-based multi-agent systems coordinate specialized reasoning through aggregation, interaction, and adaptive control, yet their potential for graph learning remains unexplored. Gr…
One Model, Many Graphs: Learning over Attributed Graphs across Heterogeneous Modalities with Vision-Language Models
Jiayi Yang, Yifang Chen, Yuanfu Sun +2
Vision-language models (VLMs) provide a unified representation space for textual and visual information, yet their potential as general-purpose backbones for graph-structured data…
Graph-MLLM: Harnessing Multimodal Large Language Models for Multimodal Graph Learning
Jiajin Liu, Dongzhe Fan, Jiacheng Shen +3
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in representing and understanding diverse modalities. However, they typically focus on modality a…
GraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Design
Yuanfu Sun, Zhengnan Ma, Yi Fang +2
The growing importance of textual and relational systems has driven interest in enhancing large language models (LLMs) for graph-structured data, particularly Text-Attributed Graph…
GAugLLM: Improving Graph Contrastive Learning for Text-Attributed Graphs with Large Language Models
Yi Fang, Dongzhe Fan, Daochen Zha +1
This work studies self-supervised graph learning for text-attributed graphs (TAGs) where nodes are represented by textual attributes. Unlike traditional graph contrastive methods t…
GraphFM: A Comprehensive Benchmark for Graph Foundation Model
Yuhao Xu, Xinqi Liu, Keyu Duan +4
Foundation Models (FMs) serve as a general class for the development of artificial intelligence systems, offering broad potential for generalization across a spectrum of downstream…
E2GNN: Efficient Graph Neural Network Ensembles for Semi-Supervised Classification
Xin Zhang, Daochen Zha, Qiaoyu Tan
This work studies ensemble learning for graph neural networks (GNNs) under the popular semi-supervised setting. Ensemble learning has shown superiority in improving the accuracy an…
Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detection
Yuanchen Bei, Sheng Zhou, Qiaoyu Tan +4
Unsupervised graph anomaly detection is crucial for various practical applications as it aims to identify anomalies in a graph that exhibit rare patterns deviating significantly fr…