Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Lei, Runlin"'
Autor:
Ji, Jiarui, Lei, Runlin, Bi, Jialing, Wei, Zhewei, Lin, Yankai, Pan, Xuchen, Li, Yaliang, Ding, Bolin
Graph generation is a fundamental task that has been extensively studied in social, technological, and scientific analysis. For modeling the dynamic graph evolution process, traditional rule-based methods struggle to capture community structures with
Externí odkaz:
http://arxiv.org/abs/2410.09824
Recent research has explored the use of Large Language Models (LLMs) for tackling complex graph reasoning tasks. However, due to the intricacies of graph structures and the inherent limitations of LLMs in handling long text, current approaches often
Externí odkaz:
http://arxiv.org/abs/2410.05130
The drastic performance degradation of Graph Neural Networks (GNNs) as the depth of the graph propagation layers exceeds 8-10 is widely attributed to a phenomenon of Over-smoothing. Although recent research suggests that Over-smoothing may not be the
Externí odkaz:
http://arxiv.org/abs/2408.03669
Graph Neural Networks (GNNs) excel across various applications but remain vulnerable to adversarial attacks, particularly Graph Injection Attacks (GIAs), which inject malicious nodes into the original graph and pose realistic threats. Text-attributed
Externí odkaz:
http://arxiv.org/abs/2405.16405
Graph Neural Networks (GNNs) have received extensive research attention for their promising performance in graph machine learning. Despite their extraordinary predictive accuracy, existing approaches, such as GCN and GPRGNN, are not robust in the fac
Externí odkaz:
http://arxiv.org/abs/2205.13892