arXiv:2607. 05952v1 Announce Type: cross Abstract: While signed social recommendation has shown great potential by modeling both trust and distrust relations, its effectiveness is often hindered by structural noise and data sparsity.
By Zifan Wang, Siyu Chen, Wenzhuo Song
arXiv:2607. 11212v1 Announce Type: new Abstract: Relational fraud detection can exploit both label-free graph context and label-derived neighborhood evidence, but these two information sources obey different validity conditions.
By Liming Liu, Chao Hu, Mingfei Lu, Yiwei Ge, Xingle Li, Heyuan Shi
arXiv:2608. 04377v1 Announce Type: cross Abstract: Hypergraph neural networks (HGNNs) have demonstrated remarkable capabilities in processing complex higher-order relationships.
By Mengyao Zhou, Zhiheng Zhou, Xiao Han, Guiying Yan
arXiv:2608. 02399v1 Announce Type: cross Abstract: Content-based detection of unreliable news is increasingly difficult, as low-reliability sources mimic credible journalism and generative AI makes fabricated content harder to flag.
By Raphaela Ke{\ss}ler, Roman David Ventzke, Viola Priesemann, Giordano De Marzo
arXiv:2607. 07716v1 Announce Type: cross Abstract: Temporal graphs are ubiquitous in real-world applications and Temporal Graph Networks (TGNs) have achieved superior predictive accuracy.
By Yazheng Liu, Xi Zhang, Sihong Xie, Hui Xiong
arXiv:2510. 09416v4 Announce Type: replace Abstract: Learning on temporal graphs has become a central topic in graph representation learning, with numerous benchmarks indicating the strong performance of state-of-the-art models.
By Abigail J. Hayes, Tobias Schumacher, Markus Strohmaier