HeTGB is a new benchmark for heterophilic text‑attributed graphs, consisting of five real‑world datasets where nodes have rich textual descriptions. It allows systematic evaluation of graph neural networks, pre‑trained language models, and co‑training methods on node classification. The benchmark highlights the utility of text attributes, the challenges of heterophilic TAGs, and the limitations of current models.
By Shujie Li, Yuxia Wu, Yuan Fang, Chuan Shi
arXiv:2609.36902v1 Announce Type: new
Abstract: Existing multimodal fake news detection methods often introduce external information to assist detection. However, most of them rely on entity-level re...
By Wenbin Shen, Guoxuan Qin, Guangxu Yao, Baodong Wang, Yuanbo Rui, Zhongjie Ba, Zhichao Lian
arXiv:2607. 11894v1 Announce Type: cross Abstract: Detecting disinformation narratives on social media is challenging due to the scale of amplification, rapid evolution, and linguistic variability of online content.
By Yuliia Vistak, Viktoriia Makovska, Vera Schmitt, Veronika Solopova
The paper reinterprets graph neural networks (GNNs) as retrieval-augmented models, where each layer uses an MLP on a node representation and a permutation‑invariant summary of retrieved graph context instead of traditional message passing. It introduces RTA, a lightweight MLP‑based framework that replaces structural message passing with label‑aware retrieval and propagation, and provides theoretical links to softmax‑attention message passing and robustness to mis‑retrieved outliers. Experiments on text‑attributed graph benchmarks demonstrate that RTA matches or surpasses strong GNN and graph LLM baselines while improving efficiency and robustness.
By Jintang Li, Yuhong Chen, Ruofan Wu, Binli Luo, Jiayi Ji, Hui Li, Rongrong Ji
arXiv:2609.36850v1 Announce Type: new
Abstract: Generative content is increasingly entering the production and dissemination of news, transforming fake news from manually fabricated or simply manipul...
By Wenbin Shen, Guoxuan Qin, Guangxu Yao, Baodong Wang, Yuanbo Rui, Zhichao Lian
The paper introduces the Echo Chamber Effect, a failure mode in Graph Neural Networks where intra-community representations collapse while inter-community separation remains, differing from traditional oversmoothing. It proposes the Echo Chamber Index (ECI) to detect this effect by stratifying pairwise distances by community membership. Building on this analysis, the authors present Community-Aware Split Propagation (CASP), a lightweight plugin that decouples intra- and inter-community aggregation and learns their balance from label structure, improving performance across various GNN backbones in both homophilic and heterophilic settings.
By Asela Hevapathige, Ahad N. Zehmakan, Asiri Wijesinghe, Saman Halgamuge