Portable graph-based rumour detection against multi-modal heterophily

The propagation of rumours on social media poses an important threat to societies, so that various techniques for graph-based rumour detection have been proposed recently. Existing works, however, are based on homophilic graphs: entities that are connected to each other often have the same label. However, recent studies found that heterophily is more common in real-world social networks, i.e., entities with different labels are also often linked to each other due to 'innocent' retweets or camouflage behaviours by malicious users. Especially, the heterophily problem is even more challenging in multi-modal social graphs, in which neighbouring entities might differ in terms of both labels and modalities. To cope with multi-modal homophily in graph-based rumour detection, we propose a Portable Graph Transformer-based Rumour Detection model (PHAROS) with novel multi-modal homophily measures. It integrates label information in the learning process, which enables us to generate discriminative neighbourhoods of entities. Our model can handle multiple modalities (a natural characteristic of social graphs) and is portable to be combined with existing graph-based models. Extensive experiments on real and synthetic data show the superiority, efficiency, robustness, and portability of PHAROS and its heterophily resilience.