Community-Enhanced Dynamic Graph Convolutional Networks for Rumor Detection on Social Networks

Along with the increasing popularization of social platforms, rumors in the Web environment have become one of the significant threats to human society. Existing rumor detection methods ignore modeling and analyzing the community structure of the rumor propagation network. This article proposes a new community-enhanced dynamic graph convolutional network (CDGCN) for effective rumor detection on online social networks, which utilize the communities formed in a rumor propagation process to improve rumor detection accuracy. CDGCN uses a designed method that combines node features and topology features to identify the communities and learn the community features of rumors. Following this, a graph convolutional network (GCN) with a community-aware attention mechanism is proposed to enable the nodes to dynamically aggregate information from their neighboring nodes’ global and community features, effectively prioritizing critical neighborhood information, enhancing the representation of both local community structures and global network patterns for improved analytical performance. The final rumor representations generated by the GCN are processed by a classifier to detect false rumors. Comprehensive experiments and comparison studies are conducted on four real-world datasets to validate the effectiveness of CDGCN.