3 accepted papers
Graph generation is essential in social network analysis, particularly for modeling information flow and user interactions. However, existing probabilistic diffusion models face challenges when applied to social propagation graphs. The continuous noise does not apply to the discrete nature of graph
With the rise of social media, the spread of fake news has become a significant concern, potentially misleading public perceptions and impacting social stability. Although deep learning methods like CNNs, RNNs, and Transformer-based models like BERT have enhanced fake news detection. However, they p…
Different social media are widely used in our daily lives. Inspired by the fact that similar topics have similar propagation characteristics, we mine the implicit knowledge of cascades with similar topics from different platforms to enhance the localization performance for scenarios where limited pr…