The rise of social bots poses a significant threat to online platforms, making their detection an urgent priority.Recent advancements in Graph Neural Networks (GNNs) have significantly improved bot detection by leveraging the rich relational data within social networks.However, existing approaches face two key challenges: imbalanced feature fusion across different modalities and edge heterophily, which limit their effectiveness.To address these issues, we propose BotBR, a novel bot detection framework.BotBR tackles feature imbalance by employing decision trees to extract behavioral patterns from user-related numerical features and utilizes an attention mechanism to seamlessly integrate multi-modal feature embeddings.To mitigate edge heterophily, BotBR incorporates an edge detector to differentiate between high-and low-reliability edges, optimizing the utilization of structural graph information.Furthermore, a homophily-based graph is introduced for consistency contrastive learning, enhancing the model's robustness.Experimental results on real-world bot detection benchmark datasets demonstrate that BotBR achieves state-of-the-art performance while maintaining efficiency comparable to classical methods.

Predicting information diffusion is a fundamental task in online social networks (OSNs).Recent studies mainly focus on the popularity prediction of specific content but ignore the correlation between multiple pieces of information.The topic is often used to correlate such information and can correspond to multi-source information.The popularity of a topic relies not only on information diffusion time but also on users' followership.Current solutions concentrate on hard time partition, lacking versatility.Meanwhile, the hop-based sampling adopted in state-of-the-art (SOTA) methods encounters redundant user followership.Moreover, many SOTA methods are not designed with good modularity and lack evaluation for each functional module and enlightening discussion.This paper presents a novel extensible framework, coined as HIF, for effective popularity prediction in OSNs with four original contributions.First, HIF adopts a soft partition of users and time intervals to better learn users' behavioral preferences over time.Second, HIF utilizes weighted sampling to optimize the construction of heterogeneous graphs and reduce redundancy.Furthermore, HIF supports multi-task collaborative optimization to improve its learning capability.Finally, as an extensible framework, HIF provides generic module slots to combine different submodules (e.g., RNNs,

Popularity prediction is to predict the number of social network users involved in information diffusion. Recently, deep learning methods for popularity prediction advance traditional approaches that rely on hand-crafted features. However, existing approaches ignore the multi-source cascade that consists of multiple sub-cascades with different content but under the same topic. Different from single-source cascade, more cascading information can be observed from multi-source cascade and they are potentially correlated. How to correlate the diverse information and take advantage of them from both temporal and spatial aspects is critical for prediction. To this end, we propose a novel framework, called HEterogeneous Recurrent Integrated Graph Convolutional Neural Network (HERI-GCN). Specifically, we construct a heterogeneous cascade graph to model the multi-source cascade where time intervals are treated as heterogeneous time nodes. Besides, we propose a heterogeneous GCN to learn rich features from the multi-source cascade. RNN is organically integrated into the heterogeneous GCN to overcome the limited learning ability toward temporal and spatial data. We evaluate HERI-GCN through comparative experiments on three datasets. The experimental evaluation shows that HERI-GCN outperforms the state-of-the-art baseline methods.