  Recent advancements in social bot detection have been driven by the adoption
of Graph Neural Networks. The social graph, constructed from social network
interactions, contains benign and bot accounts that influence each other.
However, previous graph-based detection methods that follow the transductive
message-passing paradigm may not fully utilize hidden graph information and are
vulnerable to adversarial bot behavior. The indiscriminate message passing
between nodes from different categories and communities results in excessively
homogeneous node representations, ultimately reducing the effectiveness of
social bot detectors. In this paper, we propose SEBot, a novel multi-view
graph-based contrastive learning-enabled social bot detector. In particular, we
use structural entropy as an uncertainty metric to optimize the entire graph's
structure and subgraph-level granularity, revealing the implicitly existing
hierarchical community structure. And we design an encoder to enable message
passing beyond the homophily assumption, enhancing robustness to adversarial
behaviors of social bots. Finally, we employ multi-view contrastive learning to
maximize mutual information between different views and enhance the detection
performance through multi-task learning. Experimental results demonstrate that
our approach significantly improves the performance of social bot detection
compared with SOTA methods.
