HIER: Heterogeneous Information Bottleneck and Expert Routing for Social Bot Detection

Social bots constitute a substantial fraction of active accounts on digital platforms, fundamentally threatening information authenticity and democratic discourse. Contemporary detection methods confront critical limitations: information imbalance across heterogeneous relations, computational challenges in processing massive neighborhoods, and inadequate multi-scale representation learning. We propose HIER (Heterogeneous Information Bottleneck and Expert Routing), a pioneering framework that integrates variational information theory with mixture-of-experts paradigms for social network analysis. HIER introduces relation-aware variational information bottleneck for optimal compression across relationship types, dynamic sparse expert routing that extends mixture-of-experts to edge-level graph processing, and dual-scale mutual information maximization enhancing representation discriminability through neighborhood consistency and graph-level contrastive learning. Experimental validation demonstrates HIER's superior performance across real-world datasets, establishing new benchmarks for heterogeneous social bot detection.