English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Detecting ever-evolving social bots has become increasingly challenging.Advanced bots tend to interact more with humans as a camouflage to evadedetection. While graph-based detection methods can exploit various relations insocial networks to model node behaviors, the aggregated information fromneighbors largely ignore the inherent heterophily, i.e., the connectionsbetween different classes of accounts. Message passing mechanism onheterophilic edges can lead to feature mixture between bots and normal users,resulting in more false negatives. In this paper, we present BotSCL, aheterophily-aware contrastive learning framework that can adaptivelydifferentiate neighbor representations of heterophilic relations whileassimilating the representations of homophilic neighbors. Specifically, weemploy two graph augmentation methods to generate different graph views anddesign a channel-wise and attention-free encoder to overcome the limitation ofneighbor information summing. Supervised contrastive learning is used to guidethe encoder to aggregate class-specific information. Extensive experiments ontwo social bot detection benchmarks demonstrate that BotSCL outperformsbaseline approaches including the state-of-the-art bot detection approaches,partially heterophilic GNNs and self-supervised contrast learning methods.

Heterophily-aware Social Bot Detection with Supervised Contrastive Learning