Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: BotRGCN: Twitter Bot Detection with Relational Graph Convolutional
  Networks   Twitter bot detection is an important and challenging task. Existing bot
detection measures fail to address the challenge of community and disguise,
falling short of detecting bots that disguise as genuine users and attack
collectively. To address these two challenges of Twitter bot detection, we
propose BotRGCN, which is short for Bot detection with Relational Graph
Convolutional Networks. BotRGCN addresses the challenge of community by
constructing a heterogeneous graph from follow relationships and applies
relational graph convolutional networks. Apart from that, BotRGCN makes use of
multi-modal user semantic and property information to avoid feature engineering
and augment its ability to capture bots with diversified disguise. Extensive
experiments demonstrate that BotRGCN outperforms competitive baselines on a
comprehensive benchmark TwiBot-20 which provides follow relationships.

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The categories of this paper are: q-bio
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The categories of this paper are: hep-lat
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The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
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The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
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The categories of this paper are: gr-qc
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The categories of this paper are: cond-mat
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The categories of this paper are: chao-dyn
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The categories of this paper are: adap-org
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The categories of this paper are: patt-sol
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The categories of this paper are: cmp-lg
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The categories of this paper are: supr-con
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The categories of this paper are: chem-ph
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The categories of this paper are: physics