Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: GCCAD: Graph Contrastive Coding for Anomaly Detection   Graph-based anomaly detection has been widely used for detecting malicious
activities in real-world applications. Existing attempts to address this
problem have thus far focused on structural feature engineering or learning in
the binary classification regime. In this work, we propose to leverage graph
contrastive coding and present the supervised GCCAD model for contrasting
abnormal nodes with normal ones in terms of their distances to the global
context (e.g., the average of all nodes). To handle scenarios with scarce
labels, we further enable GCCAD as a self-supervised framework by designing a
graph corrupting strategy for generating synthetic node labels. To achieve the
contrastive objective, we design a graph neural network encoder that can infer
and further remove suspicious links during message passing, as well as learn
the global context of the input graph. We conduct extensive experiments on four
public datasets, demonstrating that 1) GCCAD significantly and consistently
outperforms various advanced baselines and 2) its self-supervised version
without fine-tuning can achieve comparable performance with its fully
supervised version.

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The categories of this paper are: cmp-lg
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The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
7
The categories of this paper are: hep-th
2
The categories of this paper are: math
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The categories of this paper are: bayes-an
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-th
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The categories of this paper are: solv-int
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The categories of this paper are: hep-ex
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The categories of this paper are: gr-qc
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The categories of this paper are: alg-geom
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The categories of this paper are: nucl-ex
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The categories of this paper are: ao-sci