Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Graph Fairing Convolutional Networks for Anomaly Detection   Graph convolution is a fundamental building block for many deep neural
networks on graph-structured data. In this paper, we introduce a simple, yet
very effective graph convolutional network with skip connections for
semi-supervised anomaly detection. The proposed multi-layer network
architecture is theoretically motivated by the concept of implicit fairing in
geometry processing, and comprises a graph convolution module for aggregating
information from immediate node neighbors and a skip connection module for
combining layer-wise neighborhood representations. In addition to capturing
information from distant graph nodes through skip connections between the
network's layers, our approach exploits both the graph structure and node
features for learning discriminative node representations. The effectiveness of
our model is demonstrated through extensive experiments on five benchmark
datasets, achieving better or comparable anomaly detection results against
strong baseline methods.

14
The categories of this paper are: math-ph
34
The categories of this paper are: chem-ph
16
The categories of this paper are: stat
26
The categories of this paper are: bayes-an
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph