Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SCGC : Self-Supervised Contrastive Graph Clustering   Graph clustering discovers groups or communities within networks. Deep
learning methods such as autoencoders (AE) extract effective clustering and
downstream representations but cannot incorporate rich structural information.
While Graph Neural Networks (GNN) have shown great success in encoding graph
structure, typical GNNs based on convolution or attention variants suffer from
over-smoothing, noise, heterophily, are computationally expensive and typically
require the complete graph being present. Instead, we propose Self-Supervised
Contrastive Graph Clustering (SCGC), which imposes graph-structure via
contrastive loss signals to learn discriminative node representations and
iteratively refined soft cluster labels. We also propose SCGC*, with a more
effective, novel, Influence Augmented Contrastive (IAC) loss to fuse richer
structural information, and half the original model parameters. SCGC(*) is
faster with simple linear units, completely eliminate convolutions and
attention of traditional GNNs, yet efficiently incorporates structure. It is
impervious to layer depth and robust to over-smoothing, incorrect edges and
heterophily. It is scalable by batching, a limitation in many prior GNN models,
and trivially parallelizable. We obtain significant improvements over
state-of-the-art on a wide range of benchmark graph datasets, including images,
sensor data, text, and citation networks efficiently. Specifically, 20% on ARI
and 18% on NMI for DBLP; overall 55% reduction in training time and overall,
81% reduction on inference time. Our code is available at :
https://github.com/gayanku/SCGC

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The categories of this paper are: q-fin
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The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
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The categories of this paper are: chem-ph
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The categories of this paper are: nlin
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-alg
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The categories of this paper are: econ
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The categories of this paper are: dg-ga
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The categories of this paper are: funct-an
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The categories of this paper are: stat
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The categories of this paper are: comp-gas
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The categories of this paper are: eess