Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Tackling Oversmoothing of GNNs with Contrastive Learning   Graph neural networks (GNNs) integrate the comprehensive relation of graph
data and the representation learning capability of neural networks, which is
one of the most popular deep learning methods and achieves state-of-the-art
performance in many applications, such as natural language processing and
computer vision. In real-world scenarios, increasing the depth (i.e., the
number of layers) of GNNs is sometimes necessary to capture more latent
knowledge of the input data to mitigate the uncertainty caused by missing
values. However, involving more complex structures and more parameters will
decrease the performance of GNN models. One reason called oversmoothing is
recently introduced but the relevant research remains nascent. In general,
oversmoothing makes the final representations of nodes indiscriminative, thus
deteriorating the node classification and link prediction performance. In this
paper, we first survey the current de-oversmoothing methods and propose three
major metrics to evaluate a de-oversmoothing method, i.e., constant divergence
indicator, easy-to-determine divergence indicator, and model-agnostic strategy.
Then, we propose the Topology-guided Graph Contrastive Layer, named TGCL, which
is the first de-oversmoothing method maintaining all three mentioned metrics.
With the contrastive learning manner, we provide the theoretical analysis of
the effectiveness of the proposed TGCL. Last but not least, we design extensive
experiments to illustrate the empirical performance of TGCL comparing with
state-of-the-art baselines.

7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg