Graph Communal Contrastive Learning
- URL: http://arxiv.org/abs/2110.14863v1
- Date: Thu, 28 Oct 2021 02:57:54 GMT
- Title: Graph Communal Contrastive Learning
- Authors: Bolian Li, Baoyu Jing and Hanghang Tong
- Abstract summary: A fundamental problem for graph representation learning is how to effectively learn representations without human labeling.
We propose a novel Graph Contrastive Learning (gCooL) framework to jointly learn the community partition and learn node representations.
- Score: 34.85906025283825
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Graph representation learning is crucial for many real-world applications
(e.g. social relation analysis). A fundamental problem for graph representation
learning is how to effectively learn representations without human labeling,
which is usually costly and time-consuming. Graph contrastive learning (GCL)
addresses this problem by pulling the positive node pairs (or similar nodes)
closer while pushing the negative node pairs (or dissimilar nodes) apart in the
representation space. Despite the success of the existing GCL methods, they
primarily sample node pairs based on the node-level proximity yet the community
structures have rarely been taken into consideration. As a result, two nodes
from the same community might be sampled as a negative pair. We argue that the
community information should be considered to identify node pairs in the same
communities, where the nodes insides are semantically similar. To address this
issue, we propose a novel Graph Communal Contrastive Learning (gCooL) framework
to jointly learn the community partition and learn node representations in an
end-to-end fashion. Specifically, the proposed gCooL consists of two
components: a Dense Community Aggregation (DeCA) algorithm for community
detection and a Reweighted Self-supervised Cross-contrastive (ReSC) training
scheme to utilize the community information. Additionally, the real-world
graphs are complex and often consist of multiple views. In this paper, we
demonstrate that the proposed gCooL can also be naturally adapted to multiplex
graphs. Finally, we comprehensively evaluate the proposed gCooL on a variety of
real-world graphs. The experimental results show that the gCooL outperforms the
state-of-the-art methods.
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