Relational Self-Supervised Learning on Graphs
- URL: http://arxiv.org/abs/2208.10493v1
- Date: Sun, 21 Aug 2022 12:33:16 GMT
- Title: Relational Self-Supervised Learning on Graphs
- Authors: Namkyeong Lee, Dongmin Hyun, Junseok Lee, Chanyoung Park
- Abstract summary: Graph representation learning (GRL) has been a powerful strategy for analyzing graph-structured data.
We propose a novel GRL method, called RGRL, that learns from the relational information generated from the graph itself.
By considering the relationship among nodes in both global and local perspectives, RGRL overcomes limitations of previous contrastive and non-contrastive methods.
- Score: 6.891327852064418
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Over the past few years, graph representation learning (GRL) has been a
powerful strategy for analyzing graph-structured data. Recently, GRL methods
have shown promising results by adopting self-supervised learning methods
developed for learning representations of images. Despite their success,
existing GRL methods tend to overlook an inherent distinction between images
and graphs, i.e., images are assumed to be independently and identically
distributed, whereas graphs exhibit relational information among data
instances, i.e., nodes. To fully benefit from the relational information
inherent in the graph-structured data, we propose a novel GRL method, called
RGRL, that learns from the relational information generated from the graph
itself. RGRL learns node representations such that the relationship among nodes
is invariant to augmentations, i.e., augmentation-invariant relationship, which
allows the node representations to vary as long as the relationship among the
nodes is preserved. By considering the relationship among nodes in both global
and local perspectives, RGRL overcomes limitations of previous contrastive and
non-contrastive methods, and achieves the best of both worlds. Extensive
experiments on fourteen benchmark datasets over various downstream tasks
demonstrate the superiority of RGRL over state-of-the-art baselines. The source
code for RGRL is available at https://github.com/Namkyeong/RGRL.
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