Graph Representation Learning Beyond Node and Homophily
- URL: http://arxiv.org/abs/2203.01564v1
- Date: Thu, 3 Mar 2022 08:27:09 GMT
- Title: Graph Representation Learning Beyond Node and Homophily
- Authors: You Li, Bei Lin, Binli Luo, Ning Gui
- Abstract summary: This paper proposes PairE, a novel unsupervised graph embedding method using two paired nodes.
A multi-self-supervised autoencoder is designed to fulfill two pretext tasks: one retains the high-frequency signal better, and another enhances the representation of commonality.
Our experiments show that PairE outperforms the unsupervised state-of-the-art baselines.
- Score: 2.8417100723094357
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Unsupervised graph representation learning aims to distill various graph
information into a downstream task-agnostic dense vector embedding. However,
existing graph representation learning approaches are designed mainly under the
node homophily assumption: connected nodes tend to have similar labels and
optimize performance on node-centric downstream tasks. Their design is
apparently against the task-agnostic principle and generally suffers poor
performance in tasks, e.g., edge classification, that demands feature signals
beyond the node-view and homophily assumption. To condense different feature
signals into the embeddings, this paper proposes PairE, a novel unsupervised
graph embedding method using two paired nodes as the basic unit of embedding to
retain the high-frequency signals between nodes to support node-related and
edge-related tasks. Accordingly, a multi-self-supervised autoencoder is
designed to fulfill two pretext tasks: one retains the high-frequency signal
better, and another enhances the representation of commonality. Our extensive
experiments on a diversity of benchmark datasets clearly show that PairE
outperforms the unsupervised state-of-the-art baselines, with up to 101.1\%
relative improvement on the edge classification tasks that rely on both the
high and low-frequency signals in the pair and up to 82.5\% relative
performance gain on the node classification tasks.
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