Alleviating neighbor bias: augmenting graph self-supervise learning with
structural equivalent positive samples
- URL: http://arxiv.org/abs/2212.04365v1
- Date: Thu, 8 Dec 2022 16:04:06 GMT
- Title: Alleviating neighbor bias: augmenting graph self-supervise learning with
structural equivalent positive samples
- Authors: Jiawei Zhu, Mei Hong, Ronghua Du, Haifeng Li
- Abstract summary: We propose a signal-driven self-supervised method for graph representation learning.
It uses a topological information-guided structural equivalence sampling strategy.
The results show that the model performance can be effectively improved.
- Score: 1.0507062889290775
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In recent years, using a self-supervised learning framework to learn the
general characteristics of graphs has been considered a promising paradigm for
graph representation learning. The core of self-supervised learning strategies
for graph neural networks lies in constructing suitable positive sample
selection strategies. However, existing GNNs typically aggregate information
from neighboring nodes to update node representations, leading to an
over-reliance on neighboring positive samples, i.e., homophilous samples; while
ignoring long-range positive samples, i.e., positive samples that are far apart
on the graph but structurally equivalent samples, a problem we call "neighbor
bias." This neighbor bias can reduce the generalization performance of GNNs. In
this paper, we argue that the generalization properties of GNNs should be
determined by combining homogeneous samples and structurally equivalent
samples, which we call the "GC combination hypothesis." Therefore, we propose a
topological signal-driven self-supervised method. It uses a topological
information-guided structural equivalence sampling strategy. First, we extract
multiscale topological features using persistent homology. Then we compute the
structural equivalence of node pairs based on their topological features. In
particular, we design a topological loss function to pull in non-neighboring
node pairs with high structural equivalence in the representation space to
alleviate neighbor bias. Finally, we use the joint training mechanism to adjust
the effect of structural equivalence on the model to fit datasets with
different characteristics. We conducted experiments on the node classification
task across seven graph datasets. The results show that the model performance
can be effectively improved using a strategy of topological signal enhancement.
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