Position-Aware Subgraph Neural Networks with Data-Efficient Learning
- URL: http://arxiv.org/abs/2211.00572v1
- Date: Tue, 1 Nov 2022 16:34:42 GMT
- Title: Position-Aware Subgraph Neural Networks with Data-Efficient Learning
- Authors: Chang Liu, Yuwen Yang, Zhe Xie, Hongtao Lu, Yue Ding
- Abstract summary: We propose a Position-Aware Data-Efficient Learning framework for subgraph neural networks called PADEL.
Specifically, we propose a novel node position encoding method that is anchor-free, and design a new generative subgraph augmentation method based on a diffused variational subgraph autoencoder.
- Score: 15.58680146160525
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Data-efficient learning on graphs (GEL) is essential in real-world
applications. Existing GEL methods focus on learning useful representations for
nodes, edges, or entire graphs with ``small'' labeled data. But the problem of
data-efficient learning for subgraph prediction has not been explored. The
challenges of this problem lie in the following aspects: 1) It is crucial for
subgraphs to learn positional features to acquire structural information in the
base graph in which they exist. Although the existing subgraph neural network
method is capable of learning disentangled position encodings, the overall
computational complexity is very high. 2) Prevailing graph augmentation methods
for GEL, including rule-based, sample-based, adaptive, and automated methods,
are not suitable for augmenting subgraphs because a subgraph contains fewer
nodes but richer information such as position, neighbor, and structure.
Subgraph augmentation is more susceptible to undesirable perturbations. 3) Only
a small number of nodes in the base graph are contained in subgraphs, which
leads to a potential ``bias'' problem that the subgraph representation learning
is dominated by these ``hot'' nodes. By contrast, the remaining nodes fail to
be fully learned, which reduces the generalization ability of subgraph
representation learning. In this paper, we aim to address the challenges above
and propose a Position-Aware Data-Efficient Learning framework for subgraph
neural networks called PADEL. Specifically, we propose a novel node position
encoding method that is anchor-free, and design a new generative subgraph
augmentation method based on a diffused variational subgraph autoencoder, and
we propose exploratory and exploitable views for subgraph contrastive learning.
Extensive experiment results on three real-world datasets show the superiority
of our proposed method over state-of-the-art baselines.
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