Graph Inference Learning for Semi-supervised Classification
- URL: http://arxiv.org/abs/2001.06137v1
- Date: Fri, 17 Jan 2020 02:52:30 GMT
- Title: Graph Inference Learning for Semi-supervised Classification
- Authors: Chunyan Xu, Zhen Cui, Xiaobin Hong, Tong Zhang, Jian Yang, and Wei Liu
- Abstract summary: We propose a Graph Inference Learning framework to boost the performance of semi-supervised node classification.
For learning the inference process, we introduce meta-optimization on structure relations from training nodes to validation nodes.
Comprehensive evaluations on four benchmark datasets demonstrate the superiority of our proposed GIL when compared against state-of-the-art methods.
- Score: 50.55765399527556
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this work, we address semi-supervised classification of graph data, where
the categories of those unlabeled nodes are inferred from labeled nodes as well
as graph structures. Recent works often solve this problem via advanced graph
convolution in a conventionally supervised manner, but the performance could
degrade significantly when labeled data is scarce. To this end, we propose a
Graph Inference Learning (GIL) framework to boost the performance of
semi-supervised node classification by learning the inference of node labels on
graph topology. To bridge the connection between two nodes, we formally define
a structure relation by encapsulating node attributes, between-node paths, and
local topological structures together, which can make the inference
conveniently deduced from one node to another node. For learning the inference
process, we further introduce meta-optimization on structure relations from
training nodes to validation nodes, such that the learnt graph inference
capability can be better self-adapted to testing nodes. Comprehensive
evaluations on four benchmark datasets (including Cora, Citeseer, Pubmed, and
NELL) demonstrate the superiority of our proposed GIL when compared against
state-of-the-art methods on the semi-supervised node classification task.
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