Graph Auto-Encoders for Network Completion
- URL: http://arxiv.org/abs/2204.11852v1
- Date: Mon, 25 Apr 2022 05:24:45 GMT
- Title: Graph Auto-Encoders for Network Completion
- Authors: Zhang Zhang, Ruyi Tao, Yongzai Tao, Jiang Zhang
- Abstract summary: We propose a model to use the learned pattern of connections from the observed part of the network to complete the whole graph.
Our proposed model achieved competitive performance with less information needed.
- Score: 6.1074304332419675
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Completing a graph means inferring the missing nodes and edges from a
partially observed network. Different methods have been proposed to solve this
problem, but none of them employed the pattern similarity of parts of the
graph. In this paper, we propose a model to use the learned pattern of
connections from the observed part of the network based on the Graph
Auto-Encoder technique and generalize these patterns to complete the whole
graph. Our proposed model achieved competitive performance with less
information needed. Empirical analysis of synthetic datasets and real-world
datasets from different domains show that our model can complete the network
with higher accuracy compared with baseline prediction models in most cases.
Furthermore, we also studied the character of the model and found it is
particularly suitable to complete a network that has more complex local
connection patterns.
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