DINE: A Framework for Deep Incomplete Network Embedding

• URL: http://arxiv.org/abs/2008.06311v1
• Date: Sun, 9 Aug 2020 04:59:35 GMT
• Title: DINE: A Framework for Deep Incomplete Network Embedding
• Authors: Ke Hou, Jiaying Liu, Yin Peng, Bo Xu, Ivan Lee, Feng Xia
• Abstract summary: We propose a Deep Incomplete Network Embedding method, namely DINE. We first complete the missing part including both nodes and edges in a partially observable network by using the expectation-maximization framework. We evaluate DINE over three networks on multi-label classification and link prediction tasks.
• Score: 33.97952453310253
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Network representation learning (NRL) plays a vital role in a variety of tasks such as node classification and link prediction. It aims to learn low-dimensional vector representations for nodes based on network structures or node attributes. While embedding techniques on complete networks have been intensively studied, in real-world applications, it is still a challenging task to collect complete networks. To bridge the gap, in this paper, we propose a Deep Incomplete Network Embedding method, namely DINE. Specifically, we first complete the missing part including both nodes and edges in a partially observable network by using the expectation-maximization framework. To improve the embedding performance, we consider both network structures and node attributes to learn node representations. Empirically, we evaluate DINE over three networks on multi-label classification and link prediction tasks. The results demonstrate the superiority of our proposed approach compared against state-of-the-art baselines.

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