- Title
- DINE : a framework for deep incomplete network embedding
- Creator
- Hou, Ke; Liu, Jiaying; Peng, Yin; Xu, Bo; Lee, Ivan; Xia, Feng
- Date
- 2019
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/171256
- Identifier
- vital:14276
- Identifier
-
https://doi.org/10.1007/978-3-030-35288-2_14
- Identifier
- ISBN:03029743; 9783030352875
- 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. © 2019, Springer Nature Switzerland AG.; E1
- Publisher
- Springer
- Relation
- 32nd Australasian Joint Conference on Artificial Intelligence, AI 2019 Vol. 11919 LNAI, p. 165-176
- Rights
- © Springer Nature Switzerland AG 2019
- Rights
- This metadata is freely available under a CCO license
- Subject
- Deep learning; Incomplete network embedding; Network completion; Network representation learning
- Full Text
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