Year: 2017
Type: preprint
Abstract: Learning distributed node representations in networks has been attracting increasing attention recently due to its effectiveness in a variety of applications. Existing approaches usually study networks with a single type of proximity between nodes, which defines a single view of a network. However, in reality there usually exists multiple types of proximities between nodes, yielding networks with multiple views. This paper studies learning node representations for networks with multiple views, which aims to infer robust node representations across different views. We propose a multi-view representation learning approach, which promotes the collaboration of different views and lets them vote for the robust representations. During the voting process, an attention mechanism is introduced, which enables each node to focus on the most informative views. Experimental results on real-world networks show that the proposed approach outperforms existing state-of-the-art approaches for network representation learning with a single view and other competitive approaches with multiple views.
Source: arXiv (Cornell University)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G1346049954, III: Small: Collaborative Research: Conflicts to Harmony: Integrating Massive Data by Trustworthiness Estimation and Truth Discovery, III: Small: Multi-Dimensional Structuring, Summarizing and Mining of Social Media Data, 大规模在线课程中用户流失问题的研究, G5259331294, G6896362766, 知识图谱辅助的垂直领域自动人机对话系统框架研究, G7561134949, G9000120328, G948678646
OpenAlex ID: W2758554391