EPINE: Enhanced Proximity Information Network Embedding

• URL: http://arxiv.org/abs/2003.02689v1
• Date: Wed, 4 Mar 2020 15:57:17 GMT
• Title: EPINE: Enhanced Proximity Information Network Embedding
• Authors: Luoyi Zhang, Ming Xu
• Abstract summary: In this work, we devote to mining valuable information in adjacency matrices at a deeper level. Under the same objective, many NE methods calculate high-order proximity by the powers of adjacency matrices. We propose to redefine high-order proximity in a more intuitive manner.
• Score: 2.257737378757467
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unsupervised homogeneous network embedding (NE) represents every vertex of networks into a low-dimensional vector and meanwhile preserves the network information. Adjacency matrices retain most of the network information, and directly charactrize the first-order proximity. In this work, we devote to mining valuable information in adjacency matrices at a deeper level. Under the same objective, many NE methods calculate high-order proximity by the powers of adjacency matrices, which is not accurate and well-designed enough. Instead, we propose to redefine high-order proximity in a more intuitive manner. Besides, we design a novel algorithm for calculation, which alleviates the scalability problem in the field of accurate calculation for high-order proximity. Comprehensive experiments on real-world network datasets demonstrate the effectiveness of our method in downstream machine learning tasks such as network reconstruction, link prediction and node classification.

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