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We have a full paper accepted by IJCAI 2019!

Update:2019-05-11 21:57:04  Click:

We are happy to announce that we have a full paper accepted by CCF-A conference IJCAI 2019!


17CF4

Yu Li


Yu Li is the first author of this paper. He is a 1st-year Ph.D. candidate at the School of AI, Jilin University. He was supervised by Prof. Yi Chang.


This work was done in collaboration with Dr. Jiawei Zhang. He is an Assistant Professor at the Department of Computer Science of Florida State University.


Paper Details


Title: Learning Network Embedding with Community Structural Information

Conference: International Joint Conference on Artificial Intelligence (IJCAI) 2019

Time & Venue: August 10-16, 2019, Macao, China

Abstract: Network embedding is an effective approach to learn the low-dimensional representations of vertices in networks, aiming to capture and preserve the structure and inherent properties of networks. The vast majority of existing network embedding methods exclusively focus on vertex proximity of networks, while ignoring the network internal community structure. However, the homophily principle indicates that vertices within the same community are more similar to each other than those from different communities, thus vertices within the same community should have similar vertex representations. Motivated by this, we propose a novel network embedding framework NECS to learn the Network Embedding with Community Structural information, which preserves the highorder proximity and incorporates the community structure in vertex representation learning. We formulate the problem into a principled optimization framework and provide an effective alternating algorithm to solve it. Extensive experimental results on several benchmark network datasets demonstrate the effectiveness of the proposed framework in various network analysis tasks including vertex classification, network reconstruction and link prediction.


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