NDLI: SENA: Preserving Social Structure for Network Embedding

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SENA: Preserving Social Structure for Network Embedding

Content Provider	ACM Digital Library
Author	Park, Noseong Chakraborty, Tanmoy Hong, Sanghyun Husari, Ghaith Ahn, Sungjin
Abstract	Network embedding transforms a network into a continuous feature space. Network augmentation, on the other hand, leverages this feature representation to obtain a more informative network by adding potentially plausible edges while removing noisy edges. Traditional network embedding methods are often inefficient in capturing - (i) the latent relationship when the network is sparse (the network sparsity problem), and (ii) the local and global neighborhood structure of vertices (structure preserving problem). We propose SENA, a structural embedding and network augmentation framework for social network analysis. Unlike other embedding methods which only generate vertex features, SENA generates features for both vertices and relations (edges) by minimizing a well-designed objective function composed of a loss function and a regularization. The loss function reduces the network-sparsity problem by learning from both the edges present (true edges) and absent (false edges) in the network; whereas the regularization term preserves the structural properties of the network by efficiently considering - (i) the local neighborhood of vertices and edges, and (ii) the network spectra, i.e., eigenvectors of a symmetric matrix representing the network. We compare SENA with four baseline network embedding methods, namely DeepWalk, SE, SME and TransE. We demonstrate the efficacy of SENA through a task-based evaluation setting on different real-world networks. We consider the state-of-the-art algorithms for (i) community detection, (ii) link prediction and (iii) knowledge graph query answering, and show that with SENA's representation, these algorithms achieve up to 10%, 9% and (surprisingly) 108% higher accuracy respectively compared to the best baseline embedding methods.
Starting Page	235
Ending Page	244
Page Count	10
File Format	PDF
ISBN	9781450347082
DOI	10.1145/3078714.3078738
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-07-04
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Link prediction Community detection Network embedding
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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