Local and global information preserved network embedding

Yao Ma; Suhang Wang; Jiliang Tang

doi:10.1109/ASONAM.2018.8508496

Local and global information preserved network embedding

Yao Ma, Suhang Wang, Jiliang Tang

College of Information Sciences and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Networks such as social networks, airplane networks, and citation networks are ubiquitous. To apply advanced machine learning algorithms to network data, low-dimensional and continuous representations are desired. To achieve this goal, many network embedding methods have been proposed recently. The majority of existing methods facilitate the local information i.e. local connections between nodes, to learn the representations, while neglecting global information (or node status), which has been proven to boost numerous network mining tasks such as link prediction and social recommendation. In this paper, we study the problem of preserving local and global information for network embedding. In particular, we introduce an approach to capture global information and propose a network embedding framework LOG, which can coherently model LOcal and Global information. Experiments demonstrate the effectiveness of the proposed framework.

Original language	English (US)
Title of host publication	Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
Editors	Andrea Tagarelli, Chandan Reddy, Ulrik Brandes
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	222-225
Number of pages	4
ISBN (Electronic)	9781538660515
DOIs	https://doi.org/10.1109/ASONAM.2018.8508496
State	Published - Oct 24 2018
Event	10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018 - Barcelona, Spain Duration: Aug 28 2018 → Aug 31 2018

Publication series

Name	Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018

Conference

Conference	10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
Country	Spain
City	Barcelona
Period	8/28/18 → 8/31/18

All Science Journal Classification (ASJC) codes

Sociology and Political Science
Communication
Computer Networks and Communications
Information Systems and Management

Access to Document

10.1109/ASONAM.2018.8508496

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Ma, Y., Wang, S., & Tang, J. (2018). Local and global information preserved network embedding. In A. Tagarelli, C. Reddy, & U. Brandes (Eds.), Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018 (pp. 222-225). [8508496] (Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASONAM.2018.8508496

Ma, Yao ; Wang, Suhang ; Tang, Jiliang. / Local and global information preserved network embedding. Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018. editor / Andrea Tagarelli ; Chandan Reddy ; Ulrik Brandes. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 222-225 (Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018).

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title = "Local and global information preserved network embedding",

abstract = "Networks such as social networks, airplane networks, and citation networks are ubiquitous. To apply advanced machine learning algorithms to network data, low-dimensional and continuous representations are desired. To achieve this goal, many network embedding methods have been proposed recently. The majority of existing methods facilitate the local information i.e. local connections between nodes, to learn the representations, while neglecting global information (or node status), which has been proven to boost numerous network mining tasks such as link prediction and social recommendation. In this paper, we study the problem of preserving local and global information for network embedding. In particular, we introduce an approach to capture global information and propose a network embedding framework LOG, which can coherently model LOcal and Global information. Experiments demonstrate the effectiveness of the proposed framework.",

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year = "2018",

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booktitle = "Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018",

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note = "10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018 ; Conference date: 28-08-2018 Through 31-08-2018",

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Ma, Y, Wang, S & Tang, J 2018, Local and global information preserved network embedding. in A Tagarelli, C Reddy & U Brandes (eds), Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018., 8508496, Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018, Institute of Electrical and Electronics Engineers Inc., pp. 222-225, 10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018, Barcelona, Spain, 8/28/18. https://doi.org/10.1109/ASONAM.2018.8508496

Local and global information preserved network embedding. / Ma, Yao; Wang, Suhang; Tang, Jiliang.

Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018. ed. / Andrea Tagarelli; Chandan Reddy; Ulrik Brandes. Institute of Electrical and Electronics Engineers Inc., 2018. p. 222-225 8508496 (Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - Networks such as social networks, airplane networks, and citation networks are ubiquitous. To apply advanced machine learning algorithms to network data, low-dimensional and continuous representations are desired. To achieve this goal, many network embedding methods have been proposed recently. The majority of existing methods facilitate the local information i.e. local connections between nodes, to learn the representations, while neglecting global information (or node status), which has been proven to boost numerous network mining tasks such as link prediction and social recommendation. In this paper, we study the problem of preserving local and global information for network embedding. In particular, we introduce an approach to capture global information and propose a network embedding framework LOG, which can coherently model LOcal and Global information. Experiments demonstrate the effectiveness of the proposed framework.

AB - Networks such as social networks, airplane networks, and citation networks are ubiquitous. To apply advanced machine learning algorithms to network data, low-dimensional and continuous representations are desired. To achieve this goal, many network embedding methods have been proposed recently. The majority of existing methods facilitate the local information i.e. local connections between nodes, to learn the representations, while neglecting global information (or node status), which has been proven to boost numerous network mining tasks such as link prediction and social recommendation. In this paper, we study the problem of preserving local and global information for network embedding. In particular, we introduce an approach to capture global information and propose a network embedding framework LOG, which can coherently model LOcal and Global information. Experiments demonstrate the effectiveness of the proposed framework.

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Ma Y, Wang S, Tang J. Local and global information preserved network embedding. In Tagarelli A, Reddy C, Brandes U, editors, Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 222-225. 8508496. (Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018). https://doi.org/10.1109/ASONAM.2018.8508496