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 | |
| 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 |
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All Science Journal Classification (ASJC) codes
- Sociology and Political Science
- Communication
- Computer Networks and Communications
- Information Systems and Management
Cite this
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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
TY - GEN
T1 - Local and global information preserved network embedding
AU - Ma, Yao
AU - Wang, Suhang
AU - Tang, Jiliang
PY - 2018/10/24
Y1 - 2018/10/24
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.
UR - http://www.scopus.com/inward/record.url?scp=85057299842&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85057299842&partnerID=8YFLogxK
U2 - 10.1109/ASONAM.2018.8508496
DO - 10.1109/ASONAM.2018.8508496
M3 - Conference contribution
AN - SCOPUS:85057299842
T3 - Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
SP - 222
EP - 225
BT - Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
A2 - Tagarelli, Andrea
A2 - Reddy, Chandan
A2 - Brandes, Ulrik
PB - Institute of Electrical and Electronics Engineers Inc.
ER -