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Enhancing network embedding with implicit clustering
conference contribution
posted on 2023-05-23, 14:06 authored by Li, Q, Zhong, J, Cao, Z, Wang, CNetwork embedding aims at learning the low dimensional representation of nodes. These representations can be widely used for network mining tasks, such as link prediction, anomaly detection, and classification. Recently, a great deal of meaningful research work has been carried out on this emerging network analysis paradigm. The real-world network contains different size clusters because of the edges with different relationship types. These clusters also reflect some features of nodes, which can contribute to the optimization of the feature representation of nodes. However, existing network embedding methods do not distinguish these relationship types. In this paper, we propose an unsupervised network representation learning model that can encode edge relationship information. Firstly, an objective function is defined, which can learn the edge vectors by implicit clustering. Then, a biased random walk is designed to generate a series of node sequences, which are put into Skip-Gram to learn the low dimensional node representations. Extensive experiments are conducted on several network datasets. Compared with the state-of-art baselines, the proposed method is able to achieve favorable and stable results in multi-label classification and link prediction tasks.
History
Publication title
Proceedings of the 24th International Conference, DASFAA 2019: Database Systems for Advanced ApplicationsEditors
G Li, J Gama, Y Tong, J Yang, J NatwichaiPagination
452-467ISSN
0302-9743Department/School
School of Information and Communication TechnologyPublisher
Springer Nature SwitzerlandPlace of publication
SwitzerlandEvent title
24th International Conference, DASFAA 2019: Database Systems for Advanced ApplicationsEvent Venue
Chiang Mai, ThailandDate of Event (Start Date)
2019-04-22Date of Event (End Date)
2019-04-25Rights statement
Copyright © 2019 Springer Nature Switzerland AGRepository Status
- Restricted