Network 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.

Item Type:	Refereed Article
Keywords:	network embedding, feature learning, edge representation, network mining, clustering
Research Division:	Information and Computing Sciences
Research Group:	Computer vision and multimedia computation
Research Field:	Pattern recognition
Objective Division:	Defence
Objective Group:	Defence
Objective Field:	Intelligence, surveillance and space
UTAS Author:	Cao, Z (Dr Zehong Cao)
ID Code:	134278
Year Published:	2019
Web of Science® Times Cited:	13
Deposited By:	Information and Communication Technology
Deposited On:	2019-08-06
Last Modified:	2020-05-18
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