Sign prediction and community detection in directed signed networks based on random walk theory
by Baofang Hu; Hong Wang; Yuanjie Zheng
International Journal of Embedded Systems (IJES), Vol. 11, No. 2, 2019

Abstract: Previous studies on social networks often focused on networks with only positive edges between node pairs. As a significant extension, we applied the random walk theory based on graphs with both positive and negative edges. In particular, we derived the commute time similarity between node pairs in directed signed networks and proved that its corresponding Laplace spectral was a legal kernel to compute the similarities between node pairs. We utilised the similarity distance to predict the sign and direction of the edges on two real social networks based on the idea of collaborative filtering, and the experimental results showed that the method provided good performance. We also utilised the defined Laplacian spectrum of the directed signed networks to detect the community structure in two real-world networks and three synthetic networks and the algorithm achieved good performance.

Online publication date: Wed, 13-Mar-2019

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Embedded Systems (IJES):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email