.png){kind=icon}
.png){kind=icon}
Return
An improved network structural balance approach based on weighted node-to-node influence with evolutionary algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2020.106323.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The network structural balance is a challenging task in social networks. The difficulty is how to determine the unbalanced degree of a network and make the network balanced with the least cost. Aiming at this issue, this paper proposes a new weighted node-to-node influence (W2NI) model, which integrates two important node-to-node influence factors, i.e., weights and influences between nodes. Then, an EA-based weighted influence structural balance (WISB) algorithm is devised deliberately to optimize W2NI model. In WISB algorithm, a neighbors-based initialization and a random greedy based local search strategies are proposed to enhance its convergence performance. Comprehensive experiments on a set of generated networks and real social networks demonstrate the effectiveness and efficiency of the proposed model. (C) 2020 Published by Elsevier B.V.
Keywords:
Signed social network
Node-to-node influence
Structural balance
Evolutionary algorithm
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.6
Papers:
1.4W
Citations:
4.8W