Scalable proximity estimation and link prediction in online social networks
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Role of weak ties in link prediction of complex networks
Proceedings of the 1st ACM international workshop on Complex networks meet information & knowledge management
Towards time-aware link prediction in evolving social networks
Proceedings of the 3rd Workshop on Social Network Mining and Analysis
Link prediction applied to an open large-scale online social network
Proceedings of the 21st ACM conference on Hypertext and hypermedia
Structural link analysis and prediction in microblogs
Proceedings of the 20th ACM international conference on Information and knowledge management
Friendship prediction and homophily in social media
ACM Transactions on the Web (TWEB)
Clustered embedding of massive social networks
Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems
Security and Communication Networks
Link Prediction: Fair and Effective Evaluation
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Link Prediction Using BenefitRanks in Weighted Networks
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
How do people link?: analysis of contact structures in human face-to-face proximity networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Link prediction in multi-relational collaboration networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Predicting the social influence of upcoming contents in large social networks
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
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Question-Answering Bulletin Boards (QABB), such as Yahoo! Answers and Windows Live QnA, are gaining popularity recently. Communications on QABB connect users, and the overall connections can be regarded as a social network. If the evolution of social networks can be predicted, it is quite useful for encouraging communications among users. This paper describes an improved method for predicting links based on weighted proximity measures of social networks. The method is based on an assumption that proximities between nodes can be estimated better by using both graph proximity measures and the weights of existing links in a social network. In order to show the effectiveness of our method, the data of Yahoo! Chiebukuro (Japanese Yahoo! Answers) are used for our experiments. The results show that our method outperforms previous approaches, especially when target social networks are sufficiently dense.