Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
The Journal of Machine Learning Research
Mining Social Network of Conference Participants from the Web
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
The Small World of Software Reverse Engineering
WCRE '04 Proceedings of the 11th Working Conference on Reverse Engineering
Mining directed social network from message board
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
LINKREC: a unified framework for link recommendation with user attributes and graph structure
Proceedings of the 19th international conference on World wide web
Social Link Prediction in Online Social Tagging Systems
ACM Transactions on Information Systems (TOIS)
Who proposed the relationship?: recovering the hidden directions of undirected social networks
Proceedings of the 23rd international conference on World wide web
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In this paper, a new approach to predicting the structure of a social network without any prior knowledge from the social links is proposed. In absence of links among nodes, we assume there are other information resources associated with the nodes which are called node profiles. The task of link prediction and recommendation from text data is to learn similarities between the nodes and then translate pair-wise similarities into social links. In other words, the process is to convert a similarity matrix into an adjacency matrix. In this paper, an alternative approach is proposed. First, hidden topics of node profiles are learned using Latent Dirichlet Allocation. Then, by mapping node-topic and topic-topic relations, a new structure called semi-bipartite graph is generated which is slightly different from regular bipartite graph. Finally, by applying topological metrics such as Katz and short path scores to the new structure, we are able to rank and recommend relevant links to each node. The proposed technique is applied to several co-authorship networks. While most link prediction methods are low precision solutions, the proposed method performs effectively and offers high precision.