Topic-Centric Algorithm: A Novel Approach to Web Link Analysis
AINA '04 Proceedings of the 18th International Conference on Advanced Information Networking and Applications - Volume 2
Learning to rank networked entities
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Automated social hierarchy detection through email network analysis
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Extracting relations in social networks from the web using similarity between collective contexts
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Building a social network of research institutes from information available on the web
International Journal of Networking and Virtual Organisations
International Journal of Web Based Communities
Static analysis and exponential random graph modelling for micro-blog network
Journal of Information Science
Journal of Information Science
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Social networks have attracted much attention recently. Different studies have been conducted to automatically extract social networks among various kinds of entities from the web. Social network analysis finds its application in many current business areas. In this paper we demonstrate how the choice of the similarity measure affects ranking results of entities in a social network extracted from the web. We use different similarity measures in order to build different social networks. By applying formulas described below for each of the networks we derive a new network which is different from the original one by edge weights. Subsequently, in the derived networks we rank entities again. Finally we compare the results.