Web metasearch: rank vs. score based rank aggregation methods
Proceedings of the 2003 ACM symposium on Applied computing
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
On six degrees of separation in DBLP-DB and more
ACM SIGMOD Record
Efficient top-k querying over social-tagging networks
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Collaboration recommendation on academic social networks
ER'10 Proceedings of the 2010 international conference on Advances in conceptual modeling: applications and challenges
Supervised random walks: predicting and recommending links in social networks
Proceedings of the fourth ACM international conference on Web search and data mining
CollabSeer: a search engine for collaboration discovery
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
Co-author Relationship Prediction in Heterogeneous Bibliographic Networks
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
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This paper studies the problem of recommending collaborators in a social network, given a set of keywords. Formally, given a query q, consisting of a researcher s (who is a member of a social network) and a set of keywords k (e.g., an article name or topic of future work), the collaborator recommendation problem is to return a high-quality ranked list of possible collaborators for s on the topic k. Extensive effort was expended to define ranking functions that take into consideration a variety of properties, including structural proximity to s, textual relevance to k, and importance. The effectiveness of our methods have been experimentally proven over two large subsets of the social network determined by DBLP co-authorship data. The results show that the ranking methods developed in this paper work well in practice.