Matrix computations (3rd ed.)
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Weighted Graph Cuts without Eigenvectors A Multilevel Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combinational collaborative filtering for personalized community recommendation
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Relational learning via collective matrix factorization
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative filtering for orkut communities: discovery of user latent behavior
Proceedings of the 18th international conference on World wide web
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Co-evolution of social and affiliation networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Enhancing multilingual latent semantic analysis with term alignment information
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Clustering with Multiple Graphs
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Connecting content to community in social media via image content, user tags and user communication
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Affiliation recommendation using auxiliary networks
Proceedings of the fourth ACM conference on Recommender systems
Transfer learning to predict missing ratings via heterogeneous user feedbacks
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Applying latent semantic analysis to tag-based community recommendations
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
Transfer learning in heterogeneous collaborative filtering domains
Artificial Intelligence
Analyzing user behavior across social sharing environments
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
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Social network analysis has attracted increasing attention in recent years. In many social networks, besides friendship links among users, the phenomenon of users associating themselves with groups or communities is common. Thus, two networks exist simultaneously: the friendship network among users, and the affiliation network between users and groups. In this article, we tackle the affiliation recommendation problem, where the task is to predict or suggest new affiliations between users and communities, given the current state of the friendship and affiliation networks. More generally, affiliations need not be community affiliations---they can be a user’s taste, so affiliation recommendation algorithms have applications beyond community recommendation. In this article, we show that information from the friendship network can indeed be fruitfully exploited in making affiliation recommendations. Using a simple way of combining these networks, we suggest two models of user-community affinity for the purpose of making affiliation recommendations: one based on graph proximity, and another using latent factors to model users and communities. We explore the affiliation recommendation algorithms suggested by these models and evaluate these algorithms on two real-world networks, Orkut and Youtube. In doing so, we motivate and propose a way of evaluating recommenders, by measuring how good the top 50 recommendations are for the average user, and demonstrate the importance of choosing the right evaluation strategy. The algorithms suggested by the graph proximity model turn out to be the most effective. We also introduce scalable versions of these algorithms, and demonstrate their effectiveness. This use of link prediction techniques for the purpose of affiliation recommendation is, to our knowledge, novel.