Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to Probabilistically Identify Authoritative Documents
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
The Journal of Machine Learning Research
Combining content and link for classification using matrix factorization
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Mixed Membership Stochastic Blockmodels
The Journal of Machine Learning Research
Combining link and content for community detection: a discriminative approach
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning systems of concepts with an infinite relational model
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Relation regularized matrix factorization
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Multiplicative latent factor models for description and prediction of social networks
Computational & Mathematical Organization Theory
A Survey of Statistical Network Models
Foundations and Trends® in Machine Learning
A Bayesian framework for community detection integrating content and link
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Link prediction via latent factor BlockModel
Proceedings of the 21st international conference companion on World Wide Web
Latent factor blockmodel for modelling relational data
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Collaborative topic regression with social regularization for tag recommendation
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Online egocentric models for citation networks
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Homophily and stochastic equivalence are two primary features of interest in social networks. Recently, the multiplicative latent factor model (MLFM) is proposed to model social networks with directed links. Although MLFM can capture stochastic equivalence, it cannot model well homophily in networks. However, many real-world networks exhibit homophily or both homophily and stochastic equivalence, and hence the network structure of these networks cannot be modeled well by MLFM. In this paper, we propose a novel model, called generalized latent factor model (GLFM), for social network analysis by enhancing homophily modeling in MLFM. We devise a minorization-maximization (MM) algorithm with linear-time complexity and convergence guarantee to learn the model parameters. Extensive experiments on some real-world networks show that GLFM can effectively model homophily to dramatically outperform state-of-the-art methods.