Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images
Readings in uncertain reasoning
Enhanced hypertext categorization using hyperlinks
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Natural communities in large linked networks
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning relational probability trees
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Why collective inference improves relational classification
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Leveraging relational autocorrelation with latent group models
MRDM '05 Proceedings of the 4th international workshop on Multi-relational mining
Dynamic social network analysis using latent space models
ACM SIGKDD Explorations Newsletter
Graph mining: Laws, generators, and algorithms
ACM Computing Surveys (CSUR)
Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)
Classification in Networked Data: A Toolkit and a Univariate Case Study
The Journal of Machine Learning Research
A tutorial on spectral clustering
Statistics and Computing
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Topic taxonomy adaptation for group profiling
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Using ghost edges for classification in sparsely labeled networks
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Community evolution in dynamic multi-mode networks
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Mixed Membership Stochastic Blockmodels
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Large scale multi-label classification via metalabeler
Proceedings of the 18th international conference on World wide web
Semi-supervised multi-label learning by constrained non-negative matrix factorization
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Scalable learning of collective behavior based on sparse social dimensions
Proceedings of the 18th ACM conference on Information and knowledge management
Network quantification despite biased labels
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
Mining advisor-advisee relationships from research publication networks
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Social action tracking via noise tolerant time-varying factor graphs
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Data Mining and Knowledge Discovery
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CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Architecture for automated search and negotiation in affiliation among community websites and blogs
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Constructing social networks from unstructured group dialog in virtual worlds
SBP'11 Proceedings of the 4th international conference on Social computing, behavioral-cultural modeling and prediction
A multi-resolution approach to learning with overlapping communities
Proceedings of the First Workshop on Social Media Analytics
Exploiting vulnerability to secure user privacy on a social networking site
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ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Leveraging social media networks for classification
Data Mining and Knowledge Discovery
Extracting multi-dimensional relations: a generative model of groups of entities in a corpus
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Large-scale behavioral targeting with a social twist
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Multi-relational matrix factorization using bayesian personalized ranking for social network data
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Community detection via heterogeneous interaction analysis
Data Mining and Knowledge Discovery
Finding overlapping communities in social networks: toward a rigorous approach
Proceedings of the 13th ACM Conference on Electronic Commerce
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Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Cross-domain collaboration recommendation
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ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
Community Detection in Social Networks Using Information Diffusion
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Optimal Spatial Resolution for the Analysis of Human Mobility
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
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AWC '13 Proceedings of the First Australasian Web Conference - Volume 144
Efficient community detection with additive constrains on large networks
Knowledge-Based Systems
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Social media such as blogs, Facebook, Flickr, etc., presents data in a network format rather than classical IID distribution. To address the interdependency among data instances, relational learning has been proposed, and collective inference based on network connectivity is adopted for prediction. However, connections in social media are often multi-dimensional. An actor can connect to another actor for different reasons, e.g., alumni, colleagues, living in the same city, sharing similar interests, etc. Collective inference normally does not differentiate these connections. In this work, we propose to extract latent social dimensions based on network information, and then utilize them as features for discriminative learning. These social dimensions describe diverse affiliations of actors hidden in the network, and the discriminative learning can automatically determine which affiliations are better aligned with the class labels. Such a scheme is preferred when multiple diverse relations are associated with the same network. We conduct extensive experiments on social media data (one from a real-world blog site and the other from a popular content sharing site). Our model outperforms representative relational learning methods based on collective inference, especially when few labeled data are available. The sensitivity of this model and its connection to existing methods are also examined.