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Diffusion Kernels on Graphs and Other Discrete Input Spaces
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Normalized Cuts and Image Segmentation
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Natural communities in large linked networks
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Why collective inference improves relational classification
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Homophily in online dating: when do you like someone like yourself?
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Leveraging relational autocorrelation with latent group models
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Dynamic social network analysis using latent space models
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Classification in Networked Data: A Toolkit and a Univariate Case Study
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Statistical properties of community structure in large social and information networks
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Using ghost edges for classification in sparsely labeled networks
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Mixed Membership Stochastic Blockmodels
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Large scale multi-label classification via metalabeler
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Relational learning via latent social dimensions
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Semi-supervised multi-label learning by constrained non-negative matrix factorization
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Probabilistic classification and clustering in relational data
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Scalable learning of collective behavior based on sparse social dimensions
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Empirical comparison of algorithms for network community detection
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Discriminative probabilistic models for relational data
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Large-scale behavioral targeting with a social twist
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Social media has reshaped the way in which people interact with each other. The rapid development of participatory web and social networking sites like YouTube, Twitter, and Facebook, also brings about many data mining opportunities and novel challenges. In particular, we focus on classification tasks with user interaction information in a social network. Networks in social media are heterogeneous, consisting of various relations. Since the relation-type information may not be available in social media, most existing approaches treat these inhomogeneous connections homogeneously, leading to an unsatisfactory classification performance. In order to handle the network heterogeneity, we propose the concept of social dimension to represent actors' latent affiliations, and develop a classification framework based on that. The proposed framework, SocioDim, first extracts social dimensions based on the network structure to accurately capture prominent interaction patterns between actors, then learns a discriminative classifier to select relevant social dimensions. SocioDim, by differentiating different types of network connections, outperforms existing representative methods of classification in social media, and offers a simple yet effective approach to integrating two types of seemingly orthogonal information: the network of actors and their attributes.