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Fundamentals of matrix computations
Fundamentals of matrix computations
CrimeLink explorer: using domain knowledge to facilitate automated crime association analysis
ISI'03 Proceedings of the 1st NSF/NIJ conference on Intelligence and security informatics
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
iLink: search and routing in social networks
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Impact of social influence in e-commerce decision making
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Social ties and their relevance to churn in mobile telecom networks
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Facetnet: a framework for analyzing communities and their evolutions in dynamic networks
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Generating Social Network Features for Link-Based Classification
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Analyzing communities and their evolutions in dynamic social networks
ACM Transactions on Knowledge Discovery from Data (TKDD)
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Transforming graph data for statistical relational learning
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This paper explores two aspects of social network modeling. First, we generalize a successful static model of relationships into a dynamic model that accounts for friendships drifting over time. Second, we show how to make it tractable to learn such models from data, even as the number of entities n gets large. The generalized model associates each entity with a point in p-dimensional Euclidean latent space. The points can move as time progresses but large moves in latent space are improbable. Observed links between entities are more likely if the entities are close in latent space. We show how to make such a model tractable (sub-quadratic in the number of entities) by the use of appropriate kernel functions for similarity in latent space; the use of low dimensional KD-trees; a new efficient dynamic adaptation of multidimensional scaling for a first pass of approximate projection of entities into latent space; and an efficient conjugate gradient update rule for non-linear local optimization in which amortized time per entity during an update is O(log n). We use both synthetic and real-world data on up to 11,000 entities which indicate near-linear scaling in computation time and improved performance over four alternative approaches. We also illustrate the system operating on twelve years of NIPS co-authorship data.