Modeling Temporal Behavior in Large Networks: A Dynamic Mixed-Membership Model

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Given a large time-evolving network, how can we model and characterize the temporal behaviors of individual nodes (and network states)? How can we model the behavioral transition patterns of nodes? We propose a temporal behavior model that captures the 'roles' of nodes in the graph and how they evolve over time. The proposed dynamic behavioral mixed-membership model (DBMM) is scalable, fully automatic (no user-defined parameters), non-parametric/data-driven (no specific functional form or parameterization), interpretable (identifies explainable patterns), and flexible (applicable to dynamic and streaming networks). Moreover, the interpretable behavioral roles are generalizable, computationally efficient, and natively supports attributes. We applied our ... continued below

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PDF-file: 12 pages; size: 27.5 Mbytes

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Rossi, R; Gallagher, B; Neville, J & Henderson, K November 11, 2011.

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Given a large time-evolving network, how can we model and characterize the temporal behaviors of individual nodes (and network states)? How can we model the behavioral transition patterns of nodes? We propose a temporal behavior model that captures the 'roles' of nodes in the graph and how they evolve over time. The proposed dynamic behavioral mixed-membership model (DBMM) is scalable, fully automatic (no user-defined parameters), non-parametric/data-driven (no specific functional form or parameterization), interpretable (identifies explainable patterns), and flexible (applicable to dynamic and streaming networks). Moreover, the interpretable behavioral roles are generalizable, computationally efficient, and natively supports attributes. We applied our model for (a) identifying patterns and trends of nodes and network states based on the temporal behavior, (b) predicting future structural changes, and (c) detecting unusual temporal behavior transitions. We use eight large real-world datasets from different time-evolving settings (dynamic and streaming). In particular, we model the evolving mixed-memberships and the corresponding behavioral transitions of Twitter, Facebook, IP-Traces, Email (University), Internet AS, Enron, Reality, and IMDB. The experiments demonstrate the scalability, flexibility, and effectiveness of our model for identifying interesting patterns, detecting unusual structural transitions, and predicting the future structural changes of the network and individual nodes.

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PDF-file: 12 pages; size: 27.5 Mbytes

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  • Report No.: LLNL-TR-514271
  • Grant Number: W-7405-ENG-48
  • DOI: 10.2172/1035597 | External Link
  • Office of Scientific & Technical Information Report Number: 1035597
  • Archival Resource Key: ark:/67531/metadc840364

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  • November 11, 2011

Added to The UNT Digital Library

  • May 19, 2016, 3:16 p.m.

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  • Nov. 29, 2016, 8:22 p.m.

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Rossi, R; Gallagher, B; Neville, J & Henderson, K. Modeling Temporal Behavior in Large Networks: A Dynamic Mixed-Membership Model, report, November 11, 2011; Livermore, California. (digital.library.unt.edu/ark:/67531/metadc840364/: accessed August 23, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.