资源分类
》 资源 》论文》Dynamic Infinite Relational Model for Time-varying Relational Data Analysis

Dynamic Infinite Relational Model for Time-varying Relational Data Analysis

2020-01-06 | |  54 |   44 |   0

Abstract

We propose a new probabilistic model for analyzing dynamic evolutions of relational data, such as additions, deletions and split & merge, of relation clusters like communities in social networks. Our proposed model abstracts observed timevarying object-object relationships into relationships between object clusters. We extend the infinite Hidden Markov model to follow dynamic and time-sensitive changes in the structure of the relational data and to estimate a number of clusters simultaneously. We show the usefulness of the model through experiments with synthetic and real-world data sets.

上一篇：A Primal-Dual Message-Passing Algorithm for Approximated Large Scale Structured Prediction

下一篇：Learning the context of a category

用户评价
全部评价

热门资源

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Learning to learn...

    The move from hand-designed features to learned...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...