For the contextual labeled stochastic block model, any community detection algorithm must misclassify at least about n exp(-nD) nodes in expectation, where D combines network and attribute divergences, and the proposed spectral method only achieves a polynomial rate.
Community detection with nodal information
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abstract
Community detection is one of the fundamental problems in the study of network data. Most existing community detection approaches only consider edge information as inputs, and the output could be suboptimal when nodal information is available. In such cases, it is desirable to leverage nodal information for the improvement of community detection accuracy. Towards this goal, we propose a flexible network model incorporating nodal information, and develop likelihood-based inference methods. For the proposed methods, we establish favorable asymptotic properties as well as efficient algorithms for computation. Numerical experiments show the effectiveness of our methods in utilizing nodal information across a variety of simulated and real network data sets.
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stat.ML 1years
2025 1verdicts
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Community Detection for Contextual-LSBM: Theoretical Limitations of Misclassification Rate and Efficient Algorithms
For the contextual labeled stochastic block model, any community detection algorithm must misclassify at least about n exp(-nD) nodes in expectation, where D combines network and attribute divergences, and the proposed spectral method only achieves a polynomial rate.