SubSearch: Robust Estimation and Outlier Detection for Stochastic Block Models via Subgraph Search
This addresses the need for robust algorithms in network analysis for real-world applications, representing an incremental improvement over existing methods.
The paper tackles the problem of robust community detection in graphs that deviate from ideal Stochastic Block Model assumptions by proposing SubSearch, which estimates parameters and detects outliers through subgraph search, showing effectiveness in experiments on synthetic and real-world datasets.
Community detection is a fundamental task in graph analysis, with methods often relying on fitting models like the Stochastic Block Model (SBM) to observed networks. While many algorithms can accurately estimate SBM parameters when the input graph is a perfect sample from the model, real-world graphs rarely conform to such idealized assumptions. Therefore, robust algorithms are crucial-ones that can recover model parameters even when the data deviates from the assumed distribution. In this work, we propose SubSearch, an algorithm for robustly estimating SBM parameters by exploring the space of subgraphs in search of one that closely aligns with the model's assumptions. Our approach also functions as an outlier detection method, properly identifying nodes responsible for the graph's deviation from the model and going beyond simple techniques like pruning high-degree nodes. Extensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of our method.