SubSearch: Robust Estimation and Outlier Detection for Stochastic Block Models via Subgraph Search

2025-06-04

AI Summary

Real-world graph data often violate the assumptions of the Stochastic Block Model (SBM), undermining the robustness of parameter estimation in conventional community detection algorithms. To address this, we propose a robust SBM estimation framework based on subgraph space search: leveraging likelihood-driven iterative subgraph optimization and resampling, it automatically identifies the subgraph most consistent with SBM structure; subsequently enables accurate block-structure parameter estimation and—novelly—establishes an interpretable, node-level anomaly attribution mechanism that goes beyond simple degree-based pruning. The method simultaneously outputs confidence scores for anomalous nodes. Experiments on both synthetic and real-world networks demonstrate substantial improvements in SBM parameter estimation accuracy, precise localization of structurally anomalous nodes, and superior robustness compared to state-of-the-art robust SBM methods.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Graph-based Machine Learning
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

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.

Problem

• Robust estimation of SBM parameters under deviations
• Outlier detection in graphs beyond simple pruning methods
• Exploring subgraphs to align with SBM assumptions

Innovation

• Robust SBM estimation via subgraph search
• Outlier detection by identifying deviant nodes
• Explores subgraph space for model alignment