Regular Partitions and Their Use in Structural Pattern Recognition
This work addresses the problem of processing massive network data for researchers and practitioners in structural pattern recognition, but it appears incremental as it builds on existing heuristics and models.
The authors tackled the challenge of summarizing large graphs by introducing a framework based on Szemerédi's Regularity Lemma, which partitions graphs into random-like bipartite graphs to create summaries that retain essential structural properties, and they applied this to tasks like clustering, image segmentation, and graph search with improvements in efficiency, robustness, and summary quality.
Recent years are characterized by an unprecedented quantity of available network data which are produced at an astonishing rate by an heterogeneous variety of interconnected sensors and devices. This high-throughput generation calls for the development of new effective methods to store, retrieve, understand and process massive network data. In this thesis, we tackle this challenge by introducing a framework to summarize large graphs based on Szemerédi's Regularity Remma (RL), which roughly states that any sufficiently large graph can almost entirely be partitioned into a bounded number of random-like bipartite graphs. The partition resulting from the RL gives rise to a summary, which inherits many of the essential structural properties of the original graph. We first extend an heuristic version of the RL to improve its efficiency and its robustness. We use the proposed algorithm to address graph-based clustering and image segmentation tasks. In the second part of the thesis, we introduce a new heuristic algorithm which is characterized by an improvement of the summary quality both in terms of reconstruction error and of noise filtering. We use the proposed heuristic to address the graph search problem defined under a similarity measure. Finally, we study the linkage among the regularity lemma, the stochastic block model and the minimum description length. This study provide us a principled way to develop a graph decomposition algorithm based on stochastic block model which is fitted using likelihood maximization.