On Learning the Structure of Clusters in Graphs
This addresses the need for efficient graph clustering methods that account for high-level cluster structure in applications like image classification and social networks, but it appears incremental as it builds on existing graph clustering frameworks.
The paper tackles the problem of learning the structure of clusters in graphs and hypergraphs, which is often overlooked in existing algorithms, and presents four new algorithms that are shown to be practical and effective on synthetic and real-world datasets.
Graph clustering is a fundamental problem in unsupervised learning, with numerous applications in computer science and in analysing real-world data. In many real-world applications, we find that the clusters have a significant high-level structure. This is often overlooked in the design and analysis of graph clustering algorithms which make strong simplifying assumptions about the structure of the graph. This thesis addresses the natural question of whether the structure of clusters can be learned efficiently and describes four new algorithmic results for learning such structure in graphs and hypergraphs. All of the presented theoretical results are extensively evaluated on both synthetic and real-word datasets of different domains, including image classification and segmentation, migration networks, co-authorship networks, and natural language processing. These experimental results demonstrate that the newly developed algorithms are practical, effective, and immediately applicable for learning the structure of clusters in real-world data.