On Learning the Structure of Clusters in Graphs

Autor:	Macgregor, Peter
Rok vydání:	2022
Předmět:	Computer Science - Data Structures and Algorithms Computer Science - Machine Learning Computer Science - Social and Information Networks
Druh dokumentu:	Working Paper
Popis:	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. Comment: PhD Thesis, University of Edinburgh
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2212.14345 Zobrazit plný text záznamu View this record from Arxiv