论文标题
在学习图中簇的结构
On Learning the Structure of Clusters in Graphs
论文作者
论文摘要
图形聚类是无监督学习的一个基本问题，在计算机科学和分析现实世界中的许多应用中应用。在许多实际应用中，我们发现簇具有重要的高级结构。在图形聚类算法的设计和分析中，通常会忽略这一点，这些算法对图形结构进行了强烈的简化假设。本文解决了是否可以有效地学习簇结构的自然问题，并描述了在图和超图中学习这种结构的四种新算法结果。 所有介绍的理论结果均可在不同域的合成数据集和现实词数据集上进行广泛评估，包括图像分类和分割，迁移网络，共同授权网络以及自然语言处理。这些实验结果表明，新开发的算法是实用的，有效的，并且可立即用于学习现实数据中的群集结构。
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.
