We explore the different roles of two fundamental concepts in graph theory, indegree and outdegree, in the context of clustering. The average indegree reflects the density near a sample, and the average outdegree characterizes the local geometry around a sample. Based on such insights, we ...
This paper proposes a new graph clustering algorithm that automatically defines the number of clusters based on a clustering tendency connectivity-based validation measure, also proposed in the paper. According to the computational results, the new algorithm is able to efficiently find graph clustering ...
In this case, first a neighborhood graph is constructed using the similarities between the points and then a graph clustering algorithm is applied to this graph. In this paper we investigate the influence of the construction of the similarity graph on the clustering results. We first study the ...
In this paper, we present an empirical study that compares the node clustering performances of state-of-the-art algorithms from both the probabilistic and spectral families on undirected graphs. Our experiments show that no family dominates over the other and that network characteristics play a ...
一、想特别记录一下拉普拉斯图的一些性质,因为感觉虽然这个算法可能已经过时了,但是Laplacian graph现在仍然有很多应用场景。 拉普拉斯图是度矩阵和权重矩阵的相减得来的,所以他有一下这几种性质。: 1、他是实对称阵。这一点很重要,意味着他可以做特征分解。
We present a generalized version of spectral clustering using the graph p-Laplacian, a nonlinear generalization of the standard graph Laplacian. We show that the second eigenvector of the graph p-Laplacian interpolates between a relaxation of the normalized and the Cheeger cut. Moreover, we prove...
This paper examines a schema for graph-theoretic clustering using node-based resilience measures. Node-based resilience measures optimize an objective based on a critical set of nodes whose removal causes some severity of disconnection in the network. Be
2022A Survey of Deep Graph Clustering: Taxonomy, Challenge, and ApplicationarXivLinkLink 2022A Comprehensive Survey on Community Detection with Deep LearningTNNLSLink- 2020A Comprehensive Survey on Graph Neural NetworksTNNLSLink- 2020Deep Learning for Community Detection: Progress, Challenges and Opportunit...
Most existing graph-based clustering methods need a predefined graph and their clustering performance highly depends on the quality of the graph. Aiming to improve the multiview clustering performance, a graph learning-based method is proposed to improve the quality of the graph. Initial graphs are...
In this method based on regions of influence, a graph is constructed and the edges of the graph having the higher values are cut according to a hierarchical divisive procedure. An index is calculated from the size average of the cut edges which self-detects the more appropriate number of ...