10 pages, 8 figuresInternational audienceThis article presents an efficient hierarchical clustering algorithm that solves the problem of core community detection. It is a variant of the standard community detection problem in which we are particularly interested in the connected core of communities. To provide a solution to this problem, we question standard definitions on communities and provide alternatives. We also propose a function called compactness, designed to assess the quality of a solution to this problem. Our algorithm is based on a graph traversal algorithm, the LexDFS. The time complexity of our method is in $O(n\times log(n))$. Experiments show that our algorithm creates highly compact clusters
The main objective of the thesis is the creation of an algorithm to detect the community structure ...
Community structure is observed in many real-world networks in fields ranging from social networking...
Clustering networks play a key role in many scientific fields, from Biology to Sociology and Compute...
10 pages, 8 figuresInternational audienceThis article presents an efficient hierarchical clustering ...
Community detection has arisen as one of the most relevant topics in the field of graph mining, prin...
University of Technology Sydney. Faculty of Engineering and Information Technology.Community detecti...
International audienceDue to the development and popularization of Internet, there is more and more ...
Community detection has arisen as one of the most relevant topics in the field of graph data mining ...
Finding groups of connected individuals in large graphs with tens of thousands or more nodes has rec...
In this thesis, we first explore two different approaches to efficient community detection that addr...
Abstract—Detecting compact overlapping communities in large networks is an important pattern recogni...
Graph clustering, or community detection, is the task of identifying groups of closely related objec...
Current network analysis algorithms are of seminal importance because they are able to detect patter...
Finding community structures in social networks is considered to be a challenging task as many of th...
Community structures are an important feature of many social, biological, and technological networks...
The main objective of the thesis is the creation of an algorithm to detect the community structure ...
Community structure is observed in many real-world networks in fields ranging from social networking...
Clustering networks play a key role in many scientific fields, from Biology to Sociology and Compute...
10 pages, 8 figuresInternational audienceThis article presents an efficient hierarchical clustering ...
Community detection has arisen as one of the most relevant topics in the field of graph mining, prin...
University of Technology Sydney. Faculty of Engineering and Information Technology.Community detecti...
International audienceDue to the development and popularization of Internet, there is more and more ...
Community detection has arisen as one of the most relevant topics in the field of graph data mining ...
Finding groups of connected individuals in large graphs with tens of thousands or more nodes has rec...
In this thesis, we first explore two different approaches to efficient community detection that addr...
Abstract—Detecting compact overlapping communities in large networks is an important pattern recogni...
Graph clustering, or community detection, is the task of identifying groups of closely related objec...
Current network analysis algorithms are of seminal importance because they are able to detect patter...
Finding community structures in social networks is considered to be a challenging task as many of th...
Community structures are an important feature of many social, biological, and technological networks...
The main objective of the thesis is the creation of an algorithm to detect the community structure ...
Community structure is observed in many real-world networks in fields ranging from social networking...
Clustering networks play a key role in many scientific fields, from Biology to Sociology and Compute...