"In this master thesis we present a novel approach to finding communities in large graphs. Our method finds the overlapped and hierarchical structure of communities efficiently, outperforming previous proposals. We propose a new objective function that allows to evaluate the quality of a community naturally including nodes shared by other communities. This is achieved by implicitly mapping the nodes of the graph in a vectorial space, using as a basis a construction presented by Lóvasz in 1979. We present and analyse several algorithms to decompose a given graph into a set of not necessarily disjoint neighborhoods. This has applications for analysing and summarizing the large-scale structure of complex networks.
International audienceDiscovering the latent community structure is cru- cial to understanding the f...
International audienceDiscovering the latent community structure is crucial to understanding the fea...
summary:Community detection algorithms help us improve the management of complex networks and provid...
"In this master thesis we present a novel approach to finding communities in large graphs. Our metho...
Abstract — Finding decompositions of a graph into a family of clusters is crucial to understanding i...
Vertices in complex networks can be grouped into communities, where vertices inside communities...
Finding groups of connected individuals in large graphs with tens of thousands or more nodes has rec...
International audienceDetecting and analyzing dense subgroups or communities from social and informa...
Massive social networks have become increasingly popular in recent years. Community detection is one...
International audienceIn this paper, we propose a new approach to detect overlapping communities in ...
Community structure is observed in many real-world networks in fields ranging from social networking...
In this paper, we establish the definition of community fundamentally different from what was common...
In this thesis, we first explore two different approaches to efficient community detection that addr...
International audienceCommunity detection, also known as graph clustering, has been extensively stud...
AbstractDense sub-graphs of sparse graphs (communities), which appear in most real-world complex net...
International audienceDiscovering the latent community structure is cru- cial to understanding the f...
International audienceDiscovering the latent community structure is crucial to understanding the fea...
summary:Community detection algorithms help us improve the management of complex networks and provid...
"In this master thesis we present a novel approach to finding communities in large graphs. Our metho...
Abstract — Finding decompositions of a graph into a family of clusters is crucial to understanding i...
Vertices in complex networks can be grouped into communities, where vertices inside communities...
Finding groups of connected individuals in large graphs with tens of thousands or more nodes has rec...
International audienceDetecting and analyzing dense subgroups or communities from social and informa...
Massive social networks have become increasingly popular in recent years. Community detection is one...
International audienceIn this paper, we propose a new approach to detect overlapping communities in ...
Community structure is observed in many real-world networks in fields ranging from social networking...
In this paper, we establish the definition of community fundamentally different from what was common...
In this thesis, we first explore two different approaches to efficient community detection that addr...
International audienceCommunity detection, also known as graph clustering, has been extensively stud...
AbstractDense sub-graphs of sparse graphs (communities), which appear in most real-world complex net...
International audienceDiscovering the latent community structure is cru- cial to understanding the f...
International audienceDiscovering the latent community structure is crucial to understanding the fea...
summary:Community detection algorithms help us improve the management of complex networks and provid...