An Algorithm to Find Overlapping Community Structure in Networks
By Steve Gregory. European Conference on Principles of Data Mining and Knowledge Discovery, 2007
347 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Networks and Communications, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/6d5855cc45c011b81609affdb27f5c772e110567
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Fosca Giannotti
- Huan Liu
- Dino Pedreschi
- Lei Tang
- Michele Coscia
- Boleslaw K. Szymanski
- Jierui Xie
- Xufei Wang
In Computer Networks and Communications
- Fosca Giannotti
- Dino Pedreschi
- Michele Coscia
- Aaron F. McDaid
- Fergal Reid
- Neil J. Hurley
- Conrad Lee
- Boleslaw K. Szymanski
In Statistical and Nonlinear Physics
- Jure Leskovec
- Huan Liu
- Fosca Giannotti
- Dino Pedreschi
- Michele Coscia
- Lei Tang
- Santo Fortunato
- Boleslaw K. Szymanski
Citations in the corpus
- Detecting highly overlapping community structure by greedy clique expansion (2010, background): “[9] published a groundbreaking CAA capable of detecting overlapping communities, a number of algorithms have been developed that are able to assign nodes to more than one community [5, 9–16].”
- Finding Statistically Significant Communities in Networks (2010, neutral): “In such cases communities are overlapping (and partitions become covers) and very few methods account for this possibility [18–25], which considerably increases the complexity of the problem.”
- Overlapping community detection in networks: The state-of-the-art and comparative study (2011, background): “CONGA 14 [Gregory 2007] extends Girvan and Newman’s divisive clustering algo-rithm (GN) [Girvan and Newman 2002] by allowing a node to split into multiple copies.”
- A classification for community discovery methods in complex networks (2011, background): “The split betweenness [129] of a vertex v is the number of shortest paths that would pass between the two parts of v if it was split.”
- Community detection in graphs (2009, methodology): “In another work (Zhou and Lipowsky, 2004), Zhou and Lipowsky adopted biased random walkers, where the bias is due to the fact that walkers move preferentially towards vertices sharing a large number of neighbours with the starting vertex.”
- SLPA: Uncovering Overlapping Communities in Social Networks via a Speaker-Listener Interaction Dynamic Process (2011, background): “LFM depends significantly on a parameter of the fitness function that controls the size of the communities.”
- Scalable Learning of Collective Behavior (2012, methodology): “Gregory [33] extends the Newman-Girvan method [34] to handle overlapping communities.”