Detecting fuzzy community structures in complex networks with a Potts model.
By J. Reichardt, Stefan Bornholdt. Physical Review Letters, 2004
541 citations recorded by the source corpus.
A fast community detection algorithm based on a q-state Potts model is presented. Communities (groups of densely interconnected nodes that are only loosely connected to the rest of the network) are found to coincide with the domains of equal spin value in the minima of a modified Potts spin glass Hamiltonian. Comparing global and local minima of the Hamiltonian allows for the detection of overlapping ("fuzzy") communities and quantifying the association of nodes with multiple communities as well as the robustness of a community. No prior knowledge of the number of communities has to be assumed.
Subfields: Statistical and Nonlinear Physics, Transportation
View Paper: https://www.semanticscholar.org/paper/8cf8abe94fe7c95e09d4b7d615cac839893ea744
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Statistical and Nonlinear Physics
- Santo Fortunato
- Jure Leskovec
- M. A. Porter
- Jaewon Yang
- Renaud Lambiotte
- Mark Newman
- Olaf Sporns
- Peter J. Mucha
In Transportation
- E. Bullmore
- Srinivasan Parthasarathy
- Jukka-Pekka Onnela
- José Fernando Ferreira Mendes
- Mark Newman
- Jure Leskovec
- Renaud Lambiotte
- Haixun Wang
Citations in the corpus
- Overlapping Communities Explain Core–Periphery Organization of Networks (2014, background): “The first network we examine is the network of American college football studied in [23, 51].”
- Communities, modules and large-scale structure in networks (2011, unknown): “Another large class exploits links between community structure and processes taking place on networks, such as random walks 44,45 , Potts models 46 or oscillator synchronization 47 .”
- Communities in Networks (2009, background): “important recent insight is that optimizing modularity is mathematically equivalent to minimizing the energy (i.e., finding the ground state of the Hamiltonian) of an infinite range q-state Potts model [94,95]. In a q-state Potts spin glass, each spin can have one of q states. The interaction energy b”
- Community detection in graphs (2009, methodology): “The general spin glass framework by Reichardt and Bornholdt ((Reichardt and Bornholdt, 2006a) and Section VI.”
- Finding community structure in networks using the eigenvectors of matrices. (2006, methodology): “In this paper we focus on one approach to community detection that has proven particularly effective, the optimization of the benefit function known as “modu-larity” over the possible divisions of a network.”
- Link communities reveal multiscale complexity in networks (2009, background): “6,7,9,10,14,15 Pervasive overlap in real networks is distinct from ‘fuzzy’ community overlap with relaxed interfaces, 19–21 because overlap can exist for each and every node (Fig.”
- Modular and Hierarchically Modular Organization of Brain Networks (2010, background): “The most popular multi-scale quality function is the spin-glass modularity (Reichardt and Bornholdt, 2004) whose optimization reveals modules of different characteristic sizes when its resolution parameter is adjusted.”
- Critical phenomena in complex networks (2007, methodology): “Reichardt and Bornholdt (2004, 2006) used the configuration model as the null model, i.e., pij = qiqj/2L where qi and qj are degrees of vertices i and j respectively.”
- Local search of communities in large graphs (2014, methodology): “Some efficient approaches for the identification of overlapping communities in large real network have been proposed, including clique percolation method [20], q-state Potts model based method [21].”