# Finding and evaluating community structure in networks.

@article{Newman2004FindingAE, title={Finding and evaluating community structure in networks.}, author={Mark E. J. Newman and Michelle Girvan}, journal={Physical review. E, Statistical, nonlinear, and soft matter physics}, year={2004}, volume={69 2 Pt 2}, pages={ 026113 } }

We propose and study a set of algorithms for discovering community structure in networks-natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using any one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a…

## 11,956 Citations

### Detecting community structure of complex networks by affinity propagation

- Computer Science2009 IEEE International Conference on Intelligent Computing and Intelligent Systems
- 2009

A clustering method called affinity propagation, associating with some existent measures on graphs, such as the shortest path, the diffusion distance and the dissimilarity index, is utilized to solve the network partitioning problem.

### Detecting Overlapping Community Structures in Networks with Global Partition and Local Expansion

- Computer ScienceAPWeb
- 2008

This work proposes a novel algorithm for finding overlapping community structures from a network that can be divided into two phases: globally collect proper seeds from which the communities are derived in next step and randomly walk over the network from the seeds by a well designed local optimization process.

### Correlation Analysis of Nodes Identifies Real Communities in Networks

- Computer Science
- 2018

A simple and effective algorithm that uses the correlation of nodes alone, which requires neither optimization of predefined objective function nor information about the number or sizes of communities is proposed.

### An Algorithm to Find Overlapping Community Structure in Networks

- Computer SciencePKDD
- 2007

A new algorithm for discovering overlapping communities in networks is presented, by extending Girvan and Newman's well-known algorithm based on the betweennesscentrality measure, which performs hierarchical clustering -- partitioning a network into any desired number of clusters -- but allows them to overlap.

### Identifying different community members in complex networks based on topology potential

- Computer ScienceFrontiers of Computer Science in China
- 2010

A new method is proposed to divide networks into separate communities by spreading outward from each local important element and extracting its neighbors within the same group in each spreading operation.

### Finding community structure in networks using the eigenvectors of matrices.

- Computer SciencePhysical review. E, Statistical, nonlinear, and soft matter physics
- 2006

A modularity matrix plays a role in community detection similar to that played by the graph Laplacian in graph partitioning calculations, and a spectral measure of bipartite structure in networks and a centrality measure that identifies vertices that occupy central positions within the communities to which they belong are proposed.

### Discovering communities in complex networks

- Computer ScienceACM-SE 44
- 2006

This paper performs a brief survey of the existing community-discovery algorithms and proposes a novel approach to discovering communities using bibliographic metrics and tests the proposed algorithm on real-world networks and on computer-generated models with known community structures.

### Algorithms for community and role detection in networks

- Computer Science
- 2014

This work considers a generalization of the problem of community detection, termed role extraction, which does not use any prior assumption on the links distribution in the graph and describes a similarity measure based on the number of common neighbors between each pair of nodes that has interesting properties that reveal characteristics of the role structure in benchmark and real graphs.

### Identification and Evaluation of Weak Community Structures in Networks

- Computer ScienceAAAI
- 2006

This paper introduces a set of simple operations that capture local neighborhood information of a node to identify weak communities in networks, and considers the issue of automatically determining the most appropriate number of communities, a crucial problem for all clustering methods.

## References

SHOWING 1-10 OF 65 REFERENCES

### Community structure in social and biological networks

- Computer ScienceProceedings of the National Academy of Sciences of the United States of America
- 2002

This article proposes a method for detecting communities, built around the idea of using centrality indices to find community boundaries, and tests it on computer-generated and real-world graphs whose community structure is already known and finds that the method detects this known structure with high sensitivity and reliability.

### Finding Communities of Related Genes

- Computer Science
- 2002

An automated method of identifying communities of functionally related genes from the biomedical literature, using graphs to represent the network of gene cooccurrences in articles mentioning particular keywords, which finds that these graphs consist of one giant connected component and many small ones.

### Self-similar community structure in a network of human interactions.

- Computer SciencePhysical review. E, Statistical, nonlinear, and soft matter physics
- 2003

The results reveal the self-organization of the network into a state where the distribution of community sizes is self-similar, suggesting that a universal mechanism, responsible for emergence of scaling in other self-organized complex systems, as, for instance, river networks, could also be the underlying driving force in the formation and evolution of social networks.

### Subnetwork hierarchies of biochemical pathways

- Computer ScienceBioinform.
- 2003

A method to decompose biochemical networks into subnetworks based on the global geometry of the network is presented and is applied to 43 organisms from the WIT database.

### Mixing patterns in networks.

- Computer SciencePhysical review. E, Statistical, nonlinear, and soft matter physics
- 2003

This work proposes a number of measures of assortative mixing appropriate to the various mixing types, and applies them to a variety of real-world networks, showing that assortsative mixing is a pervasive phenomenon found in many networks.

### The structure of scientific collaboration networks.

- PhysicsProceedings of the National Academy of Sciences of the United States of America
- 2001

It is shown that these collaboration networks form "small worlds," in which randomly chosen pairs of scientists are typically separated by only a short path of intermediate acquaintances.

### The Structure and Function of Complex Networks

- Computer ScienceSIAM Rev.
- 2003

Developments in this field are reviewed, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

### A faster algorithm for betweenness centrality

- Computer Science
- 2001

New algorithms for betweenness are introduced in this paper and require O(n + m) space and run in O(nm) and O( nm + n2 log n) time on unweighted and weighted networks, respectively, where m is the number of links.

### Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality.

- Computer SciencePhysical review. E, Statistical, nonlinear, and soft matter physics
- 2001

It is argued that simple networks such as these cannot capture variation in the strength of collaborative ties and proposed a measure of collaboration strength based on the number of papers coauthored by pairs of scientists, and thenumber of other scientists with whom they coauthored those papers.

### E-Mail as Spectroscopy: Automated Discovery of Community Structure within Organizations

- Computer ScienceInf. Soc.
- 2005

A betweenness centrality algorithm is used that can rapidly find communities within a graph representing information flows and is effective at identifying true communities, both formal and informal, within these scale-free graphs.