Modularity and community structure in networks
Bibliographic Data
| ID | 23312972 |
|---|---|
| Authors | M E J Newman (0000-0002-0907-1660, University of Michigan, corresponding author) |
| Year | 2006 |
| Volume | 103 |
| Issue | 23 |
| Pages | 8577-8582 |
| Publication date | 2006-06-06 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Proceedings of the National Academy of Sciences (JOURNAL) |
| Journal identifiers | ISSN: 0027-8424 • E-ISSN: 1091-6490 |
| Publisher | National Academy of Sciences (PUBLISHER • US) |
| DOI | 10.1073/pnas.0601602103 |
| PMID | 16723398 |
| PMCID | PMC1482622 |
| OpenAlex | W2151936673 |
| Language | EN |
| Citations received | 653 |
| References cited | 28 |
Many networks of interest in the sciences, including social networks, computer networks, and metabolic and regulatory networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure is one of the outstanding issues in the study of networked systems. One highly effective approach is the optimization of the quality function known as “modularity” over the possible divisions of a network. Here I show that the modularity can be expressed in terms of the eigenvectors of a characteristic matrix for the network, which I call the modularity matrix, and that this expression leads to a spectral algorithm for community detection that returns results of demonstrably higher quality than competing methods in shorter running times. I illustrate the method with applications to several published network data sets.
Biology · Clique percolation method · Community structure · Complex network · Data mining · Data science · Eigenvalues and eigenvectors · Function (biology) · Modularity (biology) · Network analysis · Quality (philosophy) · World Wide Web · Artificial Intelligence · Bioinformatics and Genomic Networks · Complex Network Analysis Techniques · Computer Science · Engineering · Mathematics · Opinion Dynamics and Social Influence · Theoretical Computer Science
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| Unique citing works | 653 |
|---|---|
| Citations per year | 11,87 |
| Citation span | 1971 - 2026 (56) |
| Citation velocity | current |
| Highly cited | Yes |
| Citation types | Neutral: 600 |