Modularity with a more accurate baseline model

We derive an expression for the exact probability Pr[i∼j] of a link between a node i with degree d_{i} and a node j with degree d_{j} in a graph belonging to the class of Erdős-Rényi G(N,L) random graphs with N nodes and L links. The probability Pr[i∼j] is commonly approximated as d_{i}d_{j}/2L and appears in the formula of Newman's modularity, which plays a crucial rule in community detection in networks. We show that, when applied to graphs not belonging to the class of Erdős-Rényi random graphs, our formula for Pr[i∼j] is considerably more accurate than d_{i}d_{j}/2L and leads to the detection of different clusters or partitions than the original modularity formula.

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Institutions

Publication Details

Journal
Physical review. E
Published
2025-04-25
DOI
https://doi.org/10.1103/physreve.111.044317
Citations
1
Primary Topic
Complex Network Analysis Techniques
Type
article
Field-Weighted Citation Impact
1.32

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article

Modularity with a more accurate baseline model

Piet Van Mieghem, Brian L. Chang
1 citations
Physical review. E
Complex Network Analysis Techniques
1.32
article

Modularity with a more accurate baseline model

Piet Van Mieghem, Brian L. Chang
article en
1 citations

Abstract

We derive an expression for the exact probability Pr[i∼j] of a link between a node i with degree d_{i} and a node j with degree d_{j} in a graph belonging to the class of Erdős-Rényi G(N,L) random graphs with N nodes and L links. The probability Pr[i∼j] is commonly approximated as d_{i}d_{j}/2L and appears in the formula of Newman's modularity, which plays a crucial rule in community detection in networks. We show that, when applied to graphs not belonging to the class of Erdős-Rényi random graphs, our formula for Pr[i∼j] is considerably more accurate than d_{i}d_{j}/2L and leads to the detection of different clusters or partitions than the original modularity formula.

Physical review. EVol. 111(4)
Delft University of Technology (NL)
European Research Council, Horizon 2020
Openalex Percentile: Top 20%
Complex Network Analysis Techniques
1.32
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