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Questions tagged [modularity]

A measure of the strength of a network's division into modules (also called clusters, communities, or cliques). Modular networks have higher connectivity within modules and low connectivity between modules. Modularity is often employed as an optimization criterion in algorithms for detecting community structure in networks.

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Does the weighted Louvain algorithm for maximizing Modularity result in one of the modules containing low weight edges for a fully connected network?

I currently have an implementation of the Louvain Algorithm from the Brain Connectivity Toolbox (BCT) written by Rubinov and Sporns 2010. I was discussing the implementation of it with a professor who ...
Syuma's user avatar
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Is there a meaningful way to 'quantify' a group representative partition of its network when subjects within group have their own unique partitions?

I currently have a dataset which can be split into two groups: disease vs control. Each group consists of $n_{disease}$ and $n_{control}$ subjects respectively. The dataset itself is a correlation ...
Syuma's user avatar
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representative nodes in modular network

I want to find the most representative nodes in each module in a modular network. I have used the Louvain algorithm on my graph and found two main modules. Now I want to know what nodes are the most ...
saghi's user avatar
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What are the advantages of Louvain method versus K-means for clustering sparse data?

I would like to better understand the strengths of the Louvain method versus K-means for high-dimensional sparse data (e.g. zero-inflated negative binomial gene expression counts or natural language ...
Borlaug's user avatar
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Network modularity for exactly two communities

I have a number of empirical networks that tend to show bipolarization patterns, meaning that there are precisely two communities. In some networks, the two communities are very clearly separated (...
Philip Leifeld's user avatar
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Can the Louvain Modularity algorithm create communities of unconnected nodes?

I have an implementation of Louvain that is creating communities with unconnected nodes. I want to know if I am doing Louvain wrong or if this is possible in a correct implementation of Louvain? ...
Jeff Diederiks's user avatar
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Interpretation of Modularity in R

I used modularity maximization cluster method to make 3 different clusters (4, 5 and 6 clusters) from a network using "Igraph" in R. I found the modularity of the three networks are 0.15, 0.23, and 0....
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Transforming dissimilarity matrix for use as weights in network

I'm trying to identify communities or clusters in a network by optimizing the modularity function. The network is a fully connected network, with undirected edges that have weights based on a pairwise ...
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Clustering network usign modularity maximization algorithm

I have been working on a Network-based clustering approach. I used "cluster_optimal" of 'igraph' package in R for clustering. The function works by modularity maximization algorithm. I have ...
Bloodstone Programmer's user avatar
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How to design a complex machine learning system where individual classifiers can be retrained without modifying rest of the system?

I am designing a machine learning system which consists a bunch of classifiers (each output a confidence score between 0 to 1). Some classifiers consume output from other classifiers as features. Now ...
Shubajit Saha's user avatar
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Community detection in network

I'm fairly new to the subject of network theory and community detection, and I'm trying to apply to some data that I have. To start, my data essentially looks like this: Basically, what I have is a ...
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Community detection and modularity

I am reading the book "Network science" of Barabasi and in particular the chapter on community detection. If I understand correctly, modularity is a goodness factor of partition calculated by a ...
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What approaches use multiple eigenvectors in graph spectral clustering?

Background: In Newman's PNAS 2006 paper Modularity and community structure in networks, the first eigenvector splits the graph in two clusters, and then each cluster can be further divided by ...
highBandWidth's user avatar
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3 answers
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Does Newman's network modularity work for signed, weighted graphs?

The modularity of a graph is defined on its Wikipedia page. In a different post, somebody explained that modularity can easily be computed (and maximized) for weighted networks because the adjacency ...
Philip Leifeld's user avatar
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Modularity of graph: why are probabilities of self-loop included?

I'm trying to understand the Newman Modularity (doi:10.1073/pnas.0601602103) by investigating its calculation on the Wikipedia example . My question is why are probabilities of self-loops included in ...
Robert Rankin's user avatar
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How to conduct community division of a social network with R?

I am trying to use R to conduct community division within my weighted network (based from an association matrix). I tried with igraph but I encountered some problems. I usually use the program Socprog ...
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1 answer
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Interpreting output of igraph's fastgreedy.community clustering method

With the help of several people in this community I have been wetting my feet in clustering some social network data using igraph's implementation of modularity-based clustering. I am having some ...
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8 answers
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How to do community detection in a weighted social network/graph?

I'm wondering if someone could suggest what are good starting points when it comes to performing community detection/graph partitioning/clustering on a graph that has weighted, undirected edges. The ...
laramichaels's user avatar
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8 votes
2 answers
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Newman's modularity clustering for graphs

I am interested in running Newman's modularity clustering algorithm on a large graph. If you can point me to a library (or R package, etc) that implements it I would be most grateful.
laramichaels's user avatar
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