A generalised significance test for individual communities in networks

  title={A generalised significance test for individual communities in networks},
  author={Sadamori Kojaku and Naoki Masuda},
  journal={Scientific Reports},
Many empirical networks have community structure, in which nodes are densely interconnected within each community (i.e., a group of nodes) and sparsely across different communities. Like other local and meso-scale structure of networks, communities are generally heterogeneous in various aspects such as the size, density of edges, connectivity to other communities and significance. In the present study, we propose a method to statistically test the significance of individual communities in a… 

Computing the statistical significance of optimized communities in networks

A new significance scoring algorithm called Fast Optimized Community Significance (FOCS) is introduced that is highly scalable and agnostic to the type of graph, and better balances the trade-off between detection power and false positives.

Binomial Tails for Community Analysis

Simple efficient group scoring functions derived from tail probabilities using binomial models are developed and two other applications for community analysis are described: degree of community membership (which in turn yields group-scoring functions), and the discovery of significant edges in the community-induced graph.

ROBustness In Network (robin): an R Package for Comparison and Validation of Communities

Robin (ROBustness In Network), an R package to assess the robustness of the community structure of a network found by one or more methods to give indications about their reliability.

Computing exact P-values for community detection

An analytical solution to calculate the exact p-value of a single community with the Erdös–Rényi model is presented and a local search method for finding statistically significant communities based on the p -value minimization is proposed.

A model-agnostic hypothesis test for community structure and homophily in networks

This work proposes a simple and interpretable test statistic leveraging this homophily parameter and formulate both asymptotic and bootstrap-based rejection thresholds, and proves it outperforms benchmark methods on both simulated and real world data.

Generating Ensembles of Gene Regulatory Networks to Assess Robustness of Disease Modules

Constrained Random Alteration of Network Edges (CRANE), a computational method that samples networks with fixed node strengths to identify a null distribution and assess the robustness of observed changes in network structure, produces more biologically realistic results and performs better in simulations.

Detecting mesoscale structures by surprise

This study presents a statistically validated method for mesoscale structures detection based upon the score function called ‘surprise’, and attaches to the paper a Python code implementing all variants of surprise discussed in the present manuscript.

Detecting Statistically Significant Communities

A tight upper bound is presented on the inline-formula-value of a single community under the configuration model, which can be used for quantifying the statistical significance of each community analytically, and a local search method to detect statistically significant communities in an iterative manner is presented.

Visualizing novel connections and genetic similarities across diseases using a network-medicine based approach

This work uses a network-based approach to evaluate shared variants among thousands of traits in the GWAS Catalog repository and indicates many more novel disease relationships that did not exist in early studies and demonstrates that the network can reveal clusters of diseases mechanistically related.

(2019). Multiscale core-periphery structure in a global liner shipping network. Scientific Reports , , [404].

Maritime transport accounts for a majority of trades in volume, of which 70% in value is carried by container ships that transit regular routes on fixed schedules in the ocean. In the present paper,



Finding Statistically Significant Communities in Networks

OSLOM (Order Statistics Local Optimization Method), the first method capable to detect clusters in networks accounting for edge directions, edge weights, overlapping communities, hierarchies and community dynamics, is presented.

Community extraction for social networks

A new framework is proposed that extracts one community at a time, allowing for arbitrary structure in the remainder of the network, which can include weakly connected nodes, and establishes asymptotic consistency of estimated node labels.

Finding community structure in networks using the eigenvectors of matrices.

  • M. Newman
  • Computer Science
    Physical 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.

Statistical significance of communities in networks.

A measure aimed at quantifying the statistical significance of single communities is defined, which is successfully applied in the case of real-world networks for the evaluation of the significance of their communities.

Empirical comparison of algorithms for network community detection

Considering community quality as a function of its size provides a much finer lens with which to examine community detection algorithms, since objective functions and approximation algorithms often have non-obvious size-dependent behavior.

Defining and evaluating network communities based on ground-truth

It is argued that the goal of network community detection is to extract functional communities based on the connectivity structure of the nodes in the network, and a methodology is proposed, which allows for compare and quantitatively evaluate how different structural definitions of communities correspond to ground-truth functional communities.

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This work introduces a class of benchmark graphs, that account for the heterogeneity in the distributions of node degrees and of community sizes, and uses this benchmark to test two popular methods of community detection, modularity optimization, and Potts model clustering.

Scalable detection of statistically significant communities and hierarchies, using message passing for modularity

  • Pan ZhangC. Moore
  • Computer Science
    Proceedings of the National Academy of Sciences
  • 2014
By applying the proposed algorithm recursively, subdividing communities until no statistically significant subcommunities can be found, it is shown that the algorithm can detect hierarchical structure in real-world networks more efficiently than previous methods.

Random Walks, Markov Processes and the Multiscale Modular Organization of Complex Networks

This dynamic framework creates a systematic link between different stochastic dynamics and their corresponding notions of optimal communities under distinct (node and edge) centralities and shows that the Markov Stability can be computed efficiently to find multi-scale community structure in large networks.

Community detection in graphs