Community detection in networks: A user guide

  title={Community detection in networks: A user guide},
  author={Santo Fortunato and Darko Hric},

A Comparative Analysis of Community Detection Algorithms on Social Networks

In this study, performance of eight state-of-the-art graph clustering algorithms are demonstrated on small egocentric graphs, obtained from Facebook.

Community Detection in Social Networks Using Consensus Clustering

This work presents a new approach for the community detection problem by considering an ensemble of community detection methods, referred to as “Mitra”, and evaluates its approach against real and artificial datasets and compares its method with each one of the base methods.

An Empirical Study on Community Detection Algorithms

Different community detection algorithms are presented and their pros and cons are discussed and some of the research challenges in this area are stated.

The many facets of community detection in complex networks

A focused review of the different motivations that underpin community detection and highlights the different facets of community detection, which delineates the many lines of research and points out open directions and avenues for future research.

Parametric Classification of Dynamic Community Detection Techniques

This paper covers the classification of different community detection techniques in dynamic networks and then compares them on the basis of different features, e.g., parallelization, network models, community instability, temporal smoothness, etc.

A survey of community detection methods in multilayer networks

Overall comparisons of existing works and analyzed several representative algorithms provide a comprehensive understanding of community detection methods in multilayer networks, indicating that the promoting of algorithm efficiency and the extending for general multilayers networks are expected in the forthcoming studies.

Detection of Communities in Large Scale Networks

  • Baisakhi ChatterjeeH. N. Saha
  • Computer Science
    2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)
  • 2019
This paper examines a number of research involving detection of communities and summarises them based on their avenues of approach to solving the problem.

Dynamic Multi Level Approach for Community Detection

  • Suriana IsmailR. Ismail
  • Computer Science
    2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM)
  • 2021
The new algorithm, MuLAN, is more robust in providing the detection where it forms the basic group of members as its first level of community and the it will check whether the remaining members are also connected with each other which form a strong structure for the community.

Debiasing Community Detection: The Importance of Lowly Connected Nodes

An approach is introduced that is more inclusive for lowly connected users by incorporating them into larger groups and outperforms the existing state-of-the-art in terms of F1 and Jaccard similarity scores while reducing the bias towards low-degree users.

Local Community Detection Based on Small Cliques

This work considers the problem of finding a community locally around a seed node both in unweighted and weighted networks and shows that results both on synthetic as well as real-world networks can be significantly improved by starting from the largest clique in the neighborhood of the seed node.



Detecting community structure in networks

A number of more recent algorithms that appear to work well with real-world network data, including algorithms based on edge betweenness scores, on counts of short loops in networks and on voltage differences in resistor networks are described.

Metrics for Community Analysis: A Survey

A comprehensive and structured overview of the start-of-the-art metrics used for the detection and the evaluation of community structure and conducts experiments on synthetic and real-world networks to present a comparative analysis of these metrics in measuring the goodness of the underlying community structure.

Community detection in networks: Structural communities versus ground truth

It is shown that traditional community detection methods fail to find the metadata groups in many large networks, and that either the current modeling of community structure has to be substantially modified, or that metadata groups may not be recoverable from topology alone.

A classification for community discovery methods in complex networks

The aim of this survey is to provide a ‘user manual’ for the community discovery problem and to organize the main categories of community discovery methods based on the definition of community they adopt.

Improving the performance of algorithms to find communities in networks

It is shown that, if the number of clusters was known beforehand, standard methods, like modularity optimization, would considerably gain in accuracy, mitigating the severe resolution bias that undermines the reliability of the results of the original unconstrained version.

Comparative definition of community and corresponding identifying algorithm.

A comparative definition for community in networks is proposed, and the corresponding detecting algorithm works well in detecting communities, and it also gives a nice description of network division and group formation.

Clustering and Community Detection in Directed Networks: A Survey

Estimating the number of communities in a network

A mathematically principled approach for finding the number of communities in a network by maximizing the integrated likelihood of the observed network structure under an appropriate generative model is described.

Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities.

The basic ideas behind the previous benchmark are extended to generate directed and weighted networks with built-in community structure, and the possibility that nodes belong to more communities is considered, a feature occurring in real systems, such as social networks.

Community Detection in Networks with Node Attributes

This paper develops Communities from Edge Structure and Node Attributes (CESNA), an accurate and scalable algorithm for detecting overlapping communities in networks with node attributes that statistically models the interaction between the network structure and the node attributes, which leads to more accurate community detection as well as improved robustness in the presence of noise in thenetwork structure.