Force-directed algorithms as a tool to support community detection

Author(s)

Gouvea, Alessandra M. M. M. ^[1] ; da Silva, Tiago S. ^[1] ; Macau, Elbert E. N. ^{[1, 2]} ; Quiles, Marcos G. ^[1]

Affiliation

^[1] Univ Fed Sao Paulo, Sao Jose Dos Campos, SP - Brazil

^[2] Natl Inst Space Res, Sao Jose Dos Campos, SP - Brazil

• Document type: Review article
• Source: European Physical Journal-Special Topics; v. 230, n. 14-15, p. 2745-2763, OCT 2021.
• Web of Science Citations: 1

Abstract

Force-directed algorithms are a class of methods widely used to solve problems modeled via physics laws and resolved by particle simulation. Visualization of general graphs is one of the research fields which uses such algorithms and provides a vast knowledge about their benefits and challenges. Taking advantage of the knowledge provided by graph visualization theory, some authors have adopted force-directed algorithms as a tool to deal with the community detection problem. However, researches in that direction seem to be neglected by the literature of complex network. This paper explores the use of force-directed algorithms as a tool to solve the community detection problem. We revisit the works proposed in this area and point out the similarities, but mainly highlight the particularities of such a problem concerning the draw of a general graph. This literature review aims to organize the knowledge about the subject and highlight the state-of-the-art. To conduct our review, we followed a research protocol inspired by systematic review guidelines. Our review exposes that many works have chosen models that are not ideal for dealing with the community detection problem. Furthermore, we also highlight the most appropriate force-directed models for community detection. (AU)

• FAPESP's process: 16/16291-2 - Characterizing time-varying networks: methods and applications
• Grantee: Marcos Gonçalves Quiles
• Support Opportunities: Scholarships abroad - Research
• FAPESP's process: 19/26283-5 - Learning visual clues of the passage of time
• Grantee: Didier Augusto Vega Oliveros
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 16/23698-1 - Dynamical Processes in Complex Network based on Machine Learning
• Grantee: Didier Augusto Vega Oliveros
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 17/05831-9 - Analysis of climate indexes influence on wildfires using complex networks and data mining
• Grantee: Leonardo Nascimento Ferreira
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 19/00157-3 - Association and causality analyses between climate and wildfires using network science
• Grantee: Leonardo Nascimento Ferreira
• Support Opportunities: Scholarships abroad - Research Internship - Post-doctor
• FAPESP's process: 16/23642-6 - Characterization of Time-Varying Complex Networks
• Grantee: Alessandra Marli Maria Morais Gouvêa
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 15/50122-0 - Dynamic phenomena in complex networks: basics and applications
• Grantee: Elbert Einstein Nehrer Macau
• Support Opportunities: Research Projects - Thematic Grants