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.
Elbert E. N. Macau
Full Professor
Marcos G. Quiles
Associate Professor
My research interests include neural networks, machine learning, complex networks, and their applications in interdisciplinary problems, such as materials science and social networks.