{rfName}
Co

Indexed in

License and Use

Icono OpenAccess

Citations

14

Altmetrics

Impact on the Sustainable Development Goals (SDGs)

Analysis of institutional authors

Muñoz HCorresponding AuthorMateos AAuthorJiménez-Martín AAuthor

Share

March 4, 2021
Publications
>
Article
Sí

Convgraph: Community detection of homogeneous relationships in weighted graphs

Publicated to: Mathematics. 9 (4): 367-18 - 2021-02-01 9(4), DOI: 10.3390/math9040367

Authors:

Munoz, Hector; Vicente, Eloy; Gonzalez, Ignacio; Mateos, Alfonso; Jimenez-Martin, Antonio
[+]

Affiliations

Universidad Politécnica de Madrid - Author
‎ Univ Politecn Madrid, Dept Inteligencia Artificial, Decis Anal & Stat Grp, Madrid 28040, Spain - Author

Abstract

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This paper proposes a new method, ConvGraph, to detect communities in highly cohesive and isolated weighted graphs, where the sum of the weights is significantly higher inside than outside the communities. The method starts by transforming the original graph into a line graph to apply a convolution, a common technique in the computer vision field. Although this technique was originally conceived to detect the optimum edge in images, it is used here to detect the optimum edges in communities identified by their weights rather than by their topology. The method includes a final refinement step applied to communities with a high vertex density that could not be detected in the first phase. The proposed algorithm was tested on a series of highly cohesive and isolated synthetic graphs and on a real-world export graph, performing well in both cases.
[+]

Keywords

Community detectionConvolutionLine graphStrengthen the means of implementation and revitalize the global partnership for sustainable development goals

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Mathematics due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2021, it was in position 21/333, thus managing to position itself as a Q1 (Primer Cuartil), in the category Mathematics. Notably, the journal is positioned above the 90th percentile.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-28:

  • Google Scholar: 9
  • WoS: 2
  • Scopus: 2
[+]

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-28:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 9.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 9 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 1.
  • The number of mentions on the social network X (formerly Twitter): 1 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/85770/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 119
  • Downloads: 31
Continuing with the social impact of the work, it is important to emphasize that, due to its content, it can be assigned to the area of interest of ODS 17 - Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development, with a probability of 42% according to the mBERT algorithm developed by Aurora University.
[+]

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (MUÑOZ GARCÍA, HÉCTOR) and Last Author (JIMENEZ MARTIN, ANTONIO).

the author responsible for correspondence tasks has been MUÑOZ GARCÍA, HÉCTOR.

[+]

Project objectives

Los objetivos perseguidos en esta aportación se centran en mejorar la detección de comunidades en grafos ponderados. Se pretende desarrollar un método, ConvGraph, para detectar comunidades altamente cohesivas y aisladas, caracterizadas por una suma de pesos significativamente mayor dentro que fuera de ellas. Se busca transformar el grafo original en un grafo línea para aplicar convoluciones, adaptando técnicas de visión por computador para identificar aristas óptimas basadas en pesos y no en topología. Además, se aspira a incluir un paso de refinamiento final para comunidades con alta densidad de vértices no detectadas inicialmente. Finalmente, se pretende evaluar el desempeño del algoritmo en grafos sintéticos y en un grafo real de exportación.
[+]

Most relevant results

Los resultados más relevantes del estudio son los siguientes: se desarrolló el método ConvGraph para detectar comunidades en grafos ponderados altamente cohesivos y aislados, destacando que la suma de pesos es significativamente mayor dentro que fuera de las comunidades; el método transforma el grafo original en un grafo de líneas para aplicar una convolución, técnica adaptada del campo de la visión por computadora; se implementó un paso de refinamiento final para comunidades con alta densidad de vértices no detectadas inicialmente; y la eficacia del algoritmo fue validada tanto en grafos sintéticos altamente cohesivos como en un grafo real de exportación, mostrando un rendimiento satisfactorio en ambos casos.
[+]