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Algorithms
Special Issues
Optimization Algorithms for Graphs and Complex Networks
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Optimization Algorithms for Graphs and Complex Networks

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Combinatorial Optimization, Graph, and Network Algorithms".

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 4770

Special Issue Editor

Prof. Dr. Chris Walshaw

E-Mail Website
Guest Editor
School of Computing and Mathematical Sciences, University of Greenwich, London SE10 9LS, UK
Interests: combinatorial optimisation; multilevel refinement; graph partitioning; graph visualisation; music similarity

Special Issue Information

Dear Colleagues,

Researchers are warmly invited to submit high-quality papers to this Special Issue on “Optimization Algorithms for Graphs and Complex Networks”.

Since their first appearance in the 18th century graphs have had a long history as a branch of mathematics dealing with relationships. This history has been revitalised in the 21st century with the increased interest in complex networks and other graphs with non-trivial topological features. This special issue is dedicated to optimisation algorithms that can be formulated on such graphs and networks.

Papers may cover theory and/or applications, including but not limited to:

  • Tree algorithms
  • Path algorithms
  • Flow algorithms
  • Matching algorithms
  • Clustering, partitioning & colouring algorithms
  • Graph drawing & layout algorithms
  • Graph metaheuristics
  • Multilevel & hierarchical algorithms

Dr. Chris Walshaw
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Combinatorial optimization
  • Graph theory
  • Complex networks

Published Papers (2 papers)

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Research

20 pages, 516 KiB  
Article
Computing the Atom Graph of a Graph and the Union Join Graph of a Hypergraph
by Anne Berry and Geneviève Simonet
Algorithms 2021, 14(12), 347; https://doi.org/10.3390/a14120347 - 28 Nov 2021
Cited by 1 | Viewed by 2178
Abstract
The atom graph of a graph is a graph whose vertices are the atoms obtained by clique minimal separator decomposition of this graph, and whose edges are the edges of all possible atom trees of this graph. We provide two efficient algorithms for [...] Read more.
The atom graph of a graph is a graph whose vertices are the atoms obtained by clique minimal separator decomposition of this graph, and whose edges are the edges of all possible atom trees of this graph. We provide two efficient algorithms for computing this atom graph, with a complexity in O(min(nωlogn,nm,n(n+m¯)) time, where n is the number of vertices of G, m is the number of its edges, m¯ is the number of edges of the complement of G, and ω, also denoted by α in the literature, is a real number, such that O(nω) is the best known time complexity for matrix multiplication, whose current value is 2,3728596. This time complexity is no more than the time complexity of computing the atoms in the general case. We extend our results to α-acyclic hypergraphs, which are hypergraphs having at least one join tree, a join tree of an hypergraph being defined by its hyperedges in the same way as an atom tree of a graph is defined by its atoms. We introduce the notion of union join graph, which is the union of all possible join trees; we apply our algorithms for atom graphs to efficiently compute union join graphs. Full article
(This article belongs to the Special Issue Optimization Algorithms for Graphs and Complex Networks)
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14 pages, 3108 KiB  
Article
Detect Overlapping Community Based on the Combination of Local Expansion and Label Propagation
by Xu Li and Qiming Sun
Algorithms 2021, 14(8), 237; https://doi.org/10.3390/a14080237 - 11 Aug 2021
Cited by 1 | Viewed by 1845
Abstract
It is a common phenomenon in real life that individuals have diverse member relationships in different social clusters, which is called overlap in the science of network. Detecting overlapping components of the community structure in a network has extensive value in real-life applications. [...] Read more.
It is a common phenomenon in real life that individuals have diverse member relationships in different social clusters, which is called overlap in the science of network. Detecting overlapping components of the community structure in a network has extensive value in real-life applications. The mainstream algorithms for community detection generally focus on optimization of a global or local static metric. These algorithms are often not good when the community characteristics are diverse. In addition, there is a lot of randomness in the process of the algorithm. We proposed a algorithm combining local expansion and label propagation. In the stage of local expansion, the seed is determined by the node pair with the largest closeness, and the rule of expansion also depends on closeness. Local expansion is just to obtain the center of expected communities instead of final communities, and these immature communities leave only dense regions after pruning according to certain rules. Taking the dense regions as the source makes the label propagation reach stability rapidly in the early propagation so that the final communities are detected more accurately. The experiments in synthetic and real-world networks proved that our algorithm is more effective not only on the whole, but also at the level of the node. In addition, it is stable in the face of different network structures and can maintain high accuracy. Full article
(This article belongs to the Special Issue Optimization Algorithms for Graphs and Complex Networks)
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