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3 Annals of Computer Science and Information Systems

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Toward an Optimal Solution to the Network Partitioning Problem

Ferdowsi Arman, Chenary Maryam Dehghan

Annals of Computer Science and Information Systems

2023

Vol. 35

111--117

This paper delves into the realm of community detection in network science and graph theory‎ ‎with the overarching objective of unraveling the underlying structures between nodes within a network‎. ‎In this pursuit‎, ‎we put forth a novel and comprehensive approach to ascertain the optimal solution to maximizing the renowned community quality metric known as Max-Min Modularity‎. ‎Through a series of experiments encompassing diverse case studies‎, ‎we substantiate the efficacy and validity of our proposed approach‎, ‎further bolstering its credibility‎.

An Integer Programming Approach Reinforced by a Message-passing Procedure for Detecting Dense Attributed Subgraphs

Ferdowsi Arman

Annals of Computer Science and Information Systems

2022

Vol. 30

569--576

Due to the growing reliance of various scientific and industrial problems on graphs, one of the recent challenging tasks, especially when dealing with graphs equipped with a set of nodal attributes, is discovering subgraphs consisting of highly interacting nodes with respect to the number of edges and the attributes' similarities. This paper proposes an approach based on integer programming modeling and the graph neural network message-passing manner for efficiently extracting these subgraphs. The experiments illustrate the proposed method's privilege over some alternative algorithms known so far, utilizing several well-known instances.

Discovering Communities in Networks: A Linear Programming Approach Using Max-Min Modularity

Ferdowsi Arman, Khanteymoori Alireza

Annals of Computer Science and Information Systems

2021

Vol. 25

329–-335

Community detection is a fundamental challenge in network science and graph theory that aims to reveal nodes' structures. ‎While most methods consider Modularity as a community quality measure‎, ‎Max-Min Modularity improves the accuracy of the measure by penalizing the Modularity quantity when unrelated nodes are in the same community‎. ‎In this paper‎, ‎we propose a community detection approach based on linear programming using Max-Min Modularity‎. ‎The experimental results show that our algorithm has a better performance than the previously known algorithms on some well-known instances‎.