A review of heuristics and metaheuristics for community detection in complex networks: Current usage, emerging development and future directions

Attea, Bara'a Ali; Abbood, Amenah D.; Hasan, Ammar A.; Pizzuti, Clara; Al-Ani, Mayyadah; Özdemir, Suat; Al-Dabbagh, Rawaa Dawoud

doi:10.1016/j.swevo.2021.100885

Cited by 39 publications

(13 citation statements)

References 215 publications

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“…𝑆 𝑖𝑛 = ∑ 𝑀(𝑝 𝑖,𝑘 , 𝑝 𝑖𝑛,𝑘 ) 𝑖𝑛={𝑖𝑛𝑐𝑜} (3) Where 𝑖𝑛𝑐𝑜 are the proteins' indexes that are directly connected with 𝑝 𝑖,𝑘 and located in 𝑐 𝑐𝑢𝑟 . Likewise, calculate using Eq.…”

Section: 𝐽𝑆(𝑖mentioning

confidence: 99%

“…Biological networks of protein-protein interactions (PPINs) are one of the current trends that have been embraced in the field of cooperation between computer scientists and biologists. These special types of biological networks are used to model the physical interactions of a cell's components [1][2][3][4]. Recently, several methodologies have been employed to obtain a complete visualization of biological networks.…”

Section: Introductionmentioning

confidence: 99%

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Evolutionary-based Gene Ontology for Complex Detection in Protein-Protein Interaction Networks

Kadhim,

Al-Dabbagh

2024

Iraqi Journal of Science

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Complex detection in protein-protein interaction (PPI) networks is one of the major issues facing scientific study in biological networks. In PPINs, proteins are distributed differently as groups (complexes). These groups can be identified as having a great internal density in the number of edges inside the groups while having the least possible number of edges between these groups. The most common methods for finding such complexes are evolutionary algorithms (EAs), which have been used widely in literature for this objective. Despite the reliability of these complicated detection models, they are mostly based on topological (graph) qualities, and the biological implications of the PPI networks have been rarely explored. In this research, EA with mutation-based gene ontology is developed, particularly in the mutation part where the functional annotation of the protein has been considered using gene ontology structure. The experimental results prove the reliability of the proposed method using standard validation measures. It also outperforms the state-of-the-art method in terms of the prediction ability and quality of the complexes found.

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Section: 𝐽𝑆(𝑖mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evolutionary-based Gene Ontology for Complex Detection in Protein-Protein Interaction Networks

Kadhim,

Al-Dabbagh

2024

Iraqi Journal of Science

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“…Among the metaheuristic algorithms, evolutionary algorithms (EAs) are getting increased attention while supporting powerful performance in solving many real-world optimization problems. The key issues while considering the design of a new competitive EA for solving a particular real-world problem are 1) to formulate the problem with more robust (mostly multi-objective) optimization models, and 2) to develop a cross-fertilization mechanism for combining heuristic operators (that are tailored specifically to fit the problem) with the EA framework [21]. Several studies followed this rule in designing competitive EAs for solving different NPhard problems [22][23][24][25][26].…”

Section: Evolutionary Algorithms (The Canonical Framework)mentioning

confidence: 99%

Topology-Based Modularity and Modularity Density for Detecting Protein Complexes: A Comparative Study

Abdulsahib,

Attea

2024

Iraqi Journal of Science

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Binary relations or interactions among bio-entities, such as proteins, set up the essential part of any living biological system. Protein-protein interactions are usually structured in a graph data structure called "protein-protein interaction networks" (PPINs). Analysis of PPINs into complexes tries to lay out the significant knowledge needed to answer many unresolved questions, including how cells are organized and how proteins work. However, complex detection problems fall under the category of non-deterministic polynomial-time hard (NP-Hard) problems due to their computational complexity. To accommodate such combinatorial explosions, evolutionary algorithms (EAs) are proven effective alternatives to heuristics in solving NP-hard problems. The main aim of this study is to make a close examination of the performance of the EAs where modularity and modularity density are selected as two different objective functions. Topology-based modularity and topology-based modularity density are designed to examine the detection ability of the EAs and to compare their performance. To conduct the experiments, two yeast Saccharomyces cerevisiae PPINs are used and evaluated under nine evaluation metrics. The results reveal the potential impact of the topology-based modularity density to outperform the counterpart modularity functions in almost all evaluation metrics.

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“…Attea et al [43] 2021 Heuristics and metaheuristics-based community detection methods in social networks were reviewed and categorized. Current applications and future progress and trends were reviewed.…”

Section: Moscatomentioning

confidence: 99%

Community Detection Algorithms in Healthcare Applications: A Systematic Review

et al. 2023

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Over the past few years, the number and volume of data sources in healthcare databases has grown exponentially. Analyzing these voluminous medical data is both opportunity and challenge for knowledge discovery in health informatics. In the last decade, social network analysis techniques and community detection algorithms are being used more and more in scientific fields, including healthcare and medicine. While community detection algorithms have been widely used for social network analysis, a comprehensive review of its applications for healthcare in a way to benefit both health practitioners and the health informatics community is still overwhelmingly missing. This paper contributes to fill in this gap and provide a comprehensive and up-to-date literature research. Especially, categorizations of existing community detection algorithms are presented and discussed. Moreover, most applications of social network analysis and community detection algorithms in healthcare are reviewed and categorized. Finally, publicly available healthcare datasets, key challenges, and knowledge gaps in the field are studied and reviewed.

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A review of heuristics and metaheuristics for community detection in complex networks: Current usage, emerging development and future directions

Cited by 39 publications

References 215 publications

Evolutionary-based Gene Ontology for Complex Detection in Protein-Protein Interaction Networks

Evolutionary-based Gene Ontology for Complex Detection in Protein-Protein Interaction Networks

Topology-Based Modularity and Modularity Density for Detecting Protein Complexes: A Comparative Study

Community Detection Algorithms in Healthcare Applications: A Systematic Review

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