Identifying the influential nodes in complex social networks using centrality-based approach

Ishfaq, Umar; Khan, Hikmat Ullah; Iqbal, Saqib

doi:10.1016/j.jksuci.2022.09.016

Cited by 22 publications

(9 citation statements)

References 41 publications

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“…Authors designed the Isolating Clustering Distance Centrality (ICDC) [31] by integrating the isolating and clustering coefficient centrality measures and shown that proposed measure exhibits better spreading efficiency over conventional measures. A new methodology is introduced in [32] that utilizes TOPSIS ranking and entropy weighting to integrate the various centrality measures. The model outperforms single-criteria techniques in analyzing the node significance with respect to real-world networks.…”

Section: Related Workmentioning

confidence: 99%

Identifying and Ranking of Best Influential Spreaders With Extended Clustering Coefficient Local Global Centrality Method

Chiranjeevi,

Dhuli,

Enduri

et al. 2024

IEEE Access

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The detection and ranking of influential nodes in complex networks are crucial for various practical applications such as identifying potential drug targets in protein-to-protein interaction networks, critical devices in communication networks, key people in social networks, and transportation hubs in logistics networks. The knowledge of influential spreaders in complex networks is extremely useful for controlling the spread of information. Centrality measures are known for effectively quantifying the influential nodes information in large-scale complex networks. Researchers have proposed different centrality measures in the literature, including Degree, Betweenness, Closeness, and Clustering coefficient centralities. However, these measures have certain limitations when implemented over large-scale complex networks. Most of these measures can be classified as global and local structural approaches. The global structure based algorithms are too complex to evaluate key nodes, particularly in large-scale networks, whereas the local measures overlook the essential global network information. To address these challenges, an extended clustering coefficient local global centrality (ECLGC) is proposed, which combines the local and global structural information to measure the node's influence in large-scale networks. The effectiveness and computational efficiency of the proposed measure are compared with existing centrality measures on real-world network datasets. Susceptible-Infected-Recovered (SIR) model is utilized to evaluate the performance of the ECLGC to capture the high-information dissemination compared to conventional measures. Further, we demonstrate that the proposed measure outperforms the conventional measures in terms of spreading efficiency. INDEX TERMS Influential nodes, complex networks, extended clustering coefficient local global centrality, clustering coefficient VOLUME x, 2022

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Section: Related Workmentioning

confidence: 99%

Identifying and Ranking of Best Influential Spreaders With Extended Clustering Coefficient Local Global Centrality Method

Chiranjeevi,

Dhuli,

Enduri

et al. 2024

IEEE Access

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“…According to topological criteria, influential users can typically be determined by their ranking. 8 As a result, developing an effective topological measuring algorithm is crucial for identifying prominent members in complex networks. 9 Since most networks lack sufficient social network data, comparing published methodologies might be a difficult task.…”

Section: Introductionmentioning

confidence: 99%

“…A crucial task that depends on user interactions' topology is searching for the best group of important users. According to topological criteria, influential users can typically be determined by their ranking 8 . As a result, developing an effective topological measuring algorithm is crucial for identifying prominent members in complex networks 9 .…”

Section: Introductionmentioning

confidence: 99%

HIKS: A K‐shell‐weighted hybrid approach method for detecting influential nodes in complex networks using possible edge weights

Seshu Chakravarthy,

Selvaraj

2024

Int J Communication

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SummaryThe influential node in the network is the node that has a higher impact on network functioning compared to the other nodes. The influential node detection in the complex network is crucial for rumor containment, virus spreading, viral marketing, and so forth. The researchers designed several influential node detection methods; still, detecting community and influential node selection with minimal computational complexity by considering the relationship between the nodes is challenging. Hence, an optimal community detection along with the hybrid K‐shell decomposition method is introduced in this research. Initially, the optimal community from the complex network is identified to reduce the computation burden. For this, the Improved Dingo (IDingo) algorithm is introduced by hybridizing the hunting behavior of Dingo and the rough encircling behavior of Harris Hawk. After detecting the optimal community, the influential node identification is devised using the proposed hybrid K‐shell decomposition methods. The potential edge weights are considered while ranking the nodes. The performance of a proposed method is analyzed using six various datasets and accomplished the maximal cluster coefficient of 0.56578, 0.25674, 0.24022, 0.5968, 0.23419, and 0.10196 for Karate, Dolphins, C‐Elegance, Facebook, Gowalla, and Power Grid Dataset.

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“…Overall, using a combination of centrality measures and other types of data is poised to be a powerful approach for node prioritization, as it allows for a more comprehensive evaluation of a node’s importance and role in a network [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Ranking Plant Network Nodes Based on Their Centrality Measures

Kumar

Mukhtar

2023

Entropy

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Biological networks are often large and complex, making it difficult to accurately identify the most important nodes. Node prioritization algorithms are used to identify the most influential nodes in a biological network by considering their relationships with other nodes. These algorithms can help us understand the functioning of the network and the role of individual nodes. We developed CentralityCosDist, an algorithm that ranks nodes based on a combination of centrality measures and seed nodes. We applied this and four other algorithms to protein–protein interactions and co-expression patterns in Arabidopsis thaliana using pathogen effector targets as seed nodes. The accuracy of the algorithms was evaluated through functional enrichment analysis of the top 10 nodes identified by each algorithm. Most enriched terms were similar across algorithms, except for DIAMOnD. CentralityCosDist identified more plant–pathogen interactions and related functions and pathways compared to the other algorithms.

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Identifying the influential nodes in complex social networks using centrality-based approach

Cited by 22 publications

References 41 publications

Identifying and Ranking of Best Influential Spreaders With Extended Clustering Coefficient Local Global Centrality Method

Identifying and Ranking of Best Influential Spreaders With Extended Clustering Coefficient Local Global Centrality Method

HIKS: A K‐shell‐weighted hybrid approach method for detecting influential nodes in complex networks using possible edge weights

Ranking Plant Network Nodes Based on Their Centrality Measures

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