Identification of Influential Nodes via Effective Distance-based Centrality Mechanism in Complex Networks

Ullah, Aman; Wang, Bin; Sheng, Jinfang; Long, Jun; Khan, Nasrullah

doi:10.1155/2021/8403738

Cited by 23 publications

(5 citation statements)

References 67 publications

(74 reference statements)

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“…The comparison of the proposed method with the conventional influential node detection methods is devised in this section to indicate the superiority of a developed model. The conventional methods utilized for the Comparison are BIFACE, 38 EDBC, 39 KSH, 40 and SIR 41 . A short description of the comparative methods is depicted in Table 2.…”

Section: Resultsmentioning

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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Section: Resultsmentioning

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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“…A significant number of methods have been designed and disposed in efficient influential node identification where just a hardly have utilized centrality measures in addition with respect to their concerning drawbacks in their studies. To address these issues, an efficient distance-based centrality (EDBC) algorithm was designed in [13] for influential node identification, therefore contributing to accuracy and effectiveness. Yet another method to ensure software stability via change propagation was proposed in [14].…”

Section: Related Workmentioning

confidence: 99%

Copula Tubular Quantum Based Spearman Deep Neural for Influential Node Tracing in Social Network

2024

Preprint

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Social networks are one the foremost origins of information transmission at present. Nevertheless, not all nodes in social networks are indistinguishable. As a matter of fact, certain nodes are said to be more influential than others, or to be more specific, their information gravitates to proliferate more. Identifying the most influential nodes in a social network called as the Influence Maximization problem remains one of the hot issue with the evolution of Internet and social media. Several methods have been proposed to identify influential nodes in composite networks, ranging from parallel algorithm to distribution difference and non-overlapping communities. However, most of the previous methods do not take into account error involved in overlapping communities in social network. To address this issue, in this work, a Copula Tubular Neighborhood and Quantum-based Spearman Deep Neural Network (CTNQ-SDNN) for influential node tracing in social network is proposed. The CTNQ-SDNN method is split into three sections. First, computationally efficient nodes are identified by employing Copula Probability Node Pre-processing model. Then, with the identification of effective nodes, accurate feature extraction is made by means of Tubular Neighborhood Intensity-based Feature extraction model. Finally, influential node selection and tracing in case of tie or overlapping communities with the purpose of reducing error rate is proposed by utilizing Spearman Deep Neural Influential Node Tracing model. Experimental results on telecom dataset social networks, comparing our proposed method against state-of-the-art methods in current literature, indicates our method to be efficient and robust in tackling the influence maximization issue even in case of tie or overlapping communications in social network via error rate.

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“…Firstly, Kendall’s

correlation coefficient [ 47 ] is used to quantify the correlation between ranking list R obtained by each method and the ground-truth ranking list

obtained by the SIR model. The

coefficient is calculated as follows

where

refers to the sign function.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Identifying Influential Nodes in Complex Networks Based on Multiple Local Attributes and Information Entropy

Zhang

Qishan

et al. 2022

Entropy

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Identifying influential nodes in complex networks has attracted the attention of many researchers in recent years. However, due to the high time complexity, methods based on global attributes have become unsuitable for large-scale complex networks. In addition, compared with methods considering only a single attribute, considering multiple attributes can enhance the performance of the method used. Therefore, this paper proposes a new multiple local attributes-weighted centrality (LWC) based on information entropy, combining degree and clustering coefficient; both one-step and two-step neighborhood information are considered for evaluating the influence of nodes and identifying influential nodes in complex networks. Firstly, the influence of a node in a complex network is divided into direct influence and indirect influence. The degree and clustering coefficient are selected as direct influence measures. Secondly, based on the two direct influence measures, we define two indirect influence measures: two-hop degree and two-hop clustering coefficient. Then, the information entropy is used to weight the above four influence measures, and the LWC of each node is obtained by calculating the weighted sum of these measures. Finally, all the nodes are ranked based on the value of the LWC, and the influential nodes can be identified. The proposed LWC method is applied to identify influential nodes in four real-world networks and is compared with five well-known methods. The experimental results demonstrate the good performance of the proposed method on discrimination capability and accuracy.

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Identification of Influential Nodes via Effective Distance-based Centrality Mechanism in Complex Networks

Cited by 23 publications

References 67 publications

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

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

Copula Tubular Quantum Based Spearman Deep Neural for Influential Node Tracing in Social Network

Identifying Influential Nodes in Complex Networks Based on Multiple Local Attributes and Information Entropy

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