Finding important nodes based on community structure and degree of neighbor nodes to disseminate information in complex networks

Tulu, Muluneh Mekonnen; Hou, Ronghui; Younas, Talha

doi:10.1109/compcomm.2017.8322554

Cited by 12 publications

(8 citation statements)

References 12 publications

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“…In addition to human social networks, some other social networks have the same characteristics. Dolphins will share route and food information with all their companions, or warn of danger [2,14,20,32,43,47,98,103,107,108,111,124,[130][131][132]137]. The production of a commodity will involve close cooperation between many companies [35,52], and the reputation of a company in Corporate ownership network (CON) will be widely spread in the industry.…”

Section: Social Network (Sn)mentioning

confidence: 99%

See 1 more Smart Citation

Epidemic Model-based Network Influential Node Ranking Methods: A Ranking Rationality Perspective

Zhang¹,

Zhao²,

Nie³

et al. 2023

Preprint

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Most recent surveys and reviews on Influential Node Ranking Methods (INRMs) hightlight discussions on the methods' technical details, but there still lacks in-depth research on the fundamental issue of how to verify the considerable influence of these nodes in a network. Compared to conventional verification models such as cascade failure and linear threshold, the epidemic model is more widely used. Accordingly, we conducted a survey of INRM based on epidemic model on 81 primary studies and analyzed their Capability and Correctness which we defined in our work. Our study categorized 4 types of networks used by INRM, classified 7 categories of INRMs for analyzing the networks and defined 2 evaluation metrics set of Capability and Correctness for evaluating INRM from Ranking Rationality Perspective. We also discussed particular real-world networks that were used to evaluate INRM and the Capability and Correctness of different INRMs on ranking nodes in specific networks. This is, as far as we know, the first survey aimed at systematically summarizing the Capability and Correctness of INRM. Our findings can assist practitioners and researchers in choosing and comparing INRMs and identifying research gaps.

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Section: Social Network (Sn)mentioning

confidence: 99%

“…The k-shell decomposition method and its variants are used to [2,35,58,75,132,137] peel the network layer by layer from the outside to the inside. Other methods apply community partitioning algorithms [9,14,26,95,107,108,146,147], or measure the importance of nodes by deleting nodes and observing network changes [10,80,112].…”

Section: 13mentioning

confidence: 99%

Epidemic Model-based Network Influential Node Ranking Methods: A Ranking Rationality Perspective

Zhang¹,

Zhao²,

Nie³

et al. 2023

Preprint

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“…Tulu et al [143] introduced a method that selects the important nodes in a large network according to the degree of neighbor nodes. Selected nodes connect communities to each other.…”

Section: ) Degree-based Analysis Techniquementioning

confidence: 99%

Identifying the Top-k Influential Spreaders in Social Networks: a Survey and Experimental Evaluation

Taha

2022

IEEE Access

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Identifying the influential and spreader nodes in complex networks solves many types of complex scientific problems. In social networks, identifying the influential individuals can be useful for structuring techniques that accelerate or hinder information propagation. Each node in the network has unique characteristics that reflect its importance. These characteristics are used by researchers to design many different centrality algorithms. Unfortunately, current survey papers categorize these algorithms into broad classes and do not draw distinguishable boundaries among the specific techniques adopted by them. This can result in misclassifying unrelated algorithms into the same analysis category. To overcome this, we introduce a methodology-based taxonomy for classifying the algorithms that identify top-k influential spreaders into hierarchically nested, specific, and fine-grained categories. We survey 184 papers and discuss their algorithms, which fall under 26 specific techniques. Our methodological taxonomy classifies the algorithms hierarchically into the following manner: Analysis type analysis scope analysis approach analysis category analysis sub-category analysis specific technique. We introduce in this paper a comprehensive survey, review, and experimental evaluation of the recent and state-of-the-art algorithms that identify the top-k and influential spreader nodes in social networks.

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“…A network can be viewed as a graph consisting of nodes connected by edges according to a certain rule or form, so the nodes and edges do not necessarily have physical meanings in the graphs. Complex network analysis has been applied to many fields for key node identification and efficiency analysis, such as wireless communication system [16]- [18], aviation transportation system [19], [20], community network [21], underwater acoustic sensor network [22], urban transportation system [23], [24], power transmission system [25], [26], etc. Complex network analysis models have been proposed for different systems, and structural characteristics are studied based on these models, such as node degree, betweenness centrality, and clustering coefficient.…”

Section: Introductionmentioning

confidence: 99%

SEE Fault Sensitivity Analysis and Security Reinforcement Design for FPGA Circuits Based on Complex Network

et al. 2020

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There may exist many high-energy particles in spacecraft, so the FPGA circuits design needs corresponding sensitivity analysis and security reinforcement of anti-SEE (Single-event effects). However, it may be impractical to perform such measures to all modules of FGPA circuits due to limited resources. To identify the key modules which have a vital impact on the design and operation of FPGA circuits in spacecraft, this paper presents a novel scheme based on complex network for modeling the modules considering both the circuit functional structure and signal interaction relationship between modules. First, complex networks like MSN (Module Structure Network) and SFN (Signal Flow Network) are established to identify modules by treating each module as a node, and indicators including degree centrality (DC), betweenness centrality (BC), clustering coefficient (CC), etc., are calculated. Then, an entropy-weight method (EWM) is utilized to calculate the indicators comprehensively for identifying key modules. Next, network efficiency and sensitivity analysis are performed for failure modes. Finally, a case study is carried out, demonstrating the effectiveness of the proposed scheme for the key module identification. This work provides useful technical support for engineers in spacecraft FPGA circuits design and performance enhancement.INDEX TERMS Complex network, FPGA circuits, SEE sensitivity analysis, entropy-weight method.

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Finding important nodes based on community structure and degree of neighbor nodes to disseminate information in complex networks

Cited by 12 publications

References 12 publications

Epidemic Model-based Network Influential Node Ranking Methods: A Ranking Rationality Perspective

Epidemic Model-based Network Influential Node Ranking Methods: A Ranking Rationality Perspective

Identifying the Top-k Influential Spreaders in Social Networks: a Survey and Experimental Evaluation

SEE Fault Sensitivity Analysis and Security Reinforcement Design for FPGA Circuits Based on Complex Network

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