Influential Node Identification in Command and Control Networks Based on Integral <i>k</i>-Shell

Wang, Yun-Ming; Chen, Bo; Li, Weidong; Zhang, Duoping

doi:10.1155/2019/6528431

Cited by 12 publications

(8 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Let us consider the complex network with 18 edges and 14 nodes, as shown in Figure 2. [30][31][32] In the K-shell decomposition of the above-mentioned network, degree one is assigned for Nodes I, II, IX, XIV, XII, and XIII and are removed from the network during the first iteration, which is marked in blue color. Here, all the nodes with degree one are assigned with the K-index I k ¼ 1 and again recursively check for new nodes with degree one.…”

Section: K-shell Decompositionmentioning

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

View full text Add to dashboard Cite

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.

show abstract

Section: K-shell Decompositionmentioning

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

View full text Add to dashboard Cite

show abstract

“…Then, the relevant departments will take a more powerful approach to reduce the impact of negative public opinion and promote more network users believing real information, ultimately creating more immune users at the initial stage of direct immunity. Simultaneously, because opinion leaders in social networks will have an important influence on the spread of sentiment, [27,28] increasing the involvement of relevant departments should limit the spreading ability of influential communicators in the network, thus suppressing the spread.…”

Section: Interventional Force Of Relevant Departmentsmentioning

confidence: 99%

Direct immune-SCIR public-opinion propagation model based on real-time online users*

Wang¹,

Guo²,

Li³

et al. 2020

Chinese Phys. B

View full text Add to dashboard Cite

Current public-opinion propagation research usually focused on closed network topologies without considering the fluctuation of the number of network users or the impact of social factors on propagation. Thus, it remains difficult to accurately describe the public-opinion propagation rules of social networks. In order to study the rules of public opinion spread on dynamic social networks, by analyzing the activity of social-network users and the regulatory role of relevant departments in the spread of public opinion, concepts of additional user and offline rates are introduced, and the direct immune-susceptible, contacted, infected, and refractory (DI-SCIR) public-opinion propagation model based on real-time online users is established. The interventional force of relevant departments, credibility of real information, and time of intervention are considered, and a public-opinion propagation control strategy based on direct immunity is proposed. The equilibrium point and the basic reproduction number of the model are theoretically analyzed to obtain boundary conditions for public-opinion propagation. Simulation results show that the new model can accurately reflect the propagation rules of public opinion. When the basic reproduction number is less than 1, public opinion will eventually disappear in the network. Social factors can significantly influence the time and scope of public opinion spread on social networks. By controlling social factors, relevant departments can analyze the rules of public opinion spread on social networks to suppress the propagate of negative public opinion and provide a powerful tool to ensure security and stability of society.

show abstract

“…[24]. Wang et al proposed a method based on an integral k-shell to identify the influential nodes in a command-andcontrol network [25]. Escalante and Odehnal proposed a deterministic SIRS-type model for rumour propagation and applied it in simulations with two types of rumours: an original rumour, followed by a second counteracting rumour based on a complex network [26].…”

Section: Literature Reviewmentioning

confidence: 99%

The Spread of Information in Virtual Communities

Zhang

Meng

et al. 2020

Complexity

View full text Add to dashboard Cite

With the growth of online commerce, companies have created virtual communities (VCs) where users can create posts and reply to posts about the company’s products. VCs can be represented as networks, with users as nodes and relationships between users as edges. Information propagates through edges. In VC studies, it is important to know how the number of topics concerning the product grows over time and what network features make a user more influential than others in the information-spreading process. The existing literature has not provided a quantitative method with which to determine key points during the topic emergence process. Also, few researchers have considered the link between multilayer physical features and the nodes’ spreading influence. In this paper, we present two new ideas to enrich network theory as applied to VCs: a novel application of an adjusted coefficient of determination to topic growth and an adjustment to the Jaccard coefficient to measure the connection between two users. A two-layer network model was first used to study the spread of topics through a VC. A random forest method was then applied to rank various factors that might determine an individual user’s importance in topic spreading through a VC. Our research provides insightful ways for enterprises to mine information from VCs.

show abstract

Influential Node Identification in Command and Control Networks Based on Integral k-Shell

Cited by 12 publications

References 37 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

Direct immune-SCIR public-opinion propagation model based on real-time online users*

The Spread of Information in Virtual Communities

Contact Info

Product

Resources

About