Identifying Key Nodes in Social Networks Using Multi-Criteria Decision-Making Tools

Mesgari, Iman; Kermani, Mehrdad Agha Mohammad Ali; Hanneman, Robert; Aliahmadi, Alireza

doi:10.1007/978-3-319-16619-3_10

Cited by 18 publications

(12 citation statements)

References 19 publications

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“…To illustrate the utility of the model in identifying the best seed nodes of a social network for maximizing the diffusion of information, the Abrar dataset [59,60] is utilized. During 2010-2011, 163 students in two disciplines at Abrar University (Industrial Engineering and Software Engineering) were interviewed.…”

Section: Case Study Implementation and Evaluationmentioning

confidence: 99%

A robust optimization model for influence maximization in social networks with heterogeneous nodes

2021

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Influence maximization is the problem of trying to maximize the number of influenced nodes by selecting optimal seed nodes, given that influencing these nodes is costly. Due to the probabilistic nature of the problem, existing approaches deal with the concept of the expected number of nodes. In the current research, a scenario-based robust optimization approach is taken to finding the most influential nodes. The proposed robust optimization model maximizes the number of infected nodes in the last step of diffusion while minimizing the number of seed nodes. Nodes, however, are treated as heterogeneous with regard to their propensity to pass messages along; or as having varying activation thresholds. Experiments are performed on a real text-messaging social network. The model developed here significantly outperforms some of the well-known existing heuristic approaches which are proposed in previous works.

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Section: Case Study Implementation and Evaluationmentioning

confidence: 99%

A robust optimization model for influence maximization in social networks with heterogeneous nodes

2021

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“…As a result, the above three measures are very suitable for use in our comprehensive model of influential node identification. Similarly, in the literatures [23][24][25][26], the three measures are also used in their MADM (multiple-attribute decision-making) models to identifying influential nodes. Here, we firstly give a brief review on them.…”

Section: Three Typical Measures For Node Influencesmentioning

confidence: 99%

A Comprehensive Algorithm for Evaluating Node Influences in Social Networks Based on Preference Analysis and Random Walk

Mao

Xiao

2018

Complexity

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In the era of big data, social network has become an important reflection of human communications and interactions on the Internet. Identifying the influential spreaders in networks plays a crucial role in various areas, such as disease outbreak, virus propagation, and public opinion controlling. Based on the three basic centrality measures, a comprehensive algorithm named PARW-Rank for evaluating node influences has been proposed by applying preference relation analysis and random walk technique. For each basic measure, the preference relation between every node pair in a network is analyzed to construct the partial preference graph (PPG). Then, the comprehensive preference graph (CPG) is generated by combining the preference relations with respect to three basic measures. Finally, the ranking of nodes is determined by conducting random walk on the CPG. Furthermore, five public social networks are used for comparative analysis. The experimental results show that our PARW-Rank algorithm can achieve the higher precision and better stability than the existing methods with a single centrality measure.

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“…Another wave in influence maximization research line is to find the influential nodes (key nodes) in consideration of more than one criterion. For the first time, Mesgari et al in 2013, presented a novel approach in a conference in Bielefeld, in which they utilized TOPSIS method to find the key nodes in social networks [40]. They examined their approach using three datasets in various sizes and sub-structures.…”

Section: Multi-attribute-based Approach For Influence Maximizationmentioning

confidence: 99%

“…TOPSIS suggests herewith the alternative which is the closest to the ideal solution and the farthest from negative ideal solution as the most influential node in each community [48]. TOPSIS has following steps [40]:…”

Section: Multi Attribute Decision Makingmentioning

confidence: 99%

“…• Step2: The normalization step in which X is converted to R by = ( ∑︀ Based on the previous works using TOPSIS methods to identify the most influential nodes in social networks, we considered Degree Centrality (DC), Closeness Centrality (CC), Betweenness Centrality (BC) and PageRank (PR) as the attributes in this decision-making process [18], [40], [26].…”

Section: Multi Attribute Decision Makingmentioning

confidence: 99%

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A hybrid algorithm based on community detection and multi attribute decision making for influence maximization

Jalayer

Azheian

Kermani

2018

Computers & Industrial Engineering

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Influence maximization problem is trying to identify a set of nodes by which the spread of influence, diseases or information is maximized. The optimization of influence by finding such a set is NP-hard problem and a key issue in analyzing complex networks. In this paper, a new greedy and hybrid approach based on a community detection algorithm and an MADM technique (TOPSIS) is proposed to cope with the problem, called, 'Greedy TOPSIS and Community-Based' (GTaCB) algorithm. The paper concisely introduces community detection and TOPSIS technique, then it presents the pseudo-code of the proposed algorithm. Afterwards, it compares the performance of the solution which is found by GTaCB with some well-known greedy algorithms, based on Degree Centrality, Closeness Centrality, Betweenness Centrality, PageRank as well as TOPSIS, from two aspects: diffusion quality and diffusion speed. In order to evaluate the performance of GTaCB, computational experiments on nine different types of real-world networks are provided. The tests are conducted via one of the renowned epidemic diffusion models, namely, Susceptible-Infected-Recovered (SIR) model. The simulations exhibit that in most of the cases the proposed algorithm significantly outperforms the others, chiefly as number of initial nodes or probability of infection increases.

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Identifying Key Nodes in Social Networks Using Multi-Criteria Decision-Making Tools

Cited by 18 publications

References 19 publications

A robust optimization model for influence maximization in social networks with heterogeneous nodes

A robust optimization model for influence maximization in social networks with heterogeneous nodes

A Comprehensive Algorithm for Evaluating Node Influences in Social Networks Based on Preference Analysis and Random Walk

A hybrid algorithm based on community detection and multi attribute decision making for influence maximization

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