Introducing a procedure for developing a novel centrality measure (Sociability Centrality) for social networks using TOPSIS method and genetic algorithm

Kermani, Mehrdad Agha Mohammad Ali; Badiee, Aghdas; Aliahmadi, Alireza; Ghazanfari, Mehdi; Kalantari, Hamed

doi:10.1016/j.chb.2015.11.008

Cited by 42 publications

(7 citation statements)

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“…Therefore, the decision as to which measure should be employed to describe the target attribute more desirable is an essential issue. To cope with these difficulties, some researchers combined several centrality measures into a single innovative measure to investigate the ranking of the nodes (Du et al , 2014; Bian et al , 2017; Agha Mohammad Ali Kermani et al , 2016; Mesgari et al , 2015). Instead of assessing borrowers' credit risk by a single or a combined network characteristic, the proposed model uses an important set of them directly through applying SML algorithms.…”

Section: Discussionmentioning

confidence: 99%

A data-driven and network-aware approach for credit risk prediction in supply chain finance

Fayyaz

Rasouli

Amiri

2020

IMDS

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PurposeThe purpose of this paper is to propose a data-driven model to predict credit risks of actors collaborating within a supply chain finance (SCF) network based on the analysis of their network attributes. This can support applying reverse factoring mechanisms in SCFs.Design/methodology/approachBased on network science, the network measures of the actors collaborating in the investigated SCF are derived through a social network analysis. Then several supervised machine learning algorithms are applied to predict the credit risks of the actors on the basis of their network level and organizational-level characteristics. For this purpose, a data set from an SCF within an automotive industry in Iran is used.FindingsThe findings of the research clearly demonstrate that considering the network attributes of the actors within the prediction models can significantly enhance the accuracy and precision of the models.Research limitations/implicationsThe main limitation of this research is to investigate the applicability and effectiveness of the proposed model within a single case.Practical implicationsThe proposed model can provide a well-established basis for financial intermediaries in SCFs to make more sophisticated decisions within financial facilitation mechanisms.Originality/valueThis study contributes to the existing literature of credit risk evaluation by considering credit risk as a systematic risk that can be influenced by network measures of collaborating actors. To do so, the paper proposes an approach that considers network characteristics of SCFs as critical attributes to predict credit risk.

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Section: Discussionmentioning

confidence: 99%

A data-driven and network-aware approach for credit risk prediction in supply chain finance

Fayyaz

Rasouli

Amiri

2020

IMDS

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“…His studies were later expanded to SNA which sought to analyze social and economic institutions (Kermani et al, 2016). Researchers utilize SNA to model and study natural phenomena in various fields (Kermani et al, 2016), such as friends on social media platforms (Lewis et al, 2008), crisis management (Azhar et al, 2019), supply chain management (Borgatti and Li, 2009), the epidemic of an infectious disease (Danon et al, 2011), political participation (Bode, 2012;Gaines and Mondak, 2009) and community detection. Zachary Karate Club has become a well-known example in the literature for community detection (Zachary, 1977).…”

Section: Social Network Analysismentioning

confidence: 99%

“…Leonhard Euler first found the concept of networks in response to the Seven Bridges of Königsberg problem (Linda and Bertram, 2019). His studies were later expanded to SNA which sought to analyze social and economic institutions (Kermani et al. , 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

Investigation of cinematic genre diversity based on social network analysis: the lost ring of the Iranian cinema industry

Noroozian

Amiri

Kermani

2022

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PurposeMovies critics believe that the diversity of Iranian cinematic genres has decreased over time. The paper aims to answer the following questions: What is the impact of the continuous cooperation between the key nodes on the audience's taste, uniformity of the cinematic genres and the box office? Is there any relationship between the importance of actors in the actors' network and their popularity?Design/methodology/approachIn the artistic world, artists' relationships lead to a network that affects individuals' commercial or artistic success and defines the artwork's value. To study the issue that the diversity of Iranian cinematic genres has decreased over time, the authors utilized social network analysis (SNA), in which every actor is considered a node, and its collaboration with others in the same movies is depicted via edges. After preparing the desired dataset, networks were generated, and metrics were calculated. First, the authors compared the structure of the network with the box office. The results illustrated that the network density growth negatively affects box office. Second, network key nodes were identified, their relationships with other actors were inspected using the Apriori algorithm to examine the density cause and the cinematic genre of key nodes, and their followers were investigated. Finally, the relationship between the actors' Instagram follower count and their importance in the network structure was analyzed to answer whether the generated network is acceptable in society.FindingsThe social problem genre has stabilized due to continuous cooperation between the core nodes because network density negatively impacts the box office. As well as, the generated network in the cinema is acceptable by the audience because there is a positive correlation between the importance of actors in the network and their popularity.Originality/valueThe novelty of this paper is investigating the issue raised in the cinema industry and trying to inspect its aspects by utilizing the SNA to deepen the cinematic research and fill the gaps. This study demonstrates a positive correlation between the actors' Instagram follower count and their importance in the network structure, indicating that people follow those central in the actors' network. As well as investigating the network key nodes with a heuristic algorithm using coreness centrality and analyzing their relationships with others through the Apriori algorithm. The authors situated the analysis using a novel and original dataset from the Iranian actors who participated in the Fajr Film Festival from 1998 to 2020.

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“…Such networks also underlie the trade and exchange of goods in non-centralized markets, the provision of mutual insurance in developing countries, research and development, and collusive alliances among corporations, international alliances, and trading agreements; to mention just a few examples [29]. Social network analysis focuses on the relationship analysis between social and economic entities with the aim of finding common features between them [2], [6].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, a new algorithm to solve the influence maximization problem is proposed with three main features: (1) The proposed algorithm takes the topological features of the nodes into account. (2) The nodes' features are taking into consideration simultaneously by making use of one of the multi attribute decision making methods. (3) The fact that the social networks hold some communities is strictly true.…”

Section: Introductionmentioning

confidence: 99%

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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Introducing a procedure for developing a novel centrality measure (Sociability Centrality) for social networks using TOPSIS method and genetic algorithm

Cited by 42 publications

References 42 publications

A data-driven and network-aware approach for credit risk prediction in supply chain finance

A data-driven and network-aware approach for credit risk prediction in supply chain finance

Investigation of cinematic genre diversity based on social network analysis: the lost ring of the Iranian cinema industry

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

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