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

Jalayer, Masoud; Azheian, Morvarid; Kermani, Mehrdad Agha Mohammad Ali

doi:10.1016/j.cie.2018.04.049

Cited by 31 publications

(11 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…p.184. A social network can be represented as a graph G = (V, E), where V corresponds to the nodes (vertices) in the graph (users), and E corresponds to the edges that indicate the relationship between users [20], [21]. According to [20] the relationship (edges) connects the influencer and influenced node, i.e., who influences whom.…”

Section: Understanding Influence In Social Networkmentioning

confidence: 99%

How to Measure Influence in Social Networks?

Ribeiro

Azevedo

Sá

et al. 2020

Lecture Notes in Business Information Processing

View full text Add to dashboard Cite

Today, social networks are a valued resource of social data that can be used to understand the interactions among people and communities. People can influence or be influenced by interactions, shared opinions and emotions. However, in the social network analysis, one of the main problems is to find the most influential people. This work aims to report on the results of literature review whose goal was to identify and analyse the metrics, algorithms and models used to measure the user influence on social networks. The search was carried out in three databases: Scopus, IEEEXplore, and ScienceDirect. We restricted published articles between the years 2014 until 2020, in English, and we used the following keywords: social networks analysis, influence, metrics, measurements, and algorithms. Backward process was applied to complement the search considering inclusion and exclusion criteria. As a result of this process, we obtained 25 articles: 12 in the initial search and 13 in the backward process. The literature review resulted in the collection of 21 influence metrics, 4 influence algorithms, and 8 models of influence analysis. We start by defining influence and presenting its properties and applications. We then proceed by describing, analysing and categorizing all that were found metrics, algorithms, and models to measure influence in social networks. Finally, we present a discussion on these metrics, algorithms, and models. This work helps researchers to quickly gain a broad perspective on metrics, algorithms, and models for influence in social networks and their relative potentialities and limitations.

show abstract

Section: Understanding Influence In Social Networkmentioning

confidence: 99%

How to Measure Influence in Social Networks?

Ribeiro

Azevedo

Sá

et al. 2020

Lecture Notes in Business Information Processing

View full text Add to dashboard Cite

show abstract

“…On the other hand, it is used for the weighted network, whereas our algorithm is for the basic social network. As a multiple-attribute decisionmaking (MADM) technique, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) has been successfully applied to solve some typical decision-making problems [19,20,28,54]. To identify influential nodes, this technique 13 Complexity was introduced to rank the nodes in a network according to their influences [25,26].…”

Section: Related Workmentioning

confidence: 99%

“…In their solutions, several different measures like degree centrality are usually taken into account to design a comprehensive model for evaluating the influence spread of node. Typically, Jalayer et al proposed a "greedy TOPSIS and community-based" (GTaCB) algorithm [19] for this problem. It could be seen that the TOPSIS in [19,20] belongs to a greedy technique.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Mao

Xiao

2018

Complexity

View full text Add to dashboard Cite

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.

show abstract

“…erefore, some more interesting properties of the network can be captured through detecting communities. In addition, community detection can help to facilitate many downstream studies, such as prevention of epidemic propagation [4], disease detection [5], link prediction [6], and influence maximization [7]. Overall, the problem of community detection has attracted many researchers from different fields in the last decade.…”

Section: Introductionmentioning

confidence: 99%

A Seed-Expanding Method Based on TOPSIS for Community Detection in Complex Networks

et al. 2020

View full text Add to dashboard Cite

The centrality plays an important role in many community-detection algorithms, which depend on various kinds of centralities to identify seed vertices of communities first and then expand each of communities based on the seeds to get the resulting community structure. The traditional algorithms always use a single centrality measure to recognize seed vertices from the network, but each centrality measure has both pros and cons when being used in this circumstance; hence seed vertices identified using a single centrality measure might not be the best ones. In this paper, we propose a framework which integrates advantages of various centrality measures to identify the seed vertices from the network based on the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) multiattribute decision-making technology. We take each of the centrality measures involved as an attribute, rank vertices according to the scores which are calculated for them using TOPSIS, and then take vertices with top ranks as the seeds. To put this framework into practice, we concretize it in this paper by considering four centrality measures as attributes to identify the seed vertices of communities first, then expanding communities by iteratively inserting one unclassified vertex into the community to which its most similar neighbor belongs, and the similarity between them is the largest among all pairs of vertices. After that, we obtain the initial community structure. However, the amount of communities might be much more than they should be, and some communities might be too small to make sense. Therefore, we finally consider a postprocessing procedure to merge some initial communities into larger ones to acquire the resulting community structure. To test the effectiveness of the proposed framework and method, we have performed extensive experiments on both some synthetic networks and some real-world networks; the experimental results show that the proposed method can get better results, and the quality of the detected community structure is much higher than those of competitors.

show abstract

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

Cited by 31 publications

References 53 publications

How to Measure Influence in Social Networks?

How to Measure Influence in Social Networks?

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

A Seed-Expanding Method Based on TOPSIS for Community Detection in Complex Networks

Contact Info

Product

Resources

About