A New Structural and Semantic Approach for Identifying Influential Nodes in Social Networks

Hafiene, Nesrine; Karoui, Wafa

doi:10.1109/aiccsa.2017.196

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…Hafiene and Karoui [48] presented a new approach that identifies influential nodes. First, the method identifies leader nodes, whose degree centralities are higher than their neighbors.…”

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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“…Hafiene and Karoui [48] presented a new approach that identifies influential nodes. First, the method identifies leader nodes, whose degree centralities are higher than their neighbors.…”

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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“…Where (u, v) represent the common attributes among two nodes (u) and (v). leng (u) is the length of the attributes vector of a node (u) and leng (v) is the length of the attributes vector of a node (v) [23]. The model used in the impact deployment process in the suggested method is probabilistic social influence model.…”

Section: Sem( U V) = (3)mentioning

confidence: 99%

Extracting Influential Nodes in Social Networks Based On Community Structure

Sabar Miften,

Naseem,

Abdulkareem Huzam

2021

JEPS

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Influence maximization (IM) is the process focuses on finding active users who make that maximizes the spread of influence into the network. In recent years, community detection has attracted intensive interest especially in the implementation of clustering algorithms in complex networks for community detection. In this paper the social network was divided into communities using the proposed algorithm which is called (CDBNN) algorithm, CDBNN stands for Community Detection Based on Nodes Neighbor. The key nodes (candidate nodes) were extracted using the degree centrality in each community. The propagates model (PSI) was used to information propagates through the network. Finally, using closeness centrality to extract the influential nodes from the network. Experimental results on the real network are efficient for influence propagates, compared with three known proposals.

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“…Few new researches started to consider the effect of utilizing the semantic information associated with each user in community detection as in Ben Amor (2016) as well as in discovering influential nodes as in Wagih et al (2019), Hafiene and Karoui (2017), and Jaouadi and Romdhane (2016). Wagih et al (2019) proposed a semantic based algorithm for detecting influential diffusion in social networks.…”

Section: Related Workmentioning

confidence: 99%

Strum: A Semantic Trust-Based Miner Model for Detecting Influential Spreaders in Social Networks

Wagih¹,

Mokhtar²,

Ghoniemy³

2019

Proceedings of the International Conferences Big Data Analytics, Data Mining and Computational Intelligence 2019; And Theory An

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Social Networks have become embedded in every aspect of today people's life. The success of online social networks has a significant role in shaping trust and influence among participants. They are continuing to gain ground every day, opening the opportunity for several research movements. Great attention is now given for identifying influential spreaders in many application domains as recommendation systems, viral marketing, epidemic control and others. Most of the recent research focused only on the network structure with its geospatial information disregarding the semantics underlying these networks. In this paper, we propose a semantic trust-based miner model (STRUM) that efficiently detects influential spreaders in social networks for enhancing recommendation services. STRUM considers both geospatial information and semantic information in defining each user in the social network. Different experiments have been conducted to verify and validate the proposed algorithm against the latest published using two real location based social networks namely; Brightkite and Weeplaces are used in the experiments.

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A New Structural and Semantic Approach for Identifying Influential Nodes in Social Networks

Cited by 6 publications

References 18 publications

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

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

Extracting Influential Nodes in Social Networks Based On Community Structure

Strum: A Semantic Trust-Based Miner Model for Detecting Influential Spreaders in Social Networks

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