DIN: An efficient algorithm for detecting influential nodes in social graphs using network structure and attributes

Jaouadi, Myriam; Romdhane, Lotfi

doi:10.1109/aiccsa.2016.7945698

Cited by 8 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Jaouadi and Romdhane [55] proposed an information diffusion method that controls the information propagation through seed nodes. The method models the semantics of a network based on users' interests.…”

Section: ) Pagerank-based Analysis Techniquementioning

confidence: 99%

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

Taha

2022

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

Section: ) Pagerank-based Analysis Techniquementioning

confidence: 99%

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

Taha

2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…They divided these user behaviors, gave the behaviors different impact weights, and used the PageRank algorithm to calculate the influence of each behavior. Jaouadi and Romdhane [18] proposed an algorithm called DIN, which reasonably utilizes the social network structure and the semantics of information transport by the network.…”

Section: Related Workmentioning

confidence: 99%

CBIM-RSRW: An Community-Based Method for Influence Maximization in Social Network

et al. 2019

View full text Add to dashboard Cite

Influence maximization is an important problem, which seeks a small set of key users who spread the influence widely into the network. It finds applications in viral marketing, epidemic control, and assessing cascading failures within complex systems. The current studies treat nodes in social network with equal weights, and the influence possibility mainly decide by node degree. In this paper, we study the influence maximization problem in social networks and we improve the independent cascade model to realize the goal of different weights for different users, and the differentiation of influence probability. Meanwhile, We take advantage of the community structure to speed up the algorithm. Then, we propose a method called the reverse reachable index method based on random walk (RSRW) to select potential high-impact nodes from those communities. The experimental result on four actual data set shows that these improvements can greatly reduce the calculation time while ensuring the accuracy of the results.

show abstract

Parallel social behavior-based algorithm for identification of influential users in social network

2021

View full text Add to dashboard Cite

Influence maximization in social networks refers to the process of finding influential users who make the most of information or product adoption. The social networks is prone to grow exponentially, which makes it difficult to analyze. Critically, most of approaches in the literature focus only on modeling structural properties, ignoring the social behavior in the relations between users. For this, we tend to parallelize the influence maximization task based on social behavior. In this paper, we introduce a new parallel algorithm, named PSAIIM, for identification of influential users in social network. In PSAIIM, we uses two semantic metrics: the user’s interests and the dynamically-weighted social actions as user interactive behaviors. In order to overcome the size of actual real-world social networks and to minimize the execution time, we used the community structure to apply perfect parallelism to the CPU architecture of the machines to compute an optimal set of influential nodes. Experimental results on real-world networks reveal effectiveness of the proposed method as compared to the existing state-of-the-art influence maximization algorithms, especially in the speed of calculation.

show abstract

DIN: An efficient algorithm for detecting influential nodes in social graphs using network structure and attributes

Cited by 8 publications

References 15 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

CBIM-RSRW: An Community-Based Method for Influence Maximization in Social Network

Parallel social behavior-based algorithm for identification of influential users in social network

Contact Info

Product

Resources

About