scholarly journals An Improved Greedy Heuristic for the Minimum Positive Influence Dominating Set Problem in Social Networks

Algorithms ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 79
Author(s):  
Salim Bouamama ◽  
Christian Blum
Keyword(s):  
Social Networks ◽  
Greedy Algorithm ◽  
Dominating Set ◽  
Positive Influence ◽  
Greedy Heuristic ◽  
Greedy Heuristics ◽  
Small Subset ◽  
Problem Size ◽  
Wide Range ◽  
Dominating Set Problem

This paper presents a performance comparison of greedy heuristics for a recent variant of the dominating set problem known as the minimum positive influence dominating set (MPIDS) problem. This APX-hard combinatorial optimization problem has applications in social networks. Its aim is to identify a small subset of key influential individuals in order to facilitate the spread of positive influence in the whole network. In this paper, we focus on the development of a fast and effective greedy heuristic for the MPIDS problem, because greedy heuristics are an essential component of more sophisticated metaheuristics. Thus, the development of well-working greedy heuristics supports the development of efficient metaheuristics. Extensive experiments conducted on a wide range of social networks and complex networks confirm the overall superiority of our greedy algorithm over its competitors, especially when the problem size becomes large. Moreover, we compare our algorithm with the integer linear programming solver CPLEX. While the performance of CPLEX is very strong for small and medium-sized networks, it reaches its limits when being applied to the largest networks. However, even in the context of small and medium-sized networks, our greedy algorithm is only 2.53% worse than CPLEX.

scholarly journals On the approximability of positive influence dominating set in social networks

2012 ◽  
Vol 27 (3) ◽  
pp. 487-503 ◽  
Author(s):  
Thang N. Dinh ◽  
Yilin Shen ◽  
Dung T. Nguyen ◽  
My T. Thai
Keyword(s):  
Social Networks ◽  
Dominating Set ◽  
Positive Influence

Efficient respondents selection for biased survey using homophily-high social relation graph

2016 ◽  
Vol 08 (04) ◽  
pp. 1650071
Author(s):  
Donghyun Kim ◽  
Jiaofei Zhong ◽  
Minhyuk Lee ◽  
Deying Li ◽  
Yingshu Li ◽  
...  
Keyword(s):  
Social Relationships ◽  
Online Social Networks ◽  
Online Survey ◽  
Social Relation ◽  
Dominating Set ◽  
Survey Respondent ◽  
Wide Range ◽  
Respondent Group ◽  
Dominating Set Problem ◽  
Relation Graph

Online social relationships which can be extracted from various online resources such as online social networks are getting much attention from the research communities since they are rich resources to learn about the members of our society as well as the relationships among them. With the advances of Internet related technologies, online surveys are established as an essential tool for a wide range of applications. One significant issue of online survey is how to select a quality respondent group so that the survey result is reliable. This paper studies the use of pairwise online social relationships among the members of a society to form a biased survey respondent group, which might be useful for various applications. We first introduce a way to construct a homophily-high social relation graph. Then, we introduce the minimum inverse k-core dominating set problem (MIkCDSP), which aims to compute a biased respondent group using the homophily-high social relation graph. We show the problem is NP-hard and most importantly propose a greedy approximation for it. Our simulation based on a real social network shows the proposed algorithm is very effective.

Efficient Approximation Algorithms for Minimum Dominating Sets in Social Networks

2018 ◽  
Vol 9 (2) ◽  
pp. 1-32 ◽  
Author(s):  
Traian Marius Truta ◽  
Alina Campan ◽  
Matthew Beckerich
Keyword(s):  
Social Networks ◽  
Dominating Set ◽  
Dominating Sets ◽  
Traditional Media ◽  
Network Characteristics ◽  
The Social ◽  
Dominating Set Problem ◽  
Time Required ◽  
Efficient Approximation ◽  
Selection Of

Social networks are increasingly becoming an outlet that is more and more powerful in spreading news and influence individuals. Compared with other traditional media outlets such as newspaper, radio, and television, social networks empower users to spread their ideological message and/or to deliver target advertising very efficiently in terms of both cost and time. In this article, the authors focus on efficiently finding dominating sets in social networks for the classical dominating set problem as well as for two related problems: partial dominating sets and d-hop dominating sets. They will present algorithms for determining efficiently a good approximation for the social network minimum dominating sets for each of the three variants. The authors ran an extensive suite of experiments to test the presented algorithms on several datasets that include real networks made available by the Stanford Network Analysis Project and synthetic networks that follow the power-law and random models that they generated for this work. The performed experiments show that the selection of the algorithm that performs best to determine efficiently the dominating set is dependent of network characteristics and the order of importance between the size of the dominating set and the time required to determine such a set.

scholarly journals Finding a Weighted Positive In uence Dominating Set in E-learning Social Networks

2013 ◽  
Vol 10 (10) ◽  
pp. 2136-2145 ◽  
Author(s):  
Guangyuan Wang ◽  
Hua Wang ◽  
Xiaohui Tao ◽  
Ji Zhang ◽  
Guohun Zhu
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Learning Community ◽  
Dominating Set ◽  
Online Social Network ◽  
Real Life ◽  
Synthetic Data ◽  
Positive Influence ◽  
Data Sets ◽  
E Learning

Online social network has developed significantly in recent years. Most of current research has utilized the property of online social network to spread information and ideas. Motivated by the applications of dominating set in social networks (such as e-learning), a variation of the dominating set called positive influence dominating set (PIDS) has been studied in the literature. The existing research for PIDS problem do not take into consideration the attributes, directions and degrees of personal influence. However, these factors are very important for selecting a better PIDS. For example, in a real-life e-learning community, the attributes and the degrees of their influence between a tutor and a student are different; the relationship between two e-learning users is asymmetrical. Hence, comprehensive, deep investigation of user’s properties become an emerging and urgent issue. The focus of this study is on the degree and direction between e-learners’ influence. A novel dominating set model called weighted positive influence dominating set (WPIDS), and two selection algorithms for the WPIDS problem have been proposed. Experiments using synthetic data sets demonstrate that the proposed model and algorithms are more reasonable and effective than those of the positive influence dominating set (PIDS) without considering the key factors of weight, direction and so on.

scholarly journals Application of CMSA to the Minimum Positive Influence Dominating Set Problem

2021 ◽  
Author(s):  
Mehmet Anıl Akbay ◽  
Christian Blum
Keyword(s):  
Optimization Problems ◽  
Dominating Set ◽  
Positive Influence ◽  
Problem Instance ◽  
Medium Size ◽  
Combinatorial Optimization Problems ◽  
The Family ◽  
Np Hard Problem ◽  
Dominating Set Problem ◽  
Problem Instances

Construct, Merge, Solve & Adapt (CMSA) is a recently developed algorithm for solving combinatorial optimization problems. It combines heuristic elements, such as the probabilistic generation of solutions, with an exact solver that is iteratively applied to sub-instances of the tackled problem instance. In this paper, we present the application of CMSA to an NP-hard problem from the family of dominating set problems in undirected graphs. More specifically, the application in this paper concerns the minimum positive influence dominating set problem, which has applications in social networks. The obtained results show that CMSA outperforms the current state-of-the-art metaheuristics from the literature. Moreover, when instances of small and medium size are concerned CMSA finds many of the optimal solutions provided by CPLEX, while it clearly outperforms CPLEX in the context of the four largest, respectively more complicated, problem instances.

Efficient Approximation Algorithms for Minimum Dominating Sets in Social Networks

2021 ◽  
pp. 1120-1153
Author(s):  
Traian Marius Truta ◽  
Alina Campan ◽  
Matthew Beckerich
Keyword(s):  
Social Networks ◽  
Dominating Set ◽  
Dominating Sets ◽  
Network Characteristics ◽  
The Social ◽  
Random Models ◽  
Dominating Set Problem ◽  
Time Required ◽  
Efficient Approximation ◽  
Selection Of

Social networks are increasingly becoming an outlet that is more and more powerful in spreading news and influence individuals. Compared with other traditional media outlets such as newspaper, radio, and television, social networks empower users to spread their ideological message and/or to deliver target advertising very efficiently in terms of both cost and time. In this article, the authors focus on efficiently finding dominating sets in social networks for the classical dominating set problem as well as for two related problems: partial dominating sets and d-hop dominating sets. They will present algorithms for determining efficiently a good approximation for the social network minimum dominating sets for each of the three variants. The authors ran an extensive suite of experiments to test the presented algorithms on several datasets that include real networks made available by the Stanford Network Analysis Project and synthetic networks that follow the power-law and random models that they generated for this work. The performed experiments show that the selection of the algorithm that performs best to determine efficiently the dominating set is dependent of network characteristics and the order of importance between the size of the dominating set and the time required to determine such a set.

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