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.

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 The Influence of Internet and Social Media on Purchasing Decisions in Kuwait

2018 ◽  
Vol 10 (2) ◽  
pp. 792
Author(s):  
Khalid Abdulkareem Al-Enezi ◽  
Imad Fakhri Taha Al Shaikhli ◽  
Sufyan Salim Mahmood AlDabbagh
Keyword(s):  
Social Networks ◽  
Social Networking ◽  
Quantitative Methods ◽  
Purchasing Decisions ◽  
The Social ◽  
Digital Scanning ◽  
Networking Tool ◽  
The Relationship ◽  
Selection Of

<span>This research aims to measure the role of social networks in influencing purchasing decisions among consumers in Kuwait; the research used the quantitative methods, and analytical the technique to get the results, and the research developed a measure to study the relationship between the variables to the study and selection of a sample of consumers of (100). The results indicated that the social networking variables (exchange of information, evaluation of product) possess influence on purchasing decisions. Furthermore, the results indicate that majority of respondents do their digital scanning more often before intend to go to the store. The unexpected results came from the question “traditional advertising (TV, Newspaper, Magazine, Billboards) are more effective than the social networking; 23% agreed, 36% said no, and 41% said sometimes. In light of these findings, the study made a series of recommendations; the most important are; The executives and sales representatives need to understand the benefits offered by social networks, and understand the advantages and functions and tools of social communication, and knowing how to apply them effectively and efficiently, and then use the appropriate social networking tool.</span>

scholarly journals The Social Networks and Social Support of Siblings of Children with Cancer

Children ◽  
2022 ◽  
Vol 9 (1) ◽  
pp. 113
Author(s):  
Sarah E. Wawrzynski ◽  
Melissa A. Alderfer ◽  
Whitney Kvistad ◽  
Lauri Linder ◽  
Maija Reblin ◽  
...  
Keyword(s):  
Social Networks ◽  
Network Size ◽  
Support Networks ◽  
Support Network ◽  
Structured Interviews ◽  
Children With Cancer ◽  
Network Characteristics ◽  
The Social ◽  
Need Support ◽  
Sources Of Support

Siblings of children with cancer need support to ameliorate the challenges they encounter; however, little is known about what types and sources of support exist for siblings. This study addresses this gap in our understanding of the social networks and sources of support for adolescents with a brother or sister who has cancer. Additionally, we describe how the support siblings receive addresses what they feel are the hardest aspects of being a sibling of a child with cancer. During semi-structured interviews, siblings (ages 12–17) constructed ecomaps describing their support networks. Data were coded for support type (emotional, instrumental, informational, validation, companionship) and support provider (e.g., mother, teacher, friend). Network characteristics and patterns of support were explored. Support network size ranged from 3 to 10 individuals (M = 6 ± 1.9); siblings most frequently reported mothers as sources of support (n = 22, 91.7%), followed by fathers (n = 19, 79.2%), close friends (n = 19, 79.2%) and siblings (with or without cancer) (n = 17, 70.8%). Friends and brothers or sisters most often provided validation and companionship while instrumental and informational supports came from parents. This study provides foundational knowledge about siblings’ support networks, which can be utilized to design interventions that improve support for siblings of children with cancer.

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 Domination of Fuzzy Incidence Graphs with the Algorithm and Application for the Selection of a Medical Lab

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Irfan Nazeer ◽  
Tabasam Rashid ◽  
Juan Luis Garcia Guirao
Keyword(s):  
Mobile Networks ◽  
Ad Hoc ◽  
Dominating Set ◽  
Domination Number ◽  
Wireless Networking ◽  
Dominating Sets ◽  
Fuzzy Graphs ◽  
Vague Data ◽  
Selection Of ◽  
Real World Problems

Fuzzy graphs (FGs), broadly known as fuzzy incidence graphs (FIGs), have been recognized as being an effective tool to tackle real-world problems in which vague data and information are essential. Dominating sets (DSs) have multiple applications in diverse areas of life. In wireless networking, DSs are being used to find efficient routes with ad hoc mobile networks. In this paper, we extend the concept of domination of FGs to the FIGs and show some of their important properties. We propose the idea of order, size, and domination in FIGs. Two types of domination, namely, strong fuzzy incidence domination and weak fuzzy incidence domination, for FIGs are discussed. A relationship between strong fuzzy incidence domination and weak fuzzy incidence domination for complete fuzzy incidence graphs (CFIGs) is also introduced. An algorithm to find a fuzzy incidence dominating set (FIDS) and a fuzzy incidence domination number (FIDN) is discussed. Finally, an application of fuzzy incidence domination (FID) is provided to choose the best medical lab among different labs for the conduction of tests for the coronavirus.

scholarly journals Identifying Influence Agents That Promote Physical Activity Through the Simulation of Social Network Interventions: Agent-Based Modeling Study (Preprint)

2018 ◽  
Author(s):  
Thabo J van Woudenberg ◽  
Bojan Simoski ◽  
Eric Fernandes de Mello Araújo ◽  
Kirsten E Bevelander ◽  
William J Burk ◽  
...  
Keyword(s):  
Physical Activity ◽  
Social Networks ◽  
Social Network ◽  
Selection Criteria ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Network Characteristics ◽  
Social Network Intervention ◽  
The Social ◽  
Network Intervention

BACKGROUND Social network interventions targeted at children and adolescents can have a substantial effect on their health behaviors, including physical activity. However, designing successful social network interventions is a considerable research challenge. In this study, we rely on social network analysis and agent-based simulations to better understand and capitalize on the complex interplay of social networks and health behaviors. More specifically, we investigate criteria for selecting influence agents that can be expected to produce the most successful social network health interventions. OBJECTIVE The aim of this study was to test which selection criterion to determine influence agents in a social network intervention resulted in the biggest increase in physical activity in the social network. To test the differences among the selection criteria, a computational model was used to simulate different social network interventions and observe the intervention’s effect on the physical activity of primary and secondary school children within their school classes. As a next step, this study relied on the outcomes of the simulated interventions to investigate whether social network interventions are more effective in some classes than others based on network characteristics. METHODS We used a previously validated agent-based model to understand how physical activity spreads in social networks and who was influencing the spread of behavior. From the observed data of 460 participants collected in 26 school classes, we simulated multiple social network interventions with different selection criteria for the influence agents (ie, in-degree centrality, betweenness centrality, closeness centrality, and random influence agents) and a control condition (ie, no intervention). Subsequently, we investigated whether the detected variation of an intervention’s success within school classes could be explained by structural characteristics of the social networks (ie, network density and network centralization). RESULTS The 1-year simulations showed that social network interventions were more effective compared with the control condition (beta=.30; t100=3.23; P=.001). In addition, the social network interventions that used a measure of centrality to select influence agents outperformed the random influence agent intervention (beta=.46; t100=3.86; P<.001). Also, the closeness centrality condition outperformed the betweenness centrality condition (beta=.59; t100=2.02; P=.046). The anticipated interaction effects of the network characteristics were not observed. CONCLUSIONS Social network intervention can be considered as a viable and promising intervention method to promote physical activity. We demonstrated the usefulness of applying social network analysis and agent-based modeling as part of the social network interventions’ design process. We emphasize the importance of selecting the most successful influence agents and provide a better understanding of the role of network characteristics on the effectiveness of social network interventions.

scholarly journals The technology of social networks and people management in the recruitment and selection of its employees

2021 ◽  
pp. 74-90
Author(s):  
Ramiro Rodrigues Sumar
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Scientific Evidence ◽  
Face To Face ◽  
The Social ◽  
Recruitment And Selection ◽  
The Impact ◽  
A Company ◽  
Virtual Mode ◽  
Selection Of

Objective: To describe the impact that social networks can have on the recruitment and selection of their employees. Question Problem: How can the social network favor the recruitment and selection of employees of a company? Methodology: Literature review. Results: The evidence of the results showed that technologies through social networks can be relevant for the recruitment and selection of people for the organization. But this recruitment should be done with a differentiated look at each type of social network by the recruiter. Final Considerations: Recruitment and selection have been changing as a traditional (face-to-face) way for the technological (virtual) mode. The study mentioned that social networks are tools capable of bringing to the recruiter candidates able to take the organization responsibly and that there are no barriers in the virtual world to find the ideal candidate. It is emphasized the importance of extending this study based on scientific evidence, in which research can be carried out in companies for the use of social networks in the monitoring of their employees.

Motives and Contexts of Identity Change: A Case for Network Effects

2005 ◽  
Vol 68 (4) ◽  
pp. 289-315 ◽  
Author(s):  
Daniel Mcfarland ◽  
Heili Pals
Keyword(s):  
Social Networks ◽  
Social Identity Theory ◽  
Network Effects ◽  
Identity Theory ◽  
Strong Relationship ◽  
Social Contexts ◽  
Identity Change ◽  
Logit Models ◽  
Network Characteristics ◽  
The Social

In this paper we interrelate different theories of identity and describe how various social contexts and cognitive motives influence the process of identity change. We consider two competing theories about the linkage of contexts with motives for identity change: the effect of category traits, based on social identity theory, and the effect of social networks, based on identity theory. To explore these relations, we use data collected on more than 6,000 adolescents at six high schools in two consecutive school years. Multilevel logit models reveal a strong relationship between contexts and perceived identity imbalances, and a strong effect of identity imbalance on identity change. More important than category traits are the social network characteristics of prominence, homogeneity, and bridging; these form social contexts that affect perceptions of identity imbalance, and the perceptions in turn lead to a heightened incidence of identity change.

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