scholarly journals Theoretical framework for social network marketing based on genetic algorithm

2016 ◽  
Vol 11 (3) ◽  
pp. 23-28
Author(s):  
Mohammad Rababah Osama ◽  
Hamtini Thair ◽  
Nawwaf Fakhouri Hussam ◽  
Hudeb Amjed ◽  
Hwaitat Ahmad
Keyword(s):  
Genetic Algorithm ◽  
Social Network ◽  
Theoretical Framework ◽  
Network Marketing

scholarly journals Discovering the maximum k-clique on social networks using bat optimization algorithm

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Akram Khodadadi ◽  
Shahram Saeidi
Keyword(s):  
Genetic Algorithm ◽  
Social Networks ◽  
Social Network ◽  
Coding Theory ◽  
Evaluation Criteria ◽  
Convergence Speed ◽  
Optimization Approach ◽  
Computational Results ◽  
Complete Subgraph ◽  
Np Complete

AbstractThe k-clique problem is identifying the largest complete subgraph of size k on a network, and it has many applications in Social Network Analysis (SNA), coding theory, geometry, etc. Due to the NP-Complete nature of the problem, the meta-heuristic approaches have raised the interest of the researchers and some algorithms are developed. In this paper, a new algorithm based on the Bat optimization approach is developed for finding the maximum k-clique on a social network to increase the convergence speed and evaluation criteria such as Precision, Recall, and F1-score. The proposed algorithm is simulated in Matlab® software over Dolphin social network and DIMACS dataset for k = 3, 4, 5. The computational results show that the convergence speed on the former dataset is increased in comparison with the Genetic Algorithm (GA) and Ant Colony Optimization (ACO) approaches. Besides, the evaluation criteria are also modified on the latter dataset and the F1-score is obtained as 100% for k = 5.

The Impact of Social Network Marketing on University Students' Purchase Intention and How It Is Affected of Consumer Engagement

2020 ◽  
Vol 6 (2) ◽  
pp. 87-91
Author(s):  
Hartiwi Prabowo ◽  
Rini Kurnia Sari ◽  
Stephanie Bangapadang
Keyword(s):  
Social Network ◽  
Data Collection ◽  
University Students ◽  
Purchase Intention ◽  
Research Method ◽  
Quantitative Research ◽  
Consumer Engagement ◽  
Network Marketing ◽  
Consumer Purchase Intention ◽  
The Impact

The research conducted is to know the impact of social network marketing on consumer purchase intention and consumers who become research are active students at private universities in Jakarta, and how social network marketing also affect consumer engagement (as moderate variable). The research method used in this research is quantitative research method. A method of data collection used in this research is a questionnaire distributed to 119 university students. The results of this study showed that social network marketing has a strong and significant impact oncustomer engagement, customer engagementhas a strong and significant impact on consumer purchase intention, social network marketing has a strong and significant impact consumer purchase intention, and also there is a significant impact from social network marketing on consumer purchase intention through consumer engagement.

Maximizing influence in a social network: Improved results using a genetic algorithm

2017 ◽  
Vol 478 ◽  
pp. 20-30 ◽  
Author(s):  
Kaiqi Zhang ◽  
Haifeng Du ◽  
Marcus W. Feldman
Keyword(s):  
Genetic Algorithm ◽  
Social Network

Genetic Algorithm Applied in the Social Network Data Analysis

2013 ◽  
Vol 427-429 ◽  
pp. 2188-2191
Author(s):  
Lei Liu ◽  
Quan Bao Gao
Keyword(s):  
Genetic Algorithm ◽  
Social Networks ◽  
Social Network ◽  
Data Analysis ◽  
Rapid Development ◽  
Virtual Community ◽  
Social Network Data ◽  
Starting Point ◽  
The Social ◽  
Optimal Data Analysis

The rapid development of network and information technology makes the network become the indispensable part in people's life. Network design uses email as a starting point, instead of actual letters. Then Happy Nets, BBS etc. are evolved from it, with virtual as their major feature. In the process of social networks evolution, the personal image transformed from the actual into the virtual one. All this has contributed to the birth of the social network, which then makes the contacts among people presenting the feature of network expansion and cost reduction. The popular social network nowadays is considered to be social plus network, namely, through the network, as a carrier, people are connected to form a virtual community with certain characteristics. Based on the genetic algorithm and genetic coding technology, the article is designed to make the optimal data analysis and create a optimistic cyber environment in the process of the social networks explosive development.

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