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

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 152115-152125
Author(s):  
Feng Cai ◽  
Lirong Qiu ◽  
Xinkai Kuai ◽  
Hongshuai Zhao
2021 ◽  
Author(s):  
Xuanhao Chen ◽  
Liwei Deng ◽  
Yan Zhao ◽  
Xiaofang Zhou ◽  
Kai Zheng

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lucy Nyundo ◽  
Maxine Whittaker ◽  
Lynne Eagle ◽  
David R. Low

Abstract Background The significant contribution of community-based distribution (CBD) of family planning services and contraceptives to the uptake of contraceptives in hard-to-reach communities has resulted in the scaling-up of this approach in many Sub-Saharan countries. However, contextual factors need to be taken into consideration. For example, social network influence (e.g. spouse/partner, in-laws, and parents) on fertility decisions in many African and Asian societies is inevitable because of the social organisational structures. Hence the need to adapt CBD strategies to the social network context of a given society. Methods Data collection involved structured interviews from August 2018 to March 2019. Randomly selected respondents (n = 149) were recruited from four purposively selected health facilities in Lusaka district, Zambia. Respondents were screened for age (> 15 yrs.) and marital status. A mix of categorical and qualitative data was generated. The Statistical Package for Social Sciences (SPSS®24) was used to carry out descriptive analysis and tests of association (Fisher’s exact) while Nvivo®12 was used to analyse the qualitative data using a deductive thematic approach. Results The results indicate that pre-marriage counselling (pre-MC) influences key elements of the husband-wife relationship (p > 0.005), namely; sexual relationship, inter-personal communication, assignation of roles and responsibilities, leadership and authority. These elements of the husband-wife relationship also affect how spouses/partners interact when making fertility decisions. More importantly, the majority (86%) of the respondents indicated having a continuing relationship with their marriage counsellors because of the need to consult them on marital issues. Conclusion Marriage counsellors, though hardly reported in fertility studies, are important ‘constituents’ of the social network in the Zambian society. This is because marriage counsellors are trusted sources of information about marital issues and often consulted about family planning but perceived not to have the correct information about modern contraceptives. In this context, pre-MC offers a readily available, sustainable and culturally appropriate platform for disseminating accurate information about modern contraceptives provided in a private and personal manner. Therefore, the CBD strategy in Zambia can harness marriage counsellors by recruiting and training them as community agents.


2021 ◽  
Author(s):  
Cynthia Shih ◽  
Ruhi Pudipeddi ◽  
Arany Uthayakumar ◽  
Peter Washington

UNSTRUCTURED These are authors' responses to peer review of ms#24972.


In a social network the individuals connected to one another become influenced by one another, while some are more influential than others and able to direct groups of individuals towards a move, an idea and an entity. These individuals are named influential users. Attempt is made by the social network researchers to identify such individuals because by changing their behaviors and ideologies due to communications and the high influence on one another would change many others' behaviors and ideologies in a given community. In information diffusion models, at all stages, individuals are influenced by their neighboring people. These influences and impressions thereof are constructive in an information diffusion process. In the Influence Maximization problem, the goal is to finding a subset of individuals in a social network such that by activating them, the spread of influence is maximized. In this work a new algorithm is presented to identify most influential users under the linear threshold diffusion model. It uses explicit multimodal evolutionary algorithms. Four different datasets are used to evaluate the proposed method. The results show that the precision of our method in average is improved 4.8% compare to best known previous works.


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