Resident’s Alzheimer Disease and Social Networks Within a Nursing Home

A robust optimization model for influence maximization in social networks with heterogeneous nodes

“…Furthermore, as all the previous works used heuristic or approximation algorithm for finding the optimal solution, it is a trivial fact that the obtained solution in this research is better than the other research. Inspiring from [8,14,26,65], the alternative heuristic algorithms for finding the most influential nodes are the Greedy Degree Based (GDB); a simple heuristic that selects the k nodes with the largest degrees [3], Greedy Eigenvector Based (GEB); a simple heuristic that selects the k nodes with the largest eigenvector. GEB is suggested as a heuristic algorithm in [66], Greedy Betweenness Based (GBB); a simple heuristic that selects the k nodes with the largest Betweenness, Greedy Closeness Based (GCB); a simple heuristic that selects the k nodes with the largest Closeness, Greedy Pagerank Based (GPB); a simple heuristic that selects the k nodes with the largest Pagerank, Greedy Topsis Based (GTB); selecting the k nodes with the largest Topsis scores (this ranking method is proposed and used in [15,60,[67][68][69]), Greedy Sociability Based (GSB); Beside the existing simple method, the other simple heuristic can be selection of the k nodes with the largest social skill which is extracted by Social Skill questionnaire [61], and finally Random method (RND); simply select k random nodes in the graph.…”

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People often learn from each other, and this has important implications for such diverse things as how they find employment, what movies they see, which products they purchase, how technology becomes adopted, whether or not they participate in government programs or social events, and whether they protest [1][2][3]. The platform in which people can influence on the other's choices and decisions is social networks [4].

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Measuring the Impact of Bedroom Privacy on Social Networks in a Long-Term Care Facility for Hong Kong Older Adults: A Spatio-Social Network Analysis Approach

“…In Hong Kong, residents were found to have between 0.6 and 2.6 perceived social networks which suggests a prevalence of social isolation [ 13 , 16 ]. In the West, previous studies have found that residents with Alzheimer’s disease had noticeably less perceived social networks compared with those without [ 18 , 38 ]. Another study developed SNA as a framework to detect older adults in palliative care who were at risk of social isolation [ 37 ].…”

“…The authors tracked the social interactions between older adults in palliative care, and formal and informal caregivers to detect social isolation among older adults using SNA. Such knowledge would serve as a potential indicator of older adults’ psychological outcomes and cognitive functioning to better guide dementia care [ 38 , 39 , 40 , 41 ]. Abbott and colleagues [ 17 ] utilised network visualisation or sociograms in SNA to show the number of connections as well as the degree of social integration between residents and staff.…”