Enhancing Data Forwarding Efficiency in SIoT with Multidimensional Social Relations

Effective data communication is a crucial aspect of the Social Internet of Things(SIoT)and continues to be a significant research focus.This paper proposes a data forwarding algorithm based on Multidimensional Social Relations(MSRR)in SIoT to solve this problem.The proposed algorithm separates message forwarding into intra-and cross-community forwarding by analyzing interest traits and social connections among nodes.Three new metrics are defined:the intensity of node social relationships,node activity,and community connectivity.Within the community,messages are sent by determining which node is most similar to the sender by weighing the strength of social connections and node activity.When a node performs cross-community forwarding,the message is forwarded to the most reasonable relay community by measuring the node activity and the connection between communities.The proposed algorithm was compared to three existing routing algorithms in simulation experiments.Results indicate that the proposed algorithmsubstantially improves message delivery efficiency while lessening network overhead and enhancing connectivity and coordination in the SIoT context.

Set pair three-way overlapping community discovery algorithm for weighted social internet of things

There are many problems in Social Internet of Things(IoTs),such as complex topology information,different degree of association between nodes and overlapping communities.The idea of set pair information grain computing and clustering is introduced to solve the above problems so as to accurately describe the similarity between nodes and fully explore the multi-community structure.A Set Pair Three-Way Overlapping Community Discovery Algorithm for Weighted Social Internet of Things(WSIoT-SPTOCD)is proposed.In the local network structure,which fully considers the topological information between nodes,the set pair connection degree is used to analyze the identity,difference and reverse of neighbor nodes.The similarity degree of different neighbor nodes is defined from network edge weight and node degree,and the similarity measurement method of set pair between nodes based on the local information structure is proposed.According to the number of nodes'neighbors and the connection degree of adjacent edges,the clustering intensity of nodes is defined,and an improved algorithm for initial value selection of k-means is proposed.The nodes are allocated according to the set pair similarity between nodes and different communities.Three-way community structures composed of a positive domain,boundary domain and negative domain are generated iteratively.Next,the overlapping node set is generated according to the calculation results of community node membership.Finally,experiments are carried out on artificial networks and real networks.The results show that WSIoT-SPTOCD performs well in terms of standardized mutual information,overlapping community modularity and F1.

Social and Economic Considerations for Creating Sustainable Climate Change Haven Communities

As Climate Change Haven Communities are constructed across the Northern Hemisphere, it will be necessary to attract two types of migrants to populate them. The first group consists of professionals and companies in eco-sustainable businesses, such as law firms, insurance companies, investment firms, banking, technological innovation, mass media, medical research and pharmaceutical research. The second group will consist of persons engaged in organic/eco-sustainable agriculture whose crops and animal husbandry practices can be transferred successfully to Climate Change Haven regions. The present research focuses on the social and economic variables that must be taken into account to insure that each new Climate Change Haven Community becomes successfully integrated with the local population and forms a cohesive, harmonious social structure. Examples are given from the United States, France, Spain, Portugal and Italy.

Community Detection Using Jaacard Similarity with SIM-Edge Detection Techniques

The structure and dynamic nature of real-world networks can be revealed by communities that help in promotion of recommendation systems.Social Media platforms were initially developed for effective communication,but now it is being used widely for extending and to obtain profit among business community.The numerous data generated through these platforms are utilized by many companies that make a huge profit out of it.A giant network of people in social media is grouped together based on their similar properties to form a community.Commu-nity detection is recent topic among the research community due to the increase usage of online social network.Community is one of a significant property of a net-work that may have many communities which have similarity among them.Community detection technique play a vital role to discover similarities among the nodes and keep them strongly connected.Similar nodes in a network are grouped together in a single community.Communities can be merged together to avoid lot of groups if there exist more edges between them.Machine Learning algorithms use community detection to identify groups with common properties and thus for recommen-dation systems,health care assistance systems and many more.Considering the above,this paper presents alternative method SimEdge-CD(Similarity and Edge between's based Community Detection)for community detection.The two stages of SimEdge-CD initiallyfind the similarity among nodes and group them into one community.During the second stage,it identifies the exact affiliations of boundary nodes using edge betweenness to create well defined communities.Evaluation of proposed method on synthetic and real datasets proved to achieve a better accuracy-efficiency trade-of compared to other existing methods.Our proposed SimEdge-CD achieves ideal value of 1 which is higher than existing sim closure like LPA,Attractor,Leiden and walktrap techniques.

Research on the Continuous Participation of Virtual Brand Community in Decision-Making Based on Social Influence Theory

Based on the social influence theory,the influence of virtual brand community members’perceived value on social influencing factors is discussed,and the influence of social influencing factors on virtual brand community members’continuous participation in decision-making is analyzed.Through an empirical analysis of the survey data of Xiaomi users in the Xiaomi Community,the results showed that the perceived value of virtual brand community members significantly and positively affects social influencing factors,which also significantly and positively affect the members’continuous participation in virtual brand communities.Therefore,only by sharing valuable information resources and improving the efficiency of information flow,thereby enhancing the perceived value of the community and increasing the stickiness of members to the virtual community,will we have an opportunity to enhance the interaction among members and effectively promote continuous participation in community activities through the strong bonds formed among members.

Community Workers’Social Support and Sleep Quality during the Coronavirus Disease 2019(COVID-19):A Moderated Mediation Model

To explore the relationship between social support and sleep quality of community workers in Wuhan during the coronavirus disease 2019(the COVID-19 infection epidemic),this research constructed a mediating effect model to explore the mediating psychological mechanism of social support influencing sleep quality of front-line community workers.A total of 500 front-line community workers in Wuhan were investigated.We used the perceived social support scale(PSSS),the Connor-Davidson Resilience Scale(CD-RISC),the perceived stress scale(PSS),and the Pittsburgh sleep quality index(PSQI)to measure social support,psychological resilience,perceived stress and sleep quality.Specifically,the higher the PSQI,the worse the sleep quality.Pearson correlation structural equation model was used to analyze the relationship between these factors.The results showed that:(1)There was a significant negative correlation between social support,psychological resilience,and perceived stress of community workers and PSQI,that means,the higher the level of social support,psychological resilience,and perceived stress,the higher the sleep quality.(2)Social support positively predicted psychological resilience and perceived stress,and perceived stress negatively predicted PSQI.(3)Social support can affect sleep quality through the mediating role of psychological resilience and perceived stress,and the mediating role includes two paths:the single mediating role of perceived stress and the chain mediating role of psychological resilience-perceived stress.(4)Gender moderates the relation-ship between social support and perceived stress,and the influence of social support on perceived stress of women is higher than that of men.Gender moderates the relationship between psychological resilience and PSQI,and only women’s psychological resilience had a negatively predictive effect on PSQI,while men did not,which means that psychological resilience of female frontline community workers can positively predict sleep quality.This research reveals the relationship between social support and sleep quality and its mechanism and verifies that social support can indirectly affect physical health through psychological resilience and perceived stress.It provides reference suggestions and intervention guidance for improving the sleep quality of community workers.

An Extended Fuzzy-DEMATEL System for Factor Analyses on Social Capital Selection in the Renovation of Old Residential Communities

China has been promoting the renovation of old residential communities vigorously.Due to the financial pressure of the government and the sustainability of the renovation of old residential communities,public-private partnerships(PPP)have already gained attention.The selection of social capital is key to improving the efficiency of the PPP model in renovating old residential communities.In order to determine the influencing factors of social capital selection in the renovation of old residential communities,this paper aims to find an effective approach and analyze these factors.In this paper,a fuzzy decision-making and trial evaluation laboratory(fuzzy-DEMATEL)technique is extended and amore suitable systemis developed for the selection of social capital using the existing group decisionmaking theory.In the first stage,grounded theory is used to extract the unabridged key influencing factors for social capital selection in the renovation of old residential communities.Secondly,by considering the impact of expert weights,the key influencing factors are identified.The interactions within these influencing factors are discussed and the credibility of the results is verified by sensitivity analysis.Finally,these key influencing factors are sorted by importance.Based on the results,the government should focus on a technical level,organizationalmanagement abilities,corporate reputation,credit status,etc.This study provides the government with a theoretical basis and a methodology for evaluating social capital selection.

Identifying influential nodes in social networks via community structure and influence distribution difference

This paper aims to effectively solve the problem of the influence maximization in social networks.For this purpose,an influence maximization method that can identify influential nodes via the community structure and the influence distribution difference is proposed.Firstly,the network embedding-based community detection approach is developed,by which the social network is divided into several high-quality communities.Secondly,the solution of influence maximization is composed of the candidate stage and the greedy stage.The candidate stage is to select candidate nodes from the interior and the boundary of each community using a heuristic algorithm,and the greedy stage is to determine seed nodes with the largest marginal influence increment from the candidate set through the sub-modular property-based Greedy algorithm.Finally,experimental results demonstrate the superiority of the proposed method compared with existing methods,from which one can further find that our work can achieve a good tradeoff between the influence spread and the running time.

The influence of marital status on the social dysfunction of schizophrenia patients in community

Objective:Estimate predictive associations of marital status with social dysfunction in schizophrenia patients.Methods:817 schizophrenia patients lived in the community of Dongguan,Guangdong province,China,were investigated with the Social Disability Screening Schedule(SDSS)during a three-month period(1.2010e3.2010).The demographic data were harvested.The c2 test,t test,and fisher's exact were used for comparisons between groups,as appropriate.Multinomial logistic regression(MLR)was used to analyze the predictive associations of demographic variables to the grading of social dysfunctions.Results:The study group consisted of male and female patients aged 16e59 years,407 females,and 410 males with the mean age(40.7±9.5)years.Analysis of the data revealed significant differences in course of disease and marital status among patients(with and without dysfunction).The married patient made a significant difference with divorced/widowed patient in mildlyemoderately social dysfunction.There was a significant difference in married and never-married patient with mildly and profoundly social dysfunction.Significant differences were noticed in the self care and occupational roles of the married patient with that of the never-married.Conclusion:This study confirmed that bad marital status is associated with higher odds of social dysfunction among patients with schizophrenia living in the community.These effects should be included in considerations of public health investments in preventing and treating mental disorders.

Community Discovery with Location-Interaction Disparity in Mobile Social Networks

With the fast-growth of mobile social network, people＇ s interactions are frequently marked with location information, such as longitude and latitude of visited base station. This boom of data has led to considerable interest in research fields such as user behavior mining, trajectory discovery and social demographics. However, there is little research on community discovery in mobile social networks, and this is the problem this work tackles with. In this work, we take advantage of one simple property that people in different locations often belong to different social circles in order to discover communities in these networks. Based on this property, which we referred to as Location-lnteraction Disparity （LID）, we proposed a state network and then define a quality function evaluating community detection results. We also propose a hybrid community- detection algorithm using LID tor discovering location-based communities effectively and efficiently. Experiments on synthesis networks show that this algorithm can run effectively in time and discover communities with high precision. In realworld networks, the method reveals people＇s different social circles in different places with high efficiency.

Effect of cognitive behavioral group therapy on rehabilitation of community patients with schizophrenia:A short-term randomized control trial

BACKGROUND The efficacy of cognitive behavioral group therapy(CBGT)for cognitive dysfunction and negative symptoms of schizophrenia is established,but more evidence is required.AIM To assess the effectiveness of CBGT combined with mental health education as a treatment for schizophrenia compared with mental health education alone.METHODS In all,120 schizophrenia out-patients were randomized into CBGT combined with mental health education or single mental health education.The primary outcomes were positive and negative symptoms,cognitive function,excitatory factor,anxiety and depression symptom improvements on the positive and negative syndrome scale score.Secondary outcome measures included social function and drug compliance.RESULTS There were significant differences between CBGT combined with mental health education and single mental health education on measures of positive and negative symptoms,cognitive functions,excitatory factor,anxiety and depression symptoms,and social functions.No other significant difference in outcomes was observed.CONCLUSION CBGT combined with mental health education may be relevant beneficial treatment method in reducing symptoms,cognitive and social functions of patients with schizophrenia.

A Combination Approach to Community Detection in Social Networks by Utilizing Structural and Attribute Data

Community detection is one of the important tasks of social network analysis. It has significant practical importance for achieving cost-effective solutions for problems in the area of search engine optimization, spam detection, viral marketing, counter-terrorism, epidemic modeling, etc. In recent years, there has been an exponential growth of online social platforms such as Twitter, Facebook, Google+, Pinterest and Tumblr, as people can easily connect to each other in the Internet era overcoming geographical barriers. This has brought about new forms of social interaction, dialogue, exchange and collaboration across diverse social networks of unprecedented scales. At the same time, it presents new challenges and demands more effective, as well as scalable, graphmining techniques because the extraction of novel and useful knowledge from massive amount of graph data holds the key to the analysis of social networks in a much larger scale. In this research paper, the problem to find communities within social networks is considered. Existing community detection techniques utilize the topological structure of the social network, but a proper combination of the available attribute data, which represents the properties of the participants or actors, and the structure data of the social network graph is promising for the detection of more accurate and meaningful communities.

Community Detection in Dynamic Social Networks

There are many community detection algorithms for discovering communities in networks, but very few deal with networks that change structure. The SCAN (Structural Clustering Algorithm for Networks) algorithm is one of these algorithms that detect communities in static networks. To make SCAN more effective for the dynamic social networks that are continually changing their structure, we propose the algorithm DSCAN (Dynamic SCAN) which improves SCAN to allow it to update a local structure in less time than it would to run SCAN on the entire network. We also improve SCAN by removing the need for parameter tuning. DSCAN, tested on real world dynamic networks, performs faster and comparably to SCAN from one timestamp to another, relative to the size of the change. We also devised an approach to genetic algorithms for detecting communities in dynamic social networks, which performs well in speed and modularity.

Understanding the factors influencing user intention to continue contributing knowledge in social Q&A communities

Purpose:A social question & answer(SQA) community's long-term sustainability depends on its members' willingness to stay and contribute their knowledge continuously in the community.This research aims to investigate the critical factors which influence users' intention to continue contributing knowledge in the SQA community.Design/methodology/approach:Grounded on information systems(IS) continuance theory,this study put forward a model of the factors that influence SQA community members' intention to continue contributing knowledge.Survey was conducted to gather data from knowledge contributors of four major Chinese SQA communities(Baidu Knows,Sina iAsk,Soso Ask and Yahoo! Knowledge).By using the partial least squares(PLS) technique,research hypotheses derived from the proposed model were empirically validated.Findings:Except enjoyment in helping others and knowledge self-efficacy,all other factors including extrinsic reward,reputation enhancement,realization of self-worth,perceived usefulness,attitude towards knowledge contribution,and satisfaction exert significant impacts on users' continuance intentions in an SQA community.Research limitations:First,important factors such as the ease of use of information systems which may influence users' continuance intentions were not investigated in the study.Second,the study sample needs to be enlarged,and users of smaller SQA communities should also be included,to make the results more representative.Practical implications:This study will help SQA community designers and managers develop or improve incentive mechanisms to attract more people to contribute their knowledge and promote the development of the SQA community.Originality/value:This study improves the previous research models and puts forward a model of user continuance intention to contribute knowledge in an SQA community.It will extend the understanding of SQA community users' intention to continue contributing knowledge by distinguishing these users' different roles and focusing only on knowledge contributors.

Application of Social Media Tool in Disaster Management in Disaster-Prone Communities in Freetown, Sierra Leone

This study used both the “digital divide” and “attribution theory” to analyze the propensity of social media use by disaster-prone communities. The study focused on the variables that may affect how social media is used for disaster management. Structural equation modeling (SEM) was utilized in the study to analyze the data and test the hypotheses after using a survey questionnaire to collect the data. The study’s findings show that: 1) communities that are vulnerable to disasters are less likely to use social media for disaster management, 2) personal effort and intention to use social media for disaster management are positively correlated, and 3) task complexity and intention to use social media for disaster management are negatively correlated. The study added to the body of knowledge regarding the role social media plays in disaster management.

Correlation between social support and depression in elderly stroke patients in the sequelae stage from five communities of Shanghai, China

Using a convenience sampling method, 81 aged patients who were between 6 and 24 months post-stroke were selected from five communities of Shanghai, China. The correlations between social support and depression, as well as other influential factors, were investigated. Results revealed that 46% （37/81） of the stroke patients suffered from depression. Depression was correlated with social support total scores and the two domains of subjective support and utilization. Older age, male gender, low income, absence of spouse and cerebral hemorrhage were risk factors for depression and identify more vulnerable groups.

Roles of mutual help of local community networks in community health activities: Improvement for the quality of life of older people in Thailand

Objectives: This study aimed to describe work and activities of community networks focusing on the improvement of the quality of life (QOL) of older people in Thailand.The understanding of the work can help enhancing the community development and strengthening of local communities and their networks.Methods: Qualitative methods including in-depth interview,observation,and focus group discussion were employed to the study.64 participants participated to the study and were recruited from 4 key actors within the community.Content analysis was used to analyze the obtained data.This study was conducted in 6 local administrative organizations (LAOs) which selected from the outstanding areas of the project.Each LAO represents one sub-district of the regions of Thailand namely;(1) the upper north,(2) the lower north,(3)the upper eastern,(4) the lower eastern,(5) the central and (6) the south.Results: The findings of this study were categorized into three main themes: (1) Social capital including people in the community,social groups,and organizations,(2) Mutual help/collaboration activities composed of six sets of activities related to social capitals working on the improvement of QOL of older people,and (3) Impacts of the mutual help/collaboration activities on older people and local communities who help to improve of QOL of older people.Conclusion: The findings are important features for the community development.These themes should be recommended for community nurses,health related groups and organizations for the improvement of QOL of older people in the community.

SAFT-VNDN: A Socially-Aware Forwarding Technique in Vehicular Named Data Detworking

Vehicular Social Networks(VSNs)is the bridge of social networks and Vehicular Ad-Hoc Networks(VANETs).VSNs are promising as they allow the exchange of various types of contents in large-scale through Vehicle-to-Vehicle(V2V)and Vehicle-to-Infrastructure(V2I)communication protocols.Vehicular Named Data Networking(VNDN)is an auspicious communication paradigm for the challenging VSN environment since it can optimize content dissemination by decoupling contents from their physical locations.However,content dissemination and caching represent crucial challenges in VSNs due to short link lifetime and intermittent connectivity caused by vehicles’high mobility.Our aim with this paper is to improve content delivery and cache hit ratio,as well as decrease the transmission delay between end-users.In this regard,we propose a novel hybrid VNDN-VSN forwarding technique based on social communities,which allows requester vehicles to easily find the most suitable forwarder or producer among the community members in their neighborhood area.Furthermore,we introduce an effective caching mechanism by dividing the content store into two parts,one for community private contents and the second one for public contents.Simulation results show that our proposed forwarding technique can achieve a favorable performance compared with traditional VNDN,in terms of data delivery ratio,average data delivery delay,and cache hit ratio.

Advanced Community Identification Model for Social Networks

Community detection in social networks is a hard problem because of the size,and the need of a deep understanding of network structure and functions.While several methods with significant effort in this direction have been devised,an outstanding open problem is the unknown number of communities,it is generally believed that the role of influential nodes that are surrounded by neighbors is very important.In addition,the similarity among nodes inside the same cluster is greater than among nodes from other clusters.Lately,the global and local methods of community detection have been getting more attention.Therefore,in this study,we propose an advanced communitydetection model for social networks in order to identify network communities based on global and local information.Our proposed model initially detects the most influential nodes by using an Eigen score then performs local expansion powered by label propagation.This process is conducted with the same color till nodes reach maximum similarity.Finally,the communities are formed,and a clear community graph is displayed to the user.Our proposed model is completely parameter-free,and therefore,no prior information is required,such as the number of communities,etc.We perform simulations and experiments using well-known synthetic and real network benchmarks,and compare them with well-known state-of-the-art models.The results prove that our model is efficient in all aspects,because it quickly identifies communities in the network.Moreover,it can easily be used for friendship recommendations or in business recommendation systems.

Relationship between Social Capital and Cognitive Functions among Community-Based Elderly

The concept of social capital encompasses all relationships and networks among people in a local community or society as a whole. It has been found to be associated with the health and daily living status of people. Furthermore, an association between social capital and cognitive functions among the elderly has been suggested in recent years. However, the number of such reports is very few. Hence, the present study aims to evaluate the relationship between social capital and cognitive functions among the elderly living in a local community in Japan. A questionnaire survey was administered to 192 elderly individuals belonging to 10 neighborhood elderly groups. A regression analysis was conducted to determine the relationship between cognitive functions and the level of engagement with the local community, after adjusting for age, gender, and symptoms of depression. Valid responses were collected from 145 participants (mean age: 75.3 years). An analysis of the data concerning the level of engagement with the local community revealed a significant difference in the cognitive function scores between the group of people who indicated that they had someone they could consult (n = 69) and the group that indicated they had no one to consult (n = 76) (regression coefficient: -0.61, p = 0.0038, 95% confidence interval: -1.02 to -0.20). This result indicates the existence of a significant correlation between social capital and cognitive functions of the elderly living in a local community. In future studies, we need to investigate the same with a larger number of participants from a wider geographical area, and by incorporating more indicators for a comprehensive evaluation of social capital.