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.

Analysis of the Current Situation and Influencing Factors of Social Isolation Among the Elderly in the Community

Objective: To investigate the current situation of social isolation among the elderly in the community, and to analyze its influencing factors. Methods: A total of 265 elderly people were selected to conduct the survey using the general information questionnaire and the Chinese version of the social isolation scale for the elderly. Results: The social isolation score of the elderly was (20.15 ± 0.23). Factors such as age, education level, economic status, and social participation ability influenced the social isolation score (P < 0.05). Conclusion: The social isolation of the elderly is more serious, and the social isolation can be alleviated by improving the level of education and the economic situation and strengthening social participation.

Why Do the Imaginary and Imaginary Communities Emerge in Social Theory? National Imaginary in the Context of Globalization

The end of modernism questions reality and its theoretical description,and various attempts of postmodern rethinking of the social emerge-from denial,assertion of the disappearance of the social to its salvation through radical rethinking.Theorizing around the imaginary emerges and continues as a result of this rethinking.Cornelius Castoriadis,for example,absolutizes the concept of the imaginary,which,in his opinion,even contains the rational.Charles Taylor gives imaginary,though important,but limited role as a background knowledge.Speaking of the imaginary,one cannot,of course,ignore Benedict Andersen’s imaginary communities.According to Andersen,the“imagination”of a nation,like any other community,reflects not the fact that they are“invented”or“constructed”but that they are the result of human practice,that social reality is a socio-historical and cultural product.Nation differs from other communities in the style of representation,namely,the representation-understanding,first of all,of space and time.A specific moment in time is connected not only with the past and future,but also with the same moment in another time and space measurement.People in this case,communicate and socialize through books,newspapers,and national languages.Most importantly,the nation as an imaginary community opposes itself to other communities,distinguishes itself from them,and strives for autonomy.And the guarantee of autonomy is the sovereign state,the nation-state,therefore the nation is always connected with the state and the territory of the exercise of its monopoly right.That is,the nation is an imaginary community that is real only to the extent that it is correlated with the modern territorial state.According to the author,the transformation of the national imaginary under the conditions of globalization is characterized by the loss of attachment to the territory,by the fact that territoriality ceases to be the main,organizing principle of social life.Social practices are increasingly formed beyond borders,belonging to ethnicity,national identity is not determined by territory and citizenship.Despite this,we must not forget that globalization is not a finished project.The transformation of the national imaginary should be viewed not only as the emergence of new imaginary communities,whether national or transnational,but also against the backdrop of interaction and even struggle between traditional forms of social practices and new ones,as evidenced by the growth of ethnic conflicts and separatist movements.According to Appudurai,this is also a consequence of globalization processes.And how this confrontation will end,the question remains open.

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.

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.

Socialization, Traffic Distribution and E-Commerce Trends:An Interpretation of the “Pinduoduo” Phenomenon

The article takes China’s e-commerce as the research object.Starting from the macro level of e-commerce development and taking the rapid rise of“Pinduoduo”as an example,it discusses the“traffic dilemma”and its influence in the traditional e-commerce platform.This discovers the internal mechanism of mobile e-commerce to solve the problem of traffic distribution mechanism by socialization.After that,this study compares the difference between traditional e-commerce and social-commerce systematically,and concludes that traditional e-commerce platform is a necessary process of the development of social-commerce.Socialization is an important trend of the development of traditional e-commerce and social-commerce will promote the realization of C2B model.

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.

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.

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.

Research on Communication Mechanism and Effect of Social E-Commerce in China

With the popularization of the Internet and the change of residents' consumption concept, e-commerce brings convenience to people's life and work, and network consumption has become a mainstream consumption mode. With the continuous expansion of e-commerce business, social network has become the main mode of Internet economic development with a rapid trend. Social network has abundant customer resources, which continuously influences people's life and work style. The advent of network economy era has an important impact on the development of global e-commerce industry. As a product of the development of the network economy era, electronic commerce, with its unique advantages, has played a decisive role in the form of global economic growth and commodity trading mode. In this paper, through the perspective of social network, the domestic e-commerce industry was actively explored, hoping to provide a theoretical reference for the rapid development of related industries in China.

Optimization and Innovation of the Operation Model of Mobile Social E-commerce under AI Empowerment:Taking the Female Community Platform“Little Red Book”as an Example

This article takes the female community platform“Little Red Book”as an example to explore the optimization and innovation of mobile community e-commerce operation mode under Artificial Intelligence(AI)empowerment.Firstly,the relevant concepts were defined,and then the unique attributes of mobile community e-commerce were analyzed.As a typical representative of mobile community e-commerce,Little Red Book introduces the background and characteristics of its platform,analyzes its mobile community operation mode,and focuses on exploring how to establish a mobile community e-commerce platform and effective operation mode under the empowerment of AI technology,to provide some reference and inspiration for the development and operation of Little Red Book and other e-commerce platform enterprises.

A Genetic Algorithm for Identifying Overlapping Communities in Social Networks Using an Optimized Search Space

There are currently many approaches to identify the community structure of a network, but relatively few specific to detect overlapping community structures. Likewise, there are few networks with ground truth overlapping nodes. For this reason,we introduce a new network, Pilgrim, with known overlapping nodes, and a new genetic algorithm for detecting such nodes. Pilgrim is comprised of a variety of structures including two communities with dense overlap,which is common in real social structures. This study initially explores the potential of the community detection algorithm LabelRank for consistent overlap detection;however, the deterministic nature of this algorithm restricts it to very few candidate solutions. Therefore, we propose a genetic algorithm using a restricted edge-based clustering technique to detect overlapping communities by maximizing an efficient overlapping modularity function. The proposed restriction to the edge-based representation precludes the possibility of disjoint communities, thereby, dramatically reducing the search space and decreasing the number of generations required to produce an optimal solution. A tunable parameterr allows the strictness of the definition of overlap to be adjusted allowing for refinement in the number of identified overlapping nodes. Our method, tested on several real social networks, yields results comparable to the most effective overlapping community detection algorithms to date.

Graph Transformer for Communities Detection in Social Networks

Graphs are used in various disciplines such as telecommunication,biological networks,as well as social networks.In large-scale networks,it is challenging to detect the communities by learning the distinct properties of the graph.As deep learning hasmade contributions in a variety of domains,we try to use deep learning techniques to mine the knowledge from large-scale graph networks.In this paper,we aim to provide a strategy for detecting communities using deep autoencoders and obtain generic neural attention to graphs.The advantages of neural attention are widely seen in the field of NLP and computer vision,which has low computational complexity for large-scale graphs.The contributions of the paper are summarized as follows.Firstly,a transformer is utilized to downsample the first-order proximities of the graph into a latent space,which can result in the structural properties and eventually assist in detecting the communities.Secondly,the fine-tuning task is conducted by tuning variant hyperparameters cautiously,which is applied to multiple social networks(Facebook and Twitch).Furthermore,the objective function(crossentropy)is tuned by L0 regularization.Lastly,the reconstructed model forms communities that present the relationship between the groups.The proposed robust model provides good generalization and is applicable to obtaining not only the community structures in social networks but also the node classification.The proposed graph-transformer shows advanced performance on the social networks with the average NMIs of 0.67±0.04,0.198±0.02,0.228±0.02,and 0.68±0.03 on Wikipedia crocodiles,Github Developers,Twitch England,and Facebook Page-Page networks,respectively.

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.

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.

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.

The Influence Factors of Collective Intelligence Emergence in Knowledge Communities Based on Social Network Analysis

The advent of the age of Information shifts the environment we live in from off-line to on-line. The prospect of Collective Intelligence (CI) is promising. Based on this background, the aim of this paper is to discover the emergence mechanism and influence factors of CI in knowledge communities using the method of quantitative and qualitative analysis. On the basis of the previous research work, our model theorizes that the two dimensions of social network (i.e., interactive network structure and participant’s characteristics) affect two references of effectiveness (i.e. group knowledge production and participation of group decision). And this hypothetical model is validated with simulation data from “Zhihu” community. Our model has been useful since it offers some inspirations and directions to promote the level of CI in knowledge communities.