Social Network Integration Research Articles

Capturing Dynamic Interests of Similar Users for POI Recommendation Using Self-Attention Mechanism

The integration of location-based social networks and POI recommendation systems has the potential to enhance the urban experience by facilitating the exploration of new and relevant locales. The deployment of graph neural networks (GNNs) drives the development of POI recommendations, but this approach also brings with it the challenge of over-smoothing, where information propagation between nodes in the graph can lead to an excessive homogenization of the data. In prior works that utilized GNNs for POI recommendation, the bipartite graphs constructed from users and POIs as nodes failed to incorporate temporal dynamics, limiting the scope of the analysis to only spatial structure information. To circumvent this issue, the incorporation of a temporal component can be introduced during the aggregation process of graph convolution. In light of these considerations, the present study proposes a novel regionalized temporal GCN (RST-GCN) recommendation model that leverages self-attention mechanism to capture various levels of temporal information to better reflect the dynamic changes of time. By combining the graph’s spatial structure with geospatial features, similar users are distributed into distinct regional subgraphs, effectively avoiding the influence of non-similar users. The efficacy of the proposed model has been demonstrated through empirical evaluations conducted on two real-world datasets.

Коммуникативно-коннективная интеграция социальных сетей как средство развития речевых умений иноязычной письменной диалогической речи студентов бакалавриат

Currently, virtual communication on SNS is becoming an integral part of students' everyday and professional communication. Such communication mainly takes place in written form and, having a number of linguistic and discursive features, is an independent form of the functioning of a foreign language - foreign language written dialogic speech. The aim of this study is to consider the possibility of communicative and connective integration of social networks into the teaching of a foreign language for the development of undergraduate students' written conversational skills in a foreign language (in the case of English). We have formulated two main groups of skills that make up the speech component of foreign language communicative competence in the written dialogic speech: pragmatic and discursive skills. Pragmatic skills are associated with the need to convey pragmatic information: intonation and the emotional background of the message in the absence of direct eye contact between the interlocutors. Discursive skills are associated with the need to build a dialogue in such a way that, in the absence of direct eye contact, the interlocutor can unambiguously interpret the received message and continue communication. To develop effective technologies for the development of the listed groups of speech skills, we made an attempt to comprehend the implementation of a communicative approach to the introduction of social networks in the educational process, taking into account the principles of the connective paradigm of digital pedagogy. A communicative approach to the integration of social networks is implemented in the case of creating a series of network events in a social network. Connectiv-ism, on the other hand, implies such an organization of a network event that provokes a conversation between students in the comments under the published content in a foreign language. As an example of a technology that takes into account the communicative-connective approach to the integration of social networks in the teaching of a foreign language, we have proposed the technology of network communicative project activities, and also formulated a mechanism for its implementation in the VKontakte social network. This technology assumes the obligatory presence of two stages: constructive and communicative, corresponding to the principles of the communicative-connective approach. At the constructive stage, students are invited to publish foreign-language content on a social network in accordance with a specific communicative task, due to which a series of network events is created in the social network. At the communicative stage, students comment on each other's publications, engaging in written dialogic communication in a foreign language. As a result of the network communication of students as part of the work on the project task, a volume of correspondence in a foreign language is created in the social network, which can be analyzed in accordance with the criteria for the measurement of the level of the written conversational skills in a foreign language.

AGING AND THE SOCIAL BRAIN: THE ROLE OF SOCIAL NETWORKS IN ALZHEIMER'S DISEASE AND RELATED DEMENTIAS

Abstract Research suggests social connectedness reduces dementia risk and helps older adults with neuropathology maintain cognitive functionality and quality of life. However, little is known about the specific underlying social and biological mechanisms. This presentation provides an overview of three promising pathways through social bridging (i.e., cognitive enrichment through expansive social networks), social bonding (i.e., neuroendocrine benefits of integration in cohesive social networks), and social stress (i.e., HPA axis dysregulation resulting from social losses, role exits, and dysfunction or strain in relationships). It discusses how personal social network methodology combined with tests of general and social cognitive function and/or biomarkers can identify specific etiological mechanisms. These insights can be leveraged to develop policies and programs that support brain health and cognitive function in older adults. This presentation sets the stage for the remainder of the symposium, which presents empirical findings examining these mechanisms from a social network perspective.

Connectivity-Based Localization Scheme for Social Internet of Things

Different from the social network which only focuses on the interaction between people, the integration of social networks and the internet of things (IoT) leads to multi-directional interactions of human to human, human to thing, and thing to thing. The social IoT is composed of a large number of heterogeneous devices, which can improve the scalability of resource and service. Meanwhile, the heterogeneous devices have uneven computing power and different location information measurement types (e.g., the distance, angle, and hop count). Therefore, a localization approach with easy-to-obtain measurement data and low requirements on the computing power is needed. In this article, we propose a localization approach for the social IoT by combining the fuzzy rough set theory and the ridge regression extreme learning machine (RRELM). First of all, a location fingerprint database is constructed. Different from the traditional location fingerprint database, the location fingerprint database here stores the minimum hop counts between the reference node (RN) and the anchor node (AN) instead of the received signal strength (RSS). Second, the fuzzy rough set theory is used to compute the significant degree of each AN, and the ANs that contribute little to the positioning result are removed. This approach not only relieves the storage pressure of the location fingerprint database but also reduces the computational complexity of user position estimation. Third, the RRELM is trained by using the samples in the location fingerprint database. Finally, by inputting the newly collected minimum hop counts from the user to each AN into the trained RRELM, the user’s position is estimated. From the extensive experimental results, the proposed approach has high positioning accuracy and low computational complexity, which is suitable for the social IoT.

The Impact of Public Health Education on Migrant Workers’ Medical Service Utilization

Based on the dynamic monitoring survey data of China's migrant population (CMDS) in 2017, this study analyzes the impact of public health education on migrant workers' medical service utilization. The study found that public health education can significantly promote the utilization of migrant workers' medical services and has a greater effect on the older generation groups, those who received secondary and higher education, and those working in first-tier cities. By distinguishing different types of public health education, it is found that smoking control education has the most obvious effect. Further differentiating disease types, the study found that the promotion effect of receiving occupational disease education is the highest, while the effect of receiving STD/AIDS education is relatively low. The mechanism test indicates that public health education has significantly improved migrant workers' utilization of medical services by influencing their health literacy, social network, and psychological integration.

Identifying Cantonese rumors with discriminative feature integration in online social networks

To reduce the negative impacts of rumors on the real world, rumor detection on social networks has practical significance. Currently, the research on Chinese rumor detection is relatively comprehensive, but Cantonese rumors are much less investigated. As a main dialect of Chinese, Cantonese has more than 60 million speakers globally, but there are some great challenges in the Cantonese rumor detection. Firstly, there is no available benchmark dataset of Cantonese rumors. Secondly, it is a significant challenge to learn the unique linguistic characteristics of Cantonese. Thirdly, traditional rumor detection approaches cannot be directly applied to Cantonese rumors. Therefore, we propose a novel framework for Cantonese rumor detection using deep neural networks with feature fusion. To the best of our knowledge, it is the first study conducted on Cantonese rumor detection on social networks. Specifically, we build a Cantonese rumor dataset and a multi-domain Cantonese corpus. Next, a total of 27 statistical features are extracted and seven of them are newly proposed. Then, a novel deep learning model called BLA is designed to identify Cantonese rumors, which generates text and Jyutping embeddings using a further pre-trained BERT model and a CNN model. Moreover, the BLA model integrates the statistical and semantic features to implement the classification of Cantonese rumors. Experiments demonstrate that the BLA model achieves a remarkable Cantonese rumor detection performance with an F1 Score of 0.9225.

Molecular and spatial epidemiology of HCV among people who inject drugs in Boston, Massachusetts.

Integration of genetic, social network, and spatial data has the potential to improve understanding of transmission dynamics in established HCV epidemics. Sequence data were analyzed from 63 viremic people who inject drugs recruited in the Boston area through chain referral or time-location sampling. HCV subtype 1a was most prevalent (57.1%), followed by subtype 3a (33.9%). The phylogenetic distances between sequences were no shorter comparing individuals within versus across networks, nor by location or time of first injection. Social and spatial networks, while interesting, may be too ephemeral to inform transmission dynamics when the date and location of infection are indeterminate.

Social Networks Integration in Moroccan Higher Education during COVID-19: Opportunities and Challenges

The integration of social networks in Moroccan higher education has noticed significant practices ever since the COVID-19 pandemic outbreak to the present time. Apart from entertainment and informational purposes, social networks have accessed many classrooms, affecting their practices in a way or another. The aim of this study is to emphasize that social networks have managed to reach the Moroccan university and turn professors into influencers and networkers despite all the challenges. This study investigates the feedback of 20 students who have used social networks during COVID-19 pandemic with their professors as a substitute for face-to-face learning. Their responses focus on the influence of social networks on their learning in a virtual environment. Three research questions have been used, namely (i) what are the students’ attitudes towards the use of social networks? (ii) to what extent has the course content delivered via social networks influenced their learning practices? And (iii) what are their opinions about their professors’ pedagogics after taking the course via social networks? The study adopts a qualitative approach, making use of semi-structured interviews with 20 respondents. The content analysis and data coding process have also classified the themes in relation to the research questions. The findings have disclosed that the impact of the course content on students is very limited. They have also yielded very positive views about social networks. The results have stressed the fact that the use of social networks by professors is very informal, which makes students fail to commit themselves to the course.

Students’ Perception of Employing Social Media in the Educational Process in the Light of Education 4.0 Requirements

The aim of this study was to discern the students' perception of social media networks integration into the educational process. The study was conducted in seven universities in the Northern region of Jordan. To achieve this goal, authors used the descriptive survey approach. A stratified random sample of 762 students from both genders and different disciplines were targeted. For data collection purposes, a survey tool was designed, which consisted of one polar question, one open question and a questionnaire consisting of fifteen 5-point Likert scale paragraphs. Results indicated that 82.41% of respondents showed constructive attitudes toward the integration of social media to the educational process and indicated their top preferred social media applications. Furthermore, the findings revealed that there was a significant relationship between use behaviour and behavioural intentions, as well as a significant relationship between use behaviour and facilitating conditions. Finally, the results showed no significant statistical difference at α = 0.05 between the respondents’ statistical means pertaining to students' gender or field of study.

The casual effect of relational mobility on integration of social networks: An agent-based modeling approach.

Despite converging evidence for the importance of relational mobility on shaping people’s social experiences, previous work suggested mixed findings for its influence on the structure of sociocentric networks, which lays the basis for the development of all types of social relationships. Additionally, as it is timely and economically intractable to administer such longitudinal experiments in real-life settings, most previous work mainly relied on cross-sectional correlation analyses and provided limited causal evidence. The current research used an agent-based modeling approach to examine whether higher relational mobility (i.e., the number of opportunities to meet new people) would promote integration among social networks over time. Using parameters derived from survey data, we simulated how the integration of sociocentric social networks evolves under different levels of relational mobility. Based on the data of three network structural indicators, including modularity, global efficiency, and standard deviation of nodal betweenness, we obtained causal evidence supporting that higher relational mobility promotes greater network integration. These findings highlight the power of socioecological demands on our social experiences.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12144-022-03130-x.

Age integration in later life social networks and self-perceptions of aging: examining their reciprocal associations.

Relying on the age segregation theory (limited contact between age groups), this study examined the temporal reciprocal associations between age integration-the inclusion of older and younger people in one's personal network-and one's self-perceptions of aging (SPA). Data came from the 2014 and 2017 waves of the German Ageing Survey and focused on adults aged 60 and above (N = 5239). Age composition of the network was assessed as the number of kin and non-kin in the social network who are either more than10 years older or more than10 years younger than the respondent. A latent change score model assessed the bidirectional associations. The results showed that adults who had younger social network members, both kin and non-kin, had better SPA 3 years later. A positive SPA at baseline also predicted a higher number of younger non-kin and older non-kin relationships over time. These results stress the role of SPA in adults' social network as well as the role of age integration in shaping adults' SPA. Practitioners and policy makers should encourage connections between people of different ages and should strive to decrease the age segregation in society.

Collaborative Edge Computing for Social Internet of Things: Applications, Solutions, and Challenges

The paradigm of Internet of Things (IoT) has attracted much attention in both academia and industry areas in the past decades. Recently, the integration of IoT and social networks is advocated to promote further development. This article focuses on the applications, solutions, and challenges of Social Internet of Things (SIoT) over collaborative edge computing, which exploits the advantages of both mobile edge computing and social relationships among SIoT users. First, applications of collaborative offloading, caching, and streaming data processing are illustrated, followed by representative social-aware solutions, including auction, coalition game, and federated learning. Finally, several research challenges are described. The main contributions of this article are as follows: 1) we specify the role of social ties in traditional applications of IoT and its impacts on individual’s selection; 2) we elaborate the reasons why the presented three approaches can be applied in SIoT; and 3) the discussed challenges can contribute to the future development of secure, robust, and intelligent SIoT frameworks.

Wingman-Connect Program increases social integration for Air Force personnel at elevated suicide risk: Social network analysis of a cluster RCT

U.S. military suicides are increasing and disrupted relationships frequently precede them. Group-level interventions are needed that reduce future suicide vulnerability among healthy members and also ameliorate risk among those already suicidal. We examined whether our Wingman-Connect Program (W-CP) strengthened Air Force relationship networks and socially integrated at-risk members. Air Force personnel classes in training were randomized to W-CP or active control (cluster RCT), followed up at 1 and 6 months (94% and 84% retention). Data were collected in 2017–2019 and analyzed in 2020–2021. Participants were 1485 male and female Airmen in 215 technical training classes. W-CP training involved strengthening group bonds, skills for managing career and personal stressors, and diffusion of healthy norms. Active control was stress management training. Primary outcomes were social network metrics based on Airmen nominations of valued classmates after 1 month. Baseline CAT-SS >34 defined elevated suicide risk. W-CP increased social network integration, with largest impact for Airmen already at elevated suicide risk (n = 114, 7.7%). For elevated risk Airmen, W-CP improved all network integration metrics, including 53% average gain in valued connection nominations received from other Airmen (RR = 1.53, 95% CI = 1.12, 2.08) and eliminated isolation. No elevated risk Airmen in W-CP were isolates with no valued connections after 1-month vs. 10% among controls (P < .035). In contrast to at-risk controls, at-risk W-CP Airmen increased connections after intervention. W-CP's effect on a key indicator, ≥2 connections, was still greater 2–4 months after classes disbanded (6-months). Wingman-Connect Program built enhanced suicide protection into unit relationship networks and counteracted standard drift towards disconnection for at-risk Airmen, despite no explicit content targeting connections specifically to at-risk Airmen. Findings support a growing case for the unique contribution of group-level interventions to improve social health of broader military populations while also ameliorating risk among individuals already at elevated suicide risk.

Universal patterns of long-distance commuting and social assortativity in cities

Millions commute to work every day in cities and interact with colleagues, partners, friends, and strangers. Commuting facilitates the mixing of people from distant and diverse neighborhoods, but whether this has an imprint on social inclusion or instead, connections remain assortative is less explored. In this paper, we aim to better understand income sorting in social networks inside cities and investigate how commuting distance conditions the online social ties of Twitter users in the 50 largest metropolitan areas of the United States. An above-median commuting distance in cities is linked to more diverse individual networks, moreover, we find that longer commutes are associated with a nearly uniform, moderate reduction of overall social tie assortativity across all cities. This suggests a universal relation between long-distance commutes and the integration of social networks. Our results inform policy that facilitating access across distant neighborhoods can advance the social inclusion of low-income groups.

Why the cognitive "fountain of youth" may be upstream: Pathways to dementia risk and resilience through social connectedness.

Research suggests social connectedness may help older adults with dementia maintain cognitive functionality and quality of life. However, little is known about its specific social and biological mechanisms. This paper proposes two pathways through social bridging (i.e., cognitive enrichment through expansive social networks) and bonding (i.e., neuroendocrine benefits of integration in cohesive social networks). We provide preliminary evidence for these pathways using neuroimaging, cognitive, and egocentric social network data from the Social Networks and Alzheimer's Disease (SNAD) study (N=280). We found that network size, density, and presence of weak ties (i.e., social bridging) moderated the association between brain atrophy and cognitive function, while marriage/cohabitation (i.e., social bonding) moderated the association between perceived stress and cognitive function. We argue that social connectedness may have downstream implications for multiple pathophysiological processes in cognitive aging, even negating existing structural damage to the brain, making it a strong candidate for clinical or policy intervention.

SPY-BOT: Machine learning-enabled post filtering for Social Network-Integrated Industrial Internet of Things

A far-reaching expansion of advanced information technology enables ease and seamless communications over online social networks, which have been a de facto premium correspondents in the current cyber world. The ever-growing social network data has gained attention in recent years and can be handy for industrial revolution 4.0. With the integration of social networks with the Internet of Things being noticed in different industries to enhance human involvement and increase their productivity, security in such networks is increasingly alarming. Vulnerabilities can be characterized in the form of privacy invasion, leading to hazardous contents, which can be detrimental to social media actors and in turn impact the processes of the overall Social Network-Integrated Industrial Internet of Things (SN-IIoT) ecosystem. Despite this prevalence, the current platforms do not have any significant level of functionality to capture, process, and reveal unhealthy content among the social media actors. To address those challenges by detecting hazardous contents and create a stable social internet environment within IIoT, a statistical learning-enabled trustworthy analytic tool for human behaviors has been developed in this paper. More specifically, this paper proposes a machine learning (ML)-enabled scheme SPY-BOT, which incorporates a hybrid data extraction algorithm to perform post-filtering that arbitrates the users’ behavior polarity. The scheme creates class labels based on the featured keywords from the decision user and classifies suspicious contacts through the aid of ML. The results suggest the potential of the proposed approach to classify the users’ behavior in SN-IIoT.

Animal social networks: an introduction for complex systems scientists

AbstractMany animals live in societies where individuals frequently interact socially with each other. The social structures of these systems can be studied in depth by means of network analysis. A large number of studies on animal social networks in many species have in recent years been carried out in the biological research field of animal behaviour and have provided new insights into behaviour, ecology and social evolution. This line of research is currently not so well connected to the field of complex systems as could be expected. The purpose of this article is to provide an introduction to animal social networks for complex systems scientists and highlight areas of synergy. We believe that an increased integration of animal social networks with the interdisciplinary field of complex systems, and networks would be beneficial for various reasons. Increased collaboration between researchers in this field and biologists studying animal social systems could be valuable in solving challenges that are of importance to animal social network research. Furthermore, animal social networks provide the opportunity to investigate hypotheses about complex systems across a range of natural real-world social systems. In this article, we describe what animal social networks are and main research themes where they are studied; we give an overview of the methods commonly used to study animal social networks; we highlight challenges in the study of animal social networks where complex systems expertise may be particularly valuable; and we consider aspects of animal social networks that may be of particular interest to complex systems researchers. We hope that this will help to facilitate further interdisciplinary collaborations involving animal social networks and further integration of these networks into the field of complex systems.

Smart opinion formation maximization to improve political education of undergraduates in Social Information‐Centric Networking

Social Information‐Centric Networking (SICN) is an integration of Social Network (SN) and ICN, which is a popular paradigm as most of contents in SN depend on the transmission and interaction of ICN. In SICN, the research on opinion formation maximization is very important. Therefore, this paper considers the scenario regarding the political education of undergraduates and proposes a heuristic opinion formation maximization algorithm, including two stages. (a) The opinion formation model is built based on individual intimacy and social influence. (b) In order to make sure the opinion formation maximization, the collaborative evolution model is built. By comparing with three latest researches, the proposed heuristic algorithm can save the required identification time by 0.379 hour, 0.687 hour and 0.889 hour respectively. The experimental results further demonstrate that the research on opinion formation maximization in SICN makes for the improvement on the political education of undergraduates.

Age-related change in adult chimpanzee social network integration.

BackgroundSocial isolation is a key risk factor for the onset and progression of age-related disease and mortality in humans. Nevertheless, older people commonly have narrowing social networks, with influences from both cultural factors and the constraints of senescence. We evaluate evolutionarily grounded models by studying social aging in wild chimpanzees, a system where such influences are more easily separated than in humans, and where individuals are long-lived and decline physically with age.MethodologyWe applied social network analysis to examine age-related changes in social integration in a 7+ year mixed-longitudinal dataset on 38 wild adult chimpanzees (22 females, 16 males). Metrics of social integration included social attractivity and overt effort (directed degree and strength), social roles (betweenness and local transitivity) and embeddedness (eigenvector centrality) in grooming networks.ResultsBoth sexes reduced the strength of direct ties with age (males in-strength, females out-strength). However, males increased embeddedness with age, alongside cliquishness. These changes were independent of age-related changes in social and reproductive status. Both sexes maintained highly repeatable inter-individual differences in integration, particularly in mixed-sex networks.Conclusions and implicationsAs in humans, chimpanzees appear to experience senescence-related declines in social engagement. However, male social embeddedness and overall sex differences were patterned more similarly to humans in non-industrialized versus industrialized societies. Such comparisons suggest common evolutionary roots to ape social aging and that social isolation in older humans may hinge on novel cultural factors of many industrialized societies. Lastly, individual and sex differences are potentially important mediators of successful social aging in chimpanzees, as in humans. Lay summary: Few biological models explain why humans so commonly have narrowing social networks with age, despite the risk factor of social isolation that small networks pose. We use wild chimpanzees as a comparative system to evaluate models grounded in an evolutionary perspective, using social network analysis to examine changes in integration with age. Like humans in industrialized populations, chimpanzees had lower direct engagement with social partners as they aged. However, sex differences in integration and older males’ central positions within the community network were more like patterns of sociality in several non-industrialized human populations. Our results suggest common evolutionary roots to human and chimpanzee social aging, and that the risk of social isolation with age in industrialized populations stems from novel cultural factors.

Exploring qualities present in current versus dissolved cross-group friendships

In general, cross-group friendships are less stable than same-group friendships. What conditions are present in currently existing versus dissolved cross-group friendships? In order to examine qualities that may influence cross-group friendship stability we compared current and dissolved friendships, including cross-group friendships. Cross-group friendships exist in various group domains, some more easily categorizable than others. That is, sometimes it is easy to tell that a relationship is cross-group (e.g., cross-race), and other times this is less clear (e.g., cross-socio-economic status). Thus, we compared current and dissolved friendships across both a more and a less easily categorizable group domain. In this study, participants reported on their current and dissolved friendships, and we found that, overall, friendship influencing qualities such as closeness, similarity, and social network integration (i.e., becoming friends with the friends of one’s own friends) were present to a greater extent in current versus dissolved friendships. This was the case for both cross-group and same-group friendships. These qualities may influence cross-group friendship stability.