Social network structures and drought resilience: a case study of Iranian pastoralists in the Nodoushan watershed

Chapter 3 - Privacy-Preserving Social Network Integration, Analysis, and Mining

Applying the concept of organizational structure to social network analysis may well represent the power of members and the scope of their power in a social network. In this paper, we propose a data structure, called Community Tree, to represent the organizational structure in the social network. We combine the PageRank algorithm and random walks on graph to derive the community tree from the social network. In the real world, a social network is constantly changing. Hence, the organizational structure in the social network is also constantly changing. In order to present the organizational structure in a dynamic social network, we propose a tree learning algorithm to derive an evolving community tree. The evolving community tree enables a smooth transition between the two community trees and well represents the evolution of organizational structure in the dynamic social network. Experiments conducted on real data show our methods are effective at discovering the organizational structure and representing the evolution of organizational structure in a dynamic social network.

The rapid growth in popularity of online social networks provides new opportunities in computer science, sociology, math, information studies, biology, business, and more. Social network analysis (SNA) is a paramount technique supporting understanding social relationships and networks. Accordingly, certain studies and reviews have been presented focusing on information dissemination, influence analysis, link prediction, and more. However, the ultimate aim is for social network background knowledge and analysis to solve real-world social network problems. SNA still has several research challenges in this context, including users’ privacy in online social networks. Inspired by these facts, we have presented a survey on social network analysis techniques, visualization, structure, privacy, and applications. This detailed study has started with the basics of network representation, structure, and measures. Our primary focus is on SNA applications with state-of-the-art techniques. We further provide a comparative analysis of recent developments on SNA problems in the sequel. The privacy preservation with SNA is also surveyed. In the end, research challenges and future directions are discussed to suggest to researchers a starting point for their research.

BackgroundTobacco use is the single most preventable cause of death in the world. Evidence indicates that behaviours such as tobacco use can influence social networks, and that social network structures can influence behaviours. Social network analysis provides a set of analytic tools to undertake methodical analysis of social networks. We will undertake a systematic review to provide a comprehensive synthesis of the literature regarding social network analysis and tobacco use. The review will answer the following research questions: among participants who use tobacco, does social network structure/position influence tobacco use? Does tobacco use influence peer selection? Does peer selection influence tobacco use?MethodsWe will follow the Preferred Reporting Items for Systemic Reviews and Meta-Analyses (PRISMA) guidelines and search the following databases for relevant articles: CINAHL (Cumulative Index to Nursing and Allied Health Literature); Informit Health Collection; PsycINFO; PubMed/MEDLINE; Scopus/Embase; Web of Science; and the Wiley Online Library. Keywords include tobacco; smoking; smokeless; cigarettes; cigar and ‘social network’ and reference lists of included articles will be hand searched. Studies will be included that provide descriptions of social network analysis of tobacco use.Qualitative, quantitative and mixed method data that meets the inclusion criteria for the review, including methodological rigour, credibility and quality standards, will be synthesized using narrative synthesis. Results will be presented using outcome statistics that address each of the research questions.DiscussionThis systematic review will provide a timely evidence base on the role of social network analysis of tobacco use, forming a basis for future research, policy and practice in this area. This systematic review will synthesise the evidence, supporting the hypothesis that social network structures can influence tobacco use. This will also include exploring the relationship between social network structure, social network position, peer selection, peer influence and tobacco use across all age groups, and across different demographics. The research will increase our understanding of social networks and their impact on tobacco use, informing policy and practice while highlighting gaps in the literature and areas for further research.

Role of social networks in building household livelihood resilience under payments for ecosystem services programs in a poor rural community in China

Recommender system is the system which, by using automated information filtering technology, recommends products or services to the customers who are likely to be interested in. Those systems are widely used in many different Web retailers such as Amazon.com, Netfix.com, and CDNow.com. Various recommender systems have been developed. Among them, Collaborative Filtering (CF) has been known as the most successful and commonly used approach. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. However, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting in advance whether the performance of CF recommender system is acceptable or not is practically important and needed. In this study, we propose a decision making guideline which helps decide whether CF is adoptable for a given application with certain transaction data characteristics. Several previous studies reported that sparsity, gray sheep, cold-start, coverage, and serendipity could affect the performance of CF, but the theoretical and empirical justification of such factors is lacking. Recently there are many studies paying attention to Social Network Analysis (SNA) as a method to analyze social relationships among people. SNA is a method to measure and visualize the linkage structure and status focusing on interaction among objects within communication group. CF analyzes the similarity among previous ratings or purchases of each customer, finds the relationships among the customers who have similarities, and then uses the relationships for recommendations. Thus CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. Under the assumption that SNA could facilitate an exploration of the topological properties of the network structure that are implicit in transaction data for CF recommendations, we focus on density, clustering coefficient, and centralization which are ones of the most commonly used measures to capture topological properties of the social network structure. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. We explore how these SNA measures affect the performance of CF performance and how they interact to each other. Our experiments used sales transaction data from H department store, one of the well?known department stores in Korea. Total 396 data set were sampled to construct various types of social networks. The dependant variable measuring process consists of three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used UCINET 6.0 for SNA. The experiments conducted the 3-way ANOVA which employs three SNA measures as dependant variables, and the recommendation accuracy measured by F1-measure as an independent variable. The experiments report that 1) each of three SNA measures affects the recommendation accuracy, 2) the density's effect to the performance overrides those of clustering coefficient and centralization (i.e., CF adoption is not a good decision if the density is low), and 3) however though the density is low, the performance of CF is comparatively good when the clustering coefficient is low. We expect that these experiment results help firms decide whether CF recommender system is adoptable for their business domain with certain transaction data characteristics.

Local core expanding-based label diffusion and local deep embedding for fast community detection algorithm in social networks

Social network analysis is very useful in discovering the embedded knowledge in social network structures, which is applicable in many practical domains including homeland security, publish safety, epidemiology, public health, electronic commerce, marketing, and social science. However, social network data is usually distributed and no single organization is able to capture the global social network. For example, a law enforcement unit in Region A has the criminal social network data of her region; similarly, another law enforcement unit in Region B has another criminal social network data of Region B. Unfortunately, due the privacy concerns, these law enforcement units may not be allowed to share the data, and therefore, neither of them can benefit by analyzing the integrated social network that combines the data from the social networks in Region A and Region B. In this chapter, we discuss aspects of sharing the insensitive and generalized information of social networks to support social network analysis while preserving the privacy at the same time. We discuss the generalization approach to construct a generalized social network in which only insensitive and generalized information is shared. We will also discuss the integration of the generalized information and how it can satisfy a prescribed level of privacy leakage tolerance which is measured independently to the privacy-preserving techniques.

Cultures of the Central Highlands, New Guinea

Mating behaviour and the timing of reproduction can inhibit genetic exchange between closely related species; however, these reproductive barriers are challenging to measure within natural populations. Social network analysis provides promising tools for studying the social context of hybridization, and the exchange of genetic variation, more generally. We test how social networks within a hybrid population of California Callipepla californica and Gambel's quail Callipepla gambelii change over discrete periods of a breeding season. We assess patterns of phenotypic and genotypic assortment, and ask whether altered associations between individuals (association rewiring), or changes to the composition of the population (individual turnover) drive network dynamics. We use genetic data to test whether social associations and relatedness between individuals correlate with patterns of parentage within the hybrid population. To achieve these aims, we combine RFID association data, phenotypic data and genomic measures with social network analyses. We adopt methods from the ecological network literature to quantify shifts in network structure and to partition changes into those due to individual turnover and association rewiring. We integrate genomic data into networks as node-level attributes (ancestry) and edges (relatedness, parentage) to test links between social and parentage networks. We show that rewiring of associations between individuals that persist across network periods, rather than individual turnover, drives the majority of the changes in network structure throughout the breeding season, and that the traits involved in phenotypic/genotypic assortment were highly dynamic over time. Social networks were randomly assorted based on genetic ancestry, suggesting weak behavioural reproductive isolation within this hybrid population. Finally, we show that the strength of associations within the social network, but not levels of genetic relatedness, predicts patterns of parentage. Social networks play an important role in population processes such as the transmission of disease and information, yet there has been less focus on how networks influence the exchange of genetic variation. By integrating analyses of social structure, phenotypic assortment and reproductive outcomes within a hybrid zone, we demonstrate the utility of social networks for analysing links between social context and gene flow within wild populations.

Together We Can Improve Outcomes in Kidney Failure: Examining Social Networks in Hemodialysis

Context: The importance of the involved stakeholders and their networks in vocational education and training (VET) focussing on international transfer and cooperation is highlighted in various empirical studies. A systematic empirical survey of these by means of social network analysis, however, has hardly been applied to date. This article is concerned with the development of social capital in the course of network formation and its sustainability. The object of investigation is the funded European VET innovation project AI Pioneers within the Erasmus+ program of the European Union. The main objective of the project is to establish and expand an international network in the context of VET in order to support the exchange of expertise on the use of artificial intelligence (AI) in education. Approach: To answer the research questions, the first step was to combine theoretical approaches from a social network perspective from psychology in relation to the analysis of interpersonal trust, sociology regarding the social capital approach and business administration by addressing the roles of actors in innovation processes. Among others, the social network perspective in this study is based on the work of Granovetter as well as Marsden and Campbell. For the data collection, a fully structured interview questionnaire and a semi-structured interview guideline were developed based on the theoretical framework of the study. In the second step, a multi-perspective egocentric network analysis was carried out: Data on a total of N = 10 egocentric networks were collected from the funded partners in the AI Pioneers project to gain an overall picture of the combined social capital and network structures. For the visualisation of the network data, the type of structured and standardised network maps was used. Findings: Regarding the establishment of social capital in the analysed innovation project AI Pioneers, it can be emphasised that a total of 74 relationships have been recorded in the 10 egocentric networks combined. In line with the project objectives, the education sector is addressed by the majority of the analysed relationships (n = 54), with (technical) vocational schools making up a substantial part of these. Focussing on the sustainability of the surveyed network structures: Most of the analysed relationships already existed before the project start and were consolidated during it (n = 57), while new ones were also established (n = 17). In addition, the continuous development of mutual trust and the need for equal cooperation is emphasised: A relatively high level of mutual trust can be recorded overall in the analysed egocentric networks (n = 55), while a low mutual trust is present in 19 relationships which is described due to e.g. asymmetrical power relations or a lack of commitment. The results show that the relationships analysed primarily contribute their resources in the form of expertise and their networking knowledge to the egocentric networks. Furthermore, a high level of interest and willingness to support the AI Pioneers project can be captured, particularly due to the novelty of the topic and the application of AI in VET. Conclusions: The study makes a significant contribution to VET research and its methodological set by using social network analysis with a combination of qualitative approaches for analysing egocentric networks from multiple perspectives. The importance of allocating resources to the creation of social capital regarding cooperation, network building and the sustainable maintenance of established structures can be emphasised. In this respect the benefits of a network-based approach can be highlighted in the context of the Erasmus+ program and the partnerships for innovation on forward-looking topics. In addition, the development of the two structured survey instruments in this study can be emphasised, which can be further developed on the basis of future research. Further quantitative network analyses would be valuable for VET research, especially against the background of innovation drivers and network formation, such as market and trend-related drivers due to demands and developments in the field of AI in education.

In recent times, Social Network Analysis (SNA) has become a very important and interesting subject matter with regard to Artificial Intelligence (AI) in that a vast variety of processes, comprising animate and inanimate entities, can be examined by means of SNA. As a result, prediction tasks within social network structures have become significant research problems in SNA. Hidden facts and details about social network structures can be effectively and efficiently harnessed for training AI models with the goal of predicting missing links/ties within a given social network. Thus, important factors such as the individual attributes of spatial social actors, and the underlying patterns of relationship binding these social actors must be taken into consideration because these factors are relevant in understanding the nature and dynamics of a given social network structure. In this paper, we have proposed an interesting hybrid model: Representation Learning via Knowledge-Graph Embeddings and ConvNet (RLVECN). Our proposition herein is designed for examining, extracting, and learning meaningful facts for resolving link prediction problems about social network structures. RLVECN utilizes an edge sampling approach for exploiting the representations of a social graph, via learning the context of each actor with respect to its neighboring actors, with the goal of generating vector-space embeddings per actor which are further harnessed for innate representations via a Convolutional Neural Network (ConvNet) sublayer. Successively, these relatively low-dimensional representations are fed as input features to a downstream classifier for solving link prediction problems in a given social network. Our proposition, RLVECN, has been trained and evaluated on six (6) real-world benchmark social graph datasets.

The field of social networks focuses on the relationships among social actors, and on patterns that emerge from the structure of the social network and its implications (Wasserman and Faust’s Social Network Analysis: Methods and Applications). Social network research argues that actors (e.g., individuals or firms) are embedded within a network of relations, and that their behavior and choices cannot be studied independent of the social relations that shape and structure behavior. Social network perspective views relations among the social actors as ties and regular patterns in relationship as structure. Ties are the relational linkages that allow flow of resources between the actors, both tangible and intangible. Multiple actors form a web of relational ties, which can be either economic, social, or political. Networks can be of different types based on the content of the relational tie between the actors. For instance, collaboration ties between actors make a collaboration network or a co-author relation between actors makes a co-authorship network. Networks can also be at different levels of analysis—for instance, an intraorganizational friendship network is at the level of individuals while a network of intercountry trade relations is at the level of country. Ties between actors can be of different strengths (for instance, friends who meet daily versus once a year) and can also be negative or positive ties (e.g., competition networks versus collaboration networks). This article summarizes the latest research on social ties and network structure by focusing on the main thematic discussions in the field: (1) networks and strategic, governance behavior; (2) workplace networks; (3) collaboration and knowledge networks; (4) networks, personality, and individual differences; (5) entrepreneurial and family business networks; and (6) networks and social media. To ensure a comprehensive review of the topic, the article used search keywords, “networks,” or “network structure,” or “social networks,” or “social ties,” and was limited to articles in the top fourteen management journals, namely: Academy of Management Journal, Strategic Management Journal, Organization Science, Management Science, American Journal of Sociology, American Sociological Review, Administrative Science Quarterly, Academy of Management Review, Journal of Management Studies, Journal of Business Venturing, and Entrepreneurship Theory and Practice. The search was further limited to the six-year period from 2014–2019, since previous articles on organizational networks and brokerage in Oxford Bibliographies have summarized the research in this domain prior to 2014.

Due to easy and cost-effective ways, communication has amplified many folds among humans across the globe irrespective of time and geographic location. This has led to the construction of an enormous and a wide variety of social networks that is a network of social interactions or personal relations. Social network analysis (SNA) is the inspection of social networks in order to understand the participant’s arrangement and behaviour. Discovering communities from the social network has become one of the key research areas in SNA. Communities discovered from social networks facilitate its members so as to interact with relatable people who have similar or comparable interests. However, in present time, the enormous growth of social networks demands an intensive investigation of recent work carried out for identifying community division in social networks. This paper is an attempt to enlighten the ongoing developments in the domain of Community detection (CD) for SNA. Additionally, it sheds light on the algorithms which use meta-heuristic optimisation techniques to hit upon the community structure in social networks. Further, this paper gives a comparison of proposed methods in recent years and most frequently used optimisation approaches in the domain of CD. It also describes some application areas where CD methods have been used. This guides and encourages researchers to probe and take ahead the work in the area of detecting communities from social networks.