Non-Conservative Diffusion and its Application to Social Network Analysis

The random walk is fundamental to modeling dynamic processes on networks. Metrics based on the random walk have been used in many applications from image processing to Web page ranking. However, how appropriate are random walks to modeling and analyzing social networks? We argue that unlike a random walk, which conserves the quantity diffusing on a network, many interesting social phenomena, such as the spread of information or disease on a social network, are fundamentally non-conservative. When an individual infects her neighbor with a virus, the total amount of infection increases. We classify diffusion processes as conservative and non-conservative and show how these differences impact the choice of metrics used for network analysis, as well as our understanding of network structure and behavior. We show that Alpha-Centrality, which mathematically describes non-conservative diffusion, leads to new insights into the behavior of spreading processes on networks. We give a scalable approximate algorithm for computing the Alpha-Centrality in a massive graph. We validate our approach on real-world online social networks of Digg. We show that a non-conservative metric, such as Alpha-Centrality, produces better agreement with empirical measure of influence than conservative metrics, such as PageRank. We hope that our investigation will inspire further exploration into the realms of conservative and non-conservative metrics in social network analysis

Collaboration in sensor network research: an in-depth longitudinal analysis of assortative mixing patterns

Many investigations of scientific collaboration are based on statistical analyses of large networks constructed from bibliographic repositories. These investigations often rely on a wealth of bibliographic data, but very little or no other information about the individuals in the network, and thus, fail to illustrate the broader social and academic landscape in which collaboration takes place. In this article, we perform an in-depth longitudinal analysis of a relatively small network of scientific collaboration (N = 291) constructed from the bibliographic record of a research center involved in the development and application of sensor network and wireless technologies. We perform a preliminary analysis of selected structural properties of the network, computing its range, configuration and topology. We then support our preliminary statistical analysis with an in-depth temporal investigation of the assortative mixing of selected node characteristics, unveiling the researchers' propensity to collaborate preferentially with others with a similar academic profile. Our qualitative analysis of mixing patterns offers clues as to the nature of the scientific community being modeled in relation to its organizational, disciplinary, institutional, and international arrangements of collaboration.Comment: Scientometrics (In press

A Network Perspective of Economic Relations and Markets

A review of the research literature applying social network analy­ sis to different aspects of economics (markets, firms and economic organiza­ tions, policies and public administration) will be presented. The review will specifically explore the contributions that network analysis has made to the investigation of market relations and interactions between firms either at the level of whole economies or regional areas globalization (chapters 3 and 4), entrepreneurship and social development (chapter 4) and finally, contem­ porary transformation of economic relations and technological innovation (chapter 5). The introduction describes the methodological perspective of social network analysis and specifically its application to economic and histo­rical sources.A review of the research literature applying social network analy­ sis to different aspects of economics (markets, firms and economic organiza­ tions, policies and public administration) will be presented. The review will specifically explore the contributions that network analysis has made to the investigation of market relations and interactions between firms either at the level of whole economies or regional areas globalization (chapters 3 and 4), entrepreneurship and social development (chapter 4) and finally, contem­ porary transformation of economic relations and technological innovation (chapter 5). The introduction describes the methodological perspective of social network analysis and specifically its application to economic and histo­rical sources

Social network based approaches in the research on religion in Central-Eastern Europe

The network science, based on network theory is a fairly new and innovative field and its approaches are groundbreaking in many aspects. The social scientific applications of the network analysis methods and concepts have been built on the results of network science. Thus, the social network analysis of religious networks is grounded on the methodological principles and assumptions of network analysis, especially social network analysis as it has developed in recent years. In this study the author gives a review of the application of social network theory and social network analysis in the sociology of religion in Central and Eastern Europe. This approach of sociological study of religious faith and religious groups is usually based on empirical research and interpretation of the research results. The sociology of religion has a broader perspective in studying religious life, but the religious social networks usually mirror other characteristics of the studied religious entities and phenomena to make it an interesting subject of research

“Follow the Leader”: A Centrality Guided Clustering and Its Application to Social Network Analysis

Within graph theory and network analysis, centrality of a vertex measures the relative importance of a vertex within a graph. The centrality plays key role in network analysis and has been widely studied using different methods. Inspired by the idea of vertex centrality, a novel centrality guided clustering (CGC) is proposed in this paper. Different from traditional clustering methods which usually choose the initial center of a cluster randomly, the CGC clustering algorithm starts from a “LEADER”—a vertex with the highest centrality score—and a new “member” is added into the same cluster as the “LEADER” when some criterion is satisfied. The CGC algorithm also supports overlapping membership. Experiments on three benchmark social network data sets are presented and the results indicate that the proposed CGC algorithm works well in social network clustering

Analyzing Social Construction of Knowledge Online by Employing Interaction Analysis, Learning Analytics, and Social Network Analysis

This article examines research methods for analyzing social construction of knowledge in online discussion forums. We begin with an examination of the Interaction Analysis Model (Gunawardena, Lowe, & Anderson, 1997) and its applicability to analyzing social construction of knowledge. Next, employing a dataset from an online discussion, we demonstrate how interaction analysis can be supplemented by employing other research techniques such as learning analytics and Social Network Analysis that shed light on the social dynamics that support knowledge construction. Learning analytics is the application of quantitative techniques for analyzing large volumes of distributed data ( big data ) in order to discover the factors that contribute to learning (Long & Siemens, 2011, p. 34). Social Network Analysis characterizes the information infrastructure that supports the construction of knowledge in social contexts (Scott, 2012). By combining interaction analysis with learning analytics and Social Network Analysis, we were able to conceptualize the process by which knowledge construction takes place in online platforms

JARINGAN KOMUNIKASI ANTAR MAHASISWA PEMAIN JUDI ONLINE DALAM APLIKASI HIGGS DOMINO DI KOTA MALANG

The development of technology and information has enabled many aspects to be conducted digitally, leading to both positive and negative impacts for its users. One of the negative impacts is the increasing prevalence of online gambling phenomena among university students. Engaging in online gambling entails communication among players, thus forming a communication network. This study aims to analyze the communication network structure among university student online gamblers in the Higgs Domino application in the city of Malang. Additionally, it seeks to identify the factors that drive students to engage in online gambling practices. The method used in this research is descriptive qualitative. Data collection techniques involved interviews with research subjects conducted using snowball sampling theory. Once data was obtained, it was collected and analyzed using sociometric analysis techniques. Utilizing sociogram analysis via the UCINET 6.0 application, this study successfully identified a Y-shaped communication pattern within the network. Moreover, the research explored individual characteristic variables including age, level of formal education, level of social media ownership, and frequency of playing Higgs Domino online gambling as supportive factors in understanding the researched phenomenon. Based on interview results, it was found that individuals are interested in playing online gambling due to several factors: influence from friends or environment; desire to obtain a large amount of money quickly without working; seeking entertainment and feeling bored or saturated; easy internet access; and difficulty in self-control to refrain from gambling

Improving Bayesian statistics understanding in the age of Big Data with the bayesvl R package

The exponential growth of social data both in volume and complexity has increasingly exposed many of the shortcomings of the conventional frequentist approach to statistics. The scientific community has called for careful usage of the approach and its inference. Meanwhile, the alternative method, Bayesian statistics, still faces considerable barriers toward a more widespread application. The bayesvl R package is an open program, designed for implementing Bayesian modeling and analysis using the Stan language’s no-U-turn (NUTS) sampler. The package combines the ability to construct Bayesian network models using directed acyclic graphs (DAGs), the Markov chain Monte Carlo (MCMC) simulation technique, and the graphic capability of the ggplot2 package. As a result, it can improve the user experience and intuitive understanding when constructing and analyzing Bayesian network models. A case example is offered to illustrate the usefulness of the package for Big Data analytics and cognitive computing