Social Network Analysis

{Excerpt} Power no longer resides exclusively (if at all) in states, institutions, or large corporations. It is located in the networks that structure society. Social network analysis seeks to understand networks and their participants and has two main focuses: the actors and the relationships between them in a specific social context. The information revolution has given birth to new economies structured around flows of data, information, and knowledge. In parallel, social networks have grown stronger as forms of organization of human activity. Social networks are nodes of individuals, groups, organizations, and related systems that tie in one or more types of interdependencies: these include shared values, visions, and ideas; social contacts; kinship; conflict; financial exchanges; trade; joint membership in organizations; and group participation in events, among numerous other aspects of human relationships. Indeed, it sometimes appears as though networked organizations out compete all other forms of organization—certainly, they outpace vertical, rigid, command-and-control bureaucracies. When they succeed, social networks influence larger social processes by accessing human, social, natural, physical, and financial capital, as well as the information and knowledgecontent of these. (In development work, they can impact policies, strategies, programs, and projects—including their design, implementation, and results—and the partnerships that often underpin these.) To date, however, we are still far from being able to construe their public and organizational power in ways that can harness their potential. Understanding when, why, and how they function best is important. Here, social network analysis can help

Social Network Analysis: Applications: Event Programme

This seminar explores a number of current uses and applications of network analysis, including applications in social movement analysis, criminology, socio-linguistics and the study of literary networks. The aim of this is both to facilitate cross-pollination between domains of application and to offer exemplars of the method in action for those new to this approach. We hope that this seminar will prove to be an interesting introduction to network analysis for those previously unacquainted with it, which will both inspire and equip them to participate in the later seminars

PRNU-based image classification of origin social network with CNN

A huge amount of images are continuously shared on social networks (SNs) daily and, in most of cases, it is very difficult to reliably establish the SN of provenance of an image when it is recovered from a hard disk, a SD card or a smartphone memory. During an investigation, it could be crucial to be able to distinguish images coming directly from a photo-camera with respect to those downloaded from a social network and possibly, in this last circumstance, determining which is the SN among a defined group. It is well known that each SN leaves peculiar traces on each content during the upload-download process; such traces can be exploited to make image classification. In this work, the idea is to use the PRNU, embedded in every acquired images, as the “carrier” of the particular SN traces which diversely modulate the PRNU. We demonstrate, in this paper, that SN-modulated noise residual can be adopted as a feature to detect the social network of origin by means of a trained convolutional neural network (CNN)

Social network externalities and price dispersion in online markets.

Ample empirical studies in the e-commerce literature have documented that the price dispersion in online markets is 1) as large as that in offline markets, 2) persistent across time, and 3) only partially explained by observed eretailers’ attributes. Buying on the internet market is risky to consumers. First of all, consumers and the products they purchase are separated in time. There is a delay in time between the time consumers pay and the time they receive the orders. Second, consumers and the products they purchase are separated in space. Consumers cannot physically touch or examine the products at the point of purchase. As such, online markets involve an adoption process based on the interaction of consumers’ experiences in the form of references, recommendations, word of mouth, etc. The social network externalities introduced by the interaction of consumer’s experiences reduces the risk of seller choice and allows some sellers to charge higher prices for even homogeneous products. This research aims to study online market price dispersion from the social network externalities perspective. Our model posits that consumers are risk averse and assess the risk of having a satisfactory transaction from a seller based on the two dimensions of the seller’s social network externalities: quantity externality (i.e., the size of the seller’s social network) and quality externality (i.e., the satisfactory transaction probability of the seller’s social network). We further investigate the moderating effect of product value for consumers on the impact of social network externality on online market price dispersion. Our model yields several important propositions which we empirically test using data sets collected from eBay. We found that 1) both quantity externality and quality externality of social network are salient in driving online price dispersion, and 2) the salience of social network externality is stronger for purchase behavior in higher value product categories.network externalities, price dispersion, online markets, word of mouth

Quantifying Social Network Dynamics

The dynamic character of most social networks requires to model evolution of networks in order to enable complex analysis of theirs dynamics. The following paper focuses on the definition of differences between network snapshots by means of Graph Differential Tuple. These differences enable to calculate the diverse distance measures as well as to investigate the speed of changes. Four separate measures are suggested in the paper with experimental study on real social network data.Comment: In proceedings of the 4th International Conference on Computational Aspects of Social Networks, CASoN 201

The Researcher Social Network: a social network based on metadata of scientific publications

Scientific journals can capture a scholar’s research career. A researcher’s publication data often reflects his/her research interests and their social relations. It is demonstrated that scientist collaboration networks can be constructed based on co-authorship data from journal papers. The problem with such a network is that researchers are limited within their professional social network. This work proposes the idea of constructing a researcher’s social network based on data harvested from metadata of scientific publications and personal online profiles. We hypothesize that data, such as, publication keywords, personal interests, the themes of the conferences where papers are published, and co-authors of the papers, either directly or indirectly represent the authors’ research interests, and by measuring the similarity between these data we are able to construct a researcher social network. Based on the four types of data mentioned above, social network graphs were plotted, studied and analyzed. These graphs were then evaluated by the researchers themselves by giving ratings. Based on this evaluation, we estimated the weight for each type of data, in order to blend all data together to construct one ideal researcher’s social network. Interestingly, our results showed that a graph based on publication’s keywords were more representative than the one based on publication’s co-authorship. The findings from the evaluation were used to propose a dynamic social network data model

Multilayer weighted social network model

Recent empirical studies using large-scale data sets have validated the Granovetter hypothesis on the structure of the society in that there are strongly wired communities connected by weak ties. However, as interaction between individuals takes place in diverse contexts, these communities turn out to be overlapping. This implies that the society has a multilayered structure, where the layers represent the different contexts. To model this structure we begin with a single-layer weighted social network (WSN) model showing the Granovetterian structure. We find that when merging such WSN models, a sufficient amount of interlayer correlation is needed to maintain the relationship between topology and link weights, while these correlations destroy the enhancement in the community overlap due to multiple layers. To resolve this, we devise a geographic multilayer WSN model, where the indirect interlayer correlations due to the geographic constraints of individuals enhance the overlaps between the communities and, at the same time, the Granovetterian structure is preserved.Comment: 9 pages, 9 figure

Strategies in social network formation

We run a computerised experiment of network formation where all connections are beneficial and only direct links are costly. Players simultaneously submit link proposals; a connection is made only when both players involved agree. We use both simulated and experimentally generated data to test the determinants of individual behaviour in network formation. We find that approximately 40% of the network formation strategies adopted by the experimental subjects can be accounted for as best responses. We test whether subjects follow alternative patterns of behaviour and in particular if they: propose links to those from whom they have received link proposals in the previous round; propose links to those who have the largest number of direct connections. We find that together with best response behaviour, these strategies explain approximately 75% of the observed choices. We estimate individual propensities to adopt each of these strategies, controlling for group effects. Finally we estimate a mixture model to highlight the proportion of each type of decision maker in the population