The spatial structure of mobile communication networks

There has been a recent surge of interest in the relationship between the spatial and topological structure of communication networks with the availability of large scale anonymous datasets on the communication and mobility patterns of individuals. These datasets, captured as a by-product of modern communications technology, provide a detailed view of the daily interpersonal interactions of millions of people. Mobile phone call logs in particular offer an unparalleled source of information given their personal portable nature and ubiquity in modern society. The use of mobile phones has become so common that these datasets are no longer merely communication logs but close approximations of the network of interpersonal relationships that forms society. The analysis of these proxy networks has the potential to uncover knowledge about society at a scale never previously possible. Networks, and social networks in particular, have been the subject of investigation for more than a century with a rich corpus of theory and methods now available to researchers. Computational approaches to the study of networks are more recent but there are now a wide variety of structural analysis methods that have been developed and applied across many different disciplines and subject areas. The study of interactions across space has developed in parallel with theory, methods, models and a variety of applications. Recent studies of these proxy networks have tended to use computational approaches for analysing community structure and modelling spatial interacitions without much regard for the theory upon which they were built. The underlying assumption has been that all phenomena that can be represented as networks can be analysed with the same methods. In this thesis we demonstrate that this is not the case and identify a number of problems and misinterpretations that can arise when inappropriate methods or network representations are employed. Through a detailed theoretical and empirical analysis we identify appropriate combinations of network representation, spatial scale, and analysis methods for studying the spatial structure of communication networks. Using these findings we demonstrate the potential of such analysis when the appropriate methodology is employed

A Relational Hyperlink Analysis of an Online Social Movement

In this paper we propose relational hyperlink analysis (RHA) as a distinct approach for empirical social science research into hyperlink networks on the World Wide Web. We demonstrate this approach, which employs the ideas and techniques of social network analysis (in particular, exponential random graph modeling), in a study of the hyperlinking behaviors of Australian asylum advocacy groups. We show that compared with the commonly-used hyperlink counts regression approach, relational hyperlink analysis can lead to fundamentally different conclusions about the social processes underpinning hyperlinking behavior. In particular, in trying to understand why social ties are formed, counts regressions may over-estimate the role of actor attributes in the formation of hyperlinks when endogenous, purely structural network effects are not taken into account. Our analysis involves an innovative joint use of two software programs: VOSON, for the automated retrieval and processing of considerable quantities of hyperlink data, and LPNet, for the statistical modeling of social network data. Together, VOSON and LPNet enable new and unique research into social networks in the online world, and our paper highlights the importance of complementary research tools for social science research into the web

Getting Into Networks and Clusters: Evidence on the GNSS composite knowledge process in (and from) Midi-Pyrénées

This paper aims to contribute to the empirical identification of clusters by proposing methodological issues based on network analysis. We start with the detection of a composite knowledge process rather than a territorial one stricto sensu. Such a consideration allows us to avoid the overestimation of the role played by geographical proximity between agents, and grasp its ambivalence in knowledge relations. Networks and clusters correspond to the complex aggregation process of bi or n-lateral relations in which agents can play heterogeneous structural roles. Their empirical reconstitution requires thus to gather located relational data, whereas their structural properties analysis requires to compute a set of indexes developed in the field of the social network analysis. Our theoretical considerations are tested in the technological field of GNSS (Global Satellite Navigation Systems). We propose a sample of knowledge relations based on collaborative R&D projects and discuss how this sample is shaped and why we can assume its representativeness. The network we obtain allows us to show how the composite knowledge process gives rise to a structure with a peculiar combination of local and distant relations. Descriptive statistics and structural properties show the influence or the centrality of certain agents in the aggregate structure, and permit to discuss the complementarities between their heterogeneous knowledge profiles. Quantitative results are completed and confirmed by an interpretative discussion based on a run of semi-structured interviews. Concluding remarks provide theoretical feedbacks.Knowledge, Networks, Economic Geography, Cluster, GNSS

Temporal Networks

A great variety of systems in nature, society and technology -- from the web of sexual contacts to the Internet, from the nervous system to power grids -- can be modeled as graphs of vertices coupled by edges. The network structure, describing how the graph is wired, helps us understand, predict and optimize the behavior of dynamical systems. In many cases, however, the edges are not continuously active. As an example, in networks of communication via email, text messages, or phone calls, edges represent sequences of instantaneous or practically instantaneous contacts. In some cases, edges are active for non-negligible periods of time: e.g., the proximity patterns of inpatients at hospitals can be represented by a graph where an edge between two individuals is on throughout the time they are at the same ward. Like network topology, the temporal structure of edge activations can affect dynamics of systems interacting through the network, from disease contagion on the network of patients to information diffusion over an e-mail network. In this review, we present the emergent field of temporal networks, and discuss methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems. In the light of traditional network theory, one can see this framework as moving the information of when things happen from the dynamical system on the network, to the network itself. Since fundamental properties, such as the transitivity of edges, do not necessarily hold in temporal networks, many of these methods need to be quite different from those for static networks

The Effect of Influence Tactics and Contingency Factors on the Adoption and Diffusion of IS/IT Innovations in Social Networks

Despite considerable research on the adoption and diffusion of information systems (IS)/ information technology (IT) innovations by individuals in organizations, very little is known about the processes underlying the adoption of innovations, and how those processes contribute to the diffusion and assimilation of innovations within organizations. Viewing processes as sequences of actions, this research conducted two studies to: a) uncover the adoption and influence processes employed by individuals, and b) identify the factors that influence diffusion and assimilation within social networks. The first study, situated at the individual level, involved field interviews with 27 individuals from ten organizations in a large mid-western city in the United States. Three categories of actions were identified from the interview data: contextual actions, influencer actions, and adopter (pre-adoption) actions. The actions from each interview were used to construct two sequences (for adoption and influence), which were then examined using optimal matching and cluster analysis. Taxonomies of three adoption processes (Conscious Quest, Requisite Compliance, and Piloted Trial) and three influence processes (Directed Assistance, Queried Disclosure, and Logical Persuasion) were empirically developed. These processes provide insights into the adoption of innovations by individuals. The second study, situated at the network level, involved an agent-based simulation. Building on the field interviews, the simulation modeled the behaviors of individuals within 5000 networks adopting multi-feature IS/IT innovations over 50 time periods. Cross-sectional time-series analyses of the resulting data supported 13 of the 20 hypotheses, and revealed that: a) diffusion was facilitated by: a centralized organization structure, an individualistic cultural orientation, and all three actions, b) assimilation was facilitated by: a centralized organization structure and an individualistic cultural orientation during the early periods but by a decentralized organization structure and a collectivistic cultural orientation during the later periods, and c) all three actions facilitated assimilation in the early periods but only contextual and adopter actions influenced assimilation during the later periods. Overall, this study yielded insights into the diffusion and assimilation of innovations within networks. Together, the two studies provided insights into the complex processes by which individuals within networks adopt IS/IT innovations with multiple features

Understanding Communication Patterns in MOOCs: Combining Data Mining and qualitative methods

Massive Open Online Courses (MOOCs) offer unprecedented opportunities to learn at scale. Within a few years, the phenomenon of crowd-based learning has gained enormous popularity with millions of learners across the globe participating in courses ranging from Popular Music to Astrophysics. They have captured the imaginations of many, attracting significant media attention - with The New York Times naming 2012 "The Year of the MOOC." For those engaged in learning analytics and educational data mining, MOOCs have provided an exciting opportunity to develop innovative methodologies that harness big data in education.Comment: Preprint of a chapter to appear in "Data Mining and Learning Analytics: Applications in Educational Research

A study on creativity: Detection and network structures

In recent years, the topic of creativity has attracted extensive focus in the form of public discussion as well as research study. This has largely been in two areas: applying technology in order to innovate, as well as studying creativity in society and analyzing its dependence on social parameters and on network characteristics. Computational creativity has been used positively for automated creation of new content like art or recipes, but it is also being applied for pernicious activities like generating vile or misleading content, morphing pornographic or unethical videos/pictures to spread misinformation, or for blackmail. Such instances of fake content generated by artificial intelligence based generative techniques with potentially harmful applications are commonly referred to as deepfakes. This thesis consists of two parts that focus on each of these aspects separately. The first part deals with the detection problem for deepfake content. It outlines a classification problem for identifying an image as legitimate or fake, and obtains bounds on the expected performance while identifying fake content generated by generative adversarial networks. It further uses an approximation from Euclidean information theory for the low error regime and gives simplified bounds for the case where accuracy of the generative process is high. The second part deals with studying the effects of network parameters on creative productivity in social networks. It includes an overview of various theories on the ways by which network structure affects creativity, along with empirical results obtained by analyzing university innovation data alongside the online friendship networks for the same universities

The Use and Effect of Social Capital in New Venture Creation - Solo Entrepreneurs vs. New Venture Teams

This paper examines the use of social capital in the venture creation process. We compare solo entrepreneurs (n=182) and new venture teams (n=274) from a random sample of start-ups in innovative industries and test social capital use and its effects on firm performance. Our results reveal that solo entrepreneurs and new venture teams do not differ in their degree of use of social capital. However, there are differences in the determinants of social capital use in both groups. We find that weak ties assist solo entrepreneurs and have positive significant effects on new venture performance. For team start- ups, we find no direct effect of social capital. However, further tests indicate for teams that human capital variety positively moderates the effect of social capital on performance.Entrepreneurship, Nascent entrepreneurship, Social capital, Start-up teams, Entrepreneurial learning