Different approaches to community detection

A precise definition of what constitutes a community in networks has remained elusive. Consequently, network scientists have compared community detection algorithms on benchmark networks with a particular form of community structure and classified them based on the mathematical techniques they employ. However, this comparison can be misleading because apparent similarities in their mathematical machinery can disguise different reasons for why we would want to employ community detection in the first place. Here we provide a focused review of these different motivations that underpin community detection. This problem-driven classification is useful in applied network science, where it is important to select an appropriate algorithm for the given purpose. Moreover, highlighting the different approaches to community detection also delineates the many lines of research and points out open directions and avenues for future research.Comment: 14 pages, 2 figures. Written as a chapter for forthcoming Advances in network clustering and blockmodeling, and based on an extended version of The many facets of community detection in complex networks, Appl. Netw. Sci. 2: 4 (2017) by the same author

The Web as an Adaptive Network: Coevolution of Web Behavior and Web Structure

Much is known about the complex network structure of the Web, and about behavioral dynamics on the Web. A number of studies address how behaviors on the Web are affected by different network topologies, whilst others address how the behavior of users on the Web alters network topology. These represent complementary directions of influence, but they are generally not combined within any one study. In network science, the study of the coupled interaction between topology and behavior, or state-topology coevolution, is known as 'adaptive networks', and is a rapidly developing area of research. In this paper, we review the case for considering the Web as an adaptive network and several examples of state-topology coevolution on the Web. We also review some abstract results from recent literature in adaptive networks and discuss their implications for Web Science. We conclude that adaptive networks provide a formal framework for characterizing processes acting 'on' and 'of' the Web, and offers potential for identifying general organizing principles that seem otherwise illusive in Web Scienc

The Matter of Entrepreneurial Learning: A Literature Review

This paper is a comprehensive review of the entrepreneurial learning literature and its engagement with the material aspects of entrepreneurship, as part of the “material turn” in the social sciences. Drawing on actor-network theory, we construct a classificatory scheme and an evaluative matrix to find that this field is dominated by an anthropocentric bias and cognitivist approaches which largely ignore issues of materiality in entrepreneurship. However we also identify some heterogeneous network-based conceptualisations of entrepreneurial learning which could provide the foundations for more materially aware approaches. We conclude by calling for a material turn in entrepreneurial learning and outline some possible avenues for it

Untangling the Web of E-Research: Towards a Sociology of Online Knowledge

e-Research is a rapidly growing research area, both in terms of publications and in terms of funding. In this article we argue that it is necessary to reconceptualize the ways in which we seek to measure and understand e-Research by developing a sociology of knowledge based on our understanding of how science has been transformed historically and shifted into online forms. Next, we report data which allows the examination of e-Research through a variety of traces in order to begin to understand how the knowledge in the realm of e-Research has been and is being constructed. These data indicate that e-Research has had a variable impact in different fields of research. We argue that only an overall account of the scale and scope of e-Research within and between different fields makes it possible to identify the organizational coherence and diffuseness of e-Research in terms of its socio-technical networks, and thus to identify the contributions of e-Research to various research fronts in the online production of knowledge