Academic team formation as evolving hypergraphs

This paper quantitatively explores the social and socio-semantic patterns of constitution of academic collaboration teams. To this end, we broadly underline two critical features of social networks of knowledge-based collaboration: first, they essentially consist of group-level interactions which call for team-centered approaches. Formally, this induces the use of hypergraphs and n-adic interactions, rather than traditional dyadic frameworks of interaction such as graphs, binding only pairs of agents. Second, we advocate the joint consideration of structural and semantic features, as collaborations are allegedly constrained by both of them. Considering these provisions, we propose a framework which principally enables us to empirically test a series of hypotheses related to academic team formation patterns. In particular, we exhibit and characterize the influence of an implicit group structure driving recurrent team formation processes. On the whole, innovative production does not appear to be correlated with more original teams, while a polarization appears between groups composed of experts only or non-experts only, altogether corresponding to collectives with a high rate of repeated interactions

Finding and evaluating community structure in networks

We propose and study a set of algorithms for discovering community structure in networks -- natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the community structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex structure of networked systems.Comment: 16 pages, 13 figure

Socio-semantic and other dualities

The social and the cultural orders are dual – that is, they constitute each other. To understand either we need to account for both. Socio-semantic network analysis brings together the study of relations among actors (social networks), relations among elements of actors’ cultural structures (their semantic networks), and relations among these two orders of networks. In this introductory essay, we describe how the duality of the social and semantic networks that constitute each other, as well as other related dualities (including material / symbolic, micro / macro, computational / qualitative, in-presence contexts / online contexts, ‘Big’ data / ‘thick’ data), have evolved in recent decades to mold socio-semantic network analysis into its present form. In doing so, we delineate the current state of the art and the main features of socio-semantic network analysis as highlighted by the papers included in this Special Issue. These articles range from in-depth analysis of ‘thick’ data on small group interactions to automated analysis of ‘Big’ online data in contexts extending from Renaissance parliamentary discussions to cutting-edge global scientific fields of the 21st century. We conclude by delineating current problems of and future prospects for socio-semantic network analysis