Selection in scientific networks

One of the most pressing and interesting actual scientific challenges deals with the analysis and the understanding of complex network dynamics. In particular, a major trend is the definition of new frameworks for the analysis, the exploration and the detection of the dynamics at play in real dynamic networks. In this paper, we focus in particular on scientific communities by targeting the social part of science through a descriptive approach that aims at identifying the social determinants behind the emergence and the resilience of scientific communities. We consider that scientific communities are at the same time through co-authorship communities of practice and that they exist also as representations in the scientists mind, since references to other scientists’ works are not merely an objective link to a relevant work, but they reveal also social objects that one manipulates and refers to. In fact, our analysis focuses on the coexistence of co-authorships and citation dynamics and how their interplay affects the shape, the strength and the stability of the scientific systems. Such an analysis—performed through the time-varying graphs (TVG) formalism and derived metrics—concerns the evolution of a scientific network extracted from a portion of the arXiv repository covering a period of 10 years of publications in physics. We detect an example of how the selection process of citations may affect the shape of the co-authorships network from a sparser and disconnected structure to a dense and homogeneous one

Strategic management and the smaller enterprise: cases and readings.

We describe a data structure that maintains the number of triangles in a dynamic undirected graph, subject to insertions and deletions of edges and of degree-zero vertices. More generally it can be used to maintain the number of copies of each possible three-vertex subgraph in time O(h) per update, where h is the h-index of the graph, the maximum number such that the graph contains h vertices of degree at least h. We also show how to maintain the h-index itself, and a collection of h high-degree vertices in the graph, in constant time per update. Our data structure has applications in social network analysis using the exponential random graph model (ERGM); its bound of O(h) time per edge is never worse than the Θ ( √ m) time per edge necessary to list all triangles in a static graph, and is strictly better for graphs obeying a power law degree distribution. In order to better understand the behavior of the h-index statistic and its implications for the performance of our algorithms, we also study the behavior of the h-index on a set of 136 real-world networks

The emergence of new knowledge, market evolution and the dynamics of micro-innovation systems

In this paper, we develop a problem-driven approach to innovation systems to account for the emergence of new knowledge and the long-term evolution of markets. We show that (1) science, technology and markets co-evolve along coherent trajectories of long-term change, which can be efficiently mapped by longitudinal network analysis of bibliometric data that are consistent with market-level data, (2) the inception of these trajectories corresponds to phases of close interaction within small entrepreneurial networks of multi-skilled practitioners that grow through the division of innovative labour and associated incremental change in specific and problem-centred micro-innovation systems, (3) as trajectories grow and develop, changes in knowledge alter structure and composition of final product markets and the likelihood of opportunities for new entrants increases with the distance between the knowledge bases of firms. As the knowledge that is necessary to solve new technical problems grows and as these are transformed by their very solutions, new ties are created and old ties decay in an open-ended co-evolutionary process of change in knowledge and industry organisation.technology emergence, innovation systems, entrepreneurial networks, health technologies,