Community structure and the evolution of interdisciplinarity in Slovenia's scientific collaboration network

Interaction among the scientific disciplines is of vital importance in modern science. Focusing on the case of Slovenia, we study the dynamics of interdisciplinary sciences from 1960 to 2010. Our approach relies on quantifying the interdisciplinarity of research communities detected in the coauthorship network of Slovenian scientists over time. Examining the evolution of the community structure, we find that the frequency of interdisciplinary research is only proportional with the overall growth of the network. Although marginal improvements in favor of interdisciplinarity are inferable during the 70s and 80s, the overall trends during the past 20 years are constant and indicative of stalemate. We conclude that the flow of knowledge between different fields of research in Slovenia is in need of further stimulation.Comment: 11 pages, 4 figures; accepted for publication in PLoS ONE [related work available at http://arxiv.org/abs/1004.4824 and http://www.matjazperc.com/sicris/stats.html

Modeling crowdsourcing as collective problem solving

Crowdsourcing is a process of accumulating the ideas, thoughts or information from many independent participants, with aim to find the best solution for a given challenge. Modern information technologies allow for massive number of subjects to be involved in a more or less spontaneous way. Still, the full potentials of crowdsourcing are yet to be reached. We introduce a modeling framework through which we study the effectiveness of crowdsourcing in relation to the level of collectivism in facing the problem. Our findings reveal an intricate relationship between the number of participants and the difficulty of the problem, indicating the optimal size of the crowdsourced group. We discuss our results in the context of modern utilization of crowdsourcing.Comment: 19 pages, 3 figure

Synchronization in time-varying random networks with vanishing connectivity

We wish to thank D. Fanelli and M. Lucas for fruitful discussions. This work has been supported by H2020-MSCAITN-2015 Project COSMOS No. 642563. ZL also acknowledges support from ”Slovenian research agency” via P1-0383 and J5-8236”. FR acknowledges support from H2020 MSCA grant agreement No. 702981.Peer reviewedPublisher PD

What Motivates Us for Work? Intricate Web of Factors beyond Money and Prestige

Efficiency at doing a certain task, at the workplace or otherwise, is strongly influenced by how motivated individuals are. Exploring new ways to motivate employees is often at the top of a company’s agenda. Traditionally identified motivators in Western economies primarily include salary and prestige, often complemented by meaning, creation, challenge, ownership, identity, etc. We report the results of a survey conducted in Slovenia, involving an ensemble of highly educated employees from various public and private organizations. Employing new methodologies such as network analysis, we find that Slovenians are stimulated by an intricate web of interdependent factors, largely in contrast to the traditional understanding that mainly emphasizes money and prestige. In fact, these key motivators only weakly correlate with the demographic parameters. Unexpectedly, we found the evidence of a general optimism in Slovenian professional life - a tendency of the employees to look at the “bright side of things”, thus seeing more clearly the benefits of having something than the drawbacks of not having it. We attribute these particularities to Slovenian recent history, which revolves around gradually embracing the Western (economic) values

Wikipedia data

Time stamps of 14962 Wikipedia articles across 26 different languages over a span of 15 years

Revealing the Hidden Language of Complex Networks

Sophisticated methods for analysing complex networks promise to be of great benefit to almost all scientific disciplines, yet they elude us. In this work, we make fundamental methodological advances to rectify this. We discover that the interaction between a small number of roles, played by nodes in a network, can characterize a network's structure and also provide a clear real-world interpretation. Given this insight, we develop a framework for analysing and comparing networks, which outperforms all existing ones. We demonstrate its strength by uncovering novel relationships between seemingly unrelated networks, such as Facebook, metabolic, and protein structure networks. We also use it to track the dynamics of the world trade network, showing that a country's role of a broker between non-trading countries indicates economic prosperity, whereas peripheral roles are associated with poverty. This result, though intuitive, has escaped all existing frameworks. Finally, our approach translates network topology into everyday language, bringing network analysis closer to domain scientists