106,389 research outputs found
AUGUR: Forecasting the Emergence of New Research Topics
Being able to rapidly recognise new research trends is strategic for many stakeholders, including universities, institutional funding bodies, academic publishers and companies. The literature presents several approaches to identifying the emergence of new research topics, which rely on the assumption that the topic is already exhibiting a certain degree of popularity and consistently referred to by a community of researchers. However, detecting the emergence of a new research area at an embryonic stage, i.e., before the topic has been consistently labelled by a community of researchers and associated with a number of publications, is still an open challenge. We address this issue by introducing Augur, a novel approach to the early detection of research topics. Augur analyses the diachronic relationships between research areas and is able to detect clusters of topics that exhibit dynamics correlated with the emergence of new research topics. Here we also present the Advanced Clique Percolation Method (ACPM), a new community detection algorithm developed specifically for supporting this task. Augur was evaluated on a gold standard of 1,408 debutant topics in the 2000-2011 interval and outperformed four alternative approaches in terms of both precision and recall
Betweenness Centrality as a Driver of Preferential Attachment in the Evolution of Research Collaboration Networks
We analyze whether preferential attachment in scientific coauthorship
networks is different for authors with different forms of centrality. Using a
complete database for the scientific specialty of research about "steel
structures," we show that betweenness centrality of an existing node is a
significantly better predictor of preferential attachment by new entrants than
degree or closeness centrality. During the growth of a network, preferential
attachment shifts from (local) degree centrality to betweenness centrality as a
global measure. An interpretation is that supervisors of PhD projects and
postdocs broker between new entrants and the already existing network, and thus
become focal to preferential attachment. Because of this mediation, scholarly
networks can be expected to develop differently from networks which are
predicated on preferential attachment to nodes with high degree centrality.Comment: Journal of Informetrics (in press
Quantifying knowledge exchange in R&D networks: A data-driven model
We propose a model that reflects two important processes in R&D activities of
firms, the formation of R&D alliances and the exchange of knowledge as a result
of these collaborations. In a data-driven approach, we analyze two large-scale
data sets extracting unique information about 7500 R&D alliances and 5200
patent portfolios of firms. This data is used to calibrate the model parameters
for network formation and knowledge exchange. We obtain probabilities for
incumbent and newcomer firms to link to other incumbents or newcomers which are
able to reproduce the topology of the empirical R&D network. The position of
firms in a knowledge space is obtained from their patents using two different
classification schemes, IPC in 8 dimensions and ISI-OST-INPI in 35 dimensions.
Our dynamics of knowledge exchange assumes that collaborating firms approach
each other in knowledge space at a rate for an alliance duration .
Both parameters are obtained in two different ways, by comparing knowledge
distances from simulations and empirics and by analyzing the collaboration
efficiency . This is a new measure, that takes also in
account the effort of firms to maintain concurrent alliances, and is evaluated
via extensive computer simulations. We find that R&D alliances have a duration
of around two years and that the subsequent knowledge exchange occurs at a very
low rate. Hence, a firm's position in the knowledge space is rather a
determinant than a consequence of its R&D alliances. From our data-driven
approach we also find model configurations that can be both realistic and
optimized with respect to the collaboration efficiency .
Effective policies, as suggested by our model, would incentivize shorter R&D
alliances and higher knowledge exchange rates.Comment: 35 pages, 10 figure
Evolutionary Dilemmas in a Social Network
We simulate the prisoner's dilemma and hawk-dove games on a real social
acquaintance network. Using a discrete analogue of replicator dynamics, we show
that surprisingly high levels of cooperation can be achieved, contrary to what
happens in unstructured mixing populations. Moreover, we empirically show that
cooperation in this network is stable with respect to invasion by defectors.Comment: 13 pages, 9 figures; to be published in Lecture Notes in Computer
Science 200
The Local Emergence and Global Diffusion of Research Technologies: An Exploration of Patterns of Network Formation
Grasping the fruits of "emerging technologies" is an objective of many
government priority programs in a knowledge-based and globalizing economy. We
use the publication records (in the Science Citation Index) of two emerging
technologies to study the mechanisms of diffusion in the case of two innovation
trajectories: small interference RNA (siRNA) and nano-crystalline solar cells
(NCSC). Methods for analyzing and visualizing geographical and cognitive
diffusion are specified as indicators of different dynamics. Geographical
diffusion is illustrated with overlays to Google Maps; cognitive diffusion is
mapped using an overlay to a map based on the ISI Subject Categories. The
evolving geographical networks show both preferential attachment and
small-world characteristics. The strength of preferential attachment decreases
over time, while the network evolves into an oligopolistic control structure
with small-world characteristics. The transition from disciplinary-oriented
("mode-1") to transfer-oriented ("mode-2") research is suggested as the crucial
difference in explaining the different rates of diffusion between siRNA and
NCSC
- …