33,250 research outputs found
The Community Structure of R&D Cooperation in Europe. Evidence from a social network perspective
The focus of this paper is on pre-competitive R&D cooperation across Europe, as captured by R&D joint ventures funded by the European Commission in the time period 1998-2002, within the 5th Framework Program. The cooperations in this Framework Program give rise to a bipartite network with 72,745 network edges between 25,839 actors (representing organizations that include firms, universities, research organizations and public agencies) and 9,490 R&D projects. With this construction, participating actors are linked only through joint projects.
In this paper we describe the community identification problem based on the concept of modularity, and use the recently introduced label-propagation algorithm to identify communities in the network, and differentiate the identified communities by developing community-specific profiles using social network analysis and geographic visualization techniques. We expect the results to enrich our picture of the European Research Area by providing new insights into the global and local structures of R&D cooperation across Europe
Forecasting the Spreading of Technologies in Research Communities
Technologies such as algorithms, applications and formats are an important part of the knowledge produced and reused in the research process. Typically, a technology is expected to originate in the context of a research area and then spread and contribute to several other fields. For example, Semantic Web technologies have been successfully adopted by a variety of fields, e.g., Information Retrieval, Human Computer Interaction, Biology, and many others. Unfortunately, the spreading of technologies across research areas may be a slow and inefficient process, since it is easy for researchers to be unaware of potentially relevant solutions produced by other research communities. In this paper, we hypothesise that it is possible to learn typical technology propagation patterns from historical data and to exploit this knowledge i) to anticipate where a technology may be adopted next and ii) to alert relevant stakeholders about emerging and relevant technologies in other fields. To do so, we propose the Technology-Topic Framework, a novel approach which uses a semantically enhanced technology-topic model to forecast the propagation of technologies to research areas. A formal evaluation of the approach on a set of technologies in the Semantic Web and Artificial Intelligence areas has produced excellent results, confirming the validity of our solution
DHLP 1&2: Giraph based distributed label propagation algorithms on heterogeneous drug-related networks
Background and Objective: Heterogeneous complex networks are large graphs
consisting of different types of nodes and edges. The knowledge extraction from
these networks is complicated. Moreover, the scale of these networks is
steadily increasing. Thus, scalable methods are required. Methods: In this
paper, two distributed label propagation algorithms for heterogeneous networks,
namely DHLP-1 and DHLP-2 have been introduced. Biological networks are one type
of the heterogeneous complex networks. As a case study, we have measured the
efficiency of our proposed DHLP-1 and DHLP-2 algorithms on a biological network
consisting of drugs, diseases, and targets. The subject we have studied in this
network is drug repositioning but our algorithms can be used as general methods
for heterogeneous networks other than the biological network. Results: We
compared the proposed algorithms with similar non-distributed versions of them
namely MINProp and Heter-LP. The experiments revealed the good performance of
the algorithms in terms of running time and accuracy.Comment: Source code available for Apache Giraph on Hadoo
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