Communities of people are better mappers if they are spatially clustered, as revealed in an interesting new paper by Hristova, Mashhadi, Quattrone and Capra from UCL. "This preliminary analysis inspires further inquiry because it shows a clear correlation between spatial affiliation, the internal community structure and the community’s engagement in terms of coverage", according to the authors. They have studied the similarity patterns among eight hundred contributors to OpenStreetMap, the well-known crowdmapping project and detected the hidden community structure. It is a very promising field of research, coupling a social network analysis of crowdsourced data. Participants to such projects are rarely independent individuals: in most cases, they involve communities more than single participants and it would be crucial to uncover how the underlying social structure reflects on the quantity and the quality of the collected data. It has the greatest relevance for citizen science projects, as data quality is often the key issue determining the success or the failure of the collective effort.

In contrast to many other scientific disciplines, computer science considers conference publications. Conferences have the advantage of providing fast publication of papers and of bringing researchers together to present and discuss the paper with peers. Previous work on knowledge mapping focused on the map of all sciences or a particular domain based on ISI published Journal Citation Report (JCR). Although this data cover most of the important journals, it lacks computer science conference and workshop proceedings, which results in an imprecise and incomplete analysis of the computer science knowledge. This paper presents an analysis on the computer science knowledge network constructed from all types of publications, aiming at providing a complete view of computer science research. Based on the combination of two important digital libraries (DBLP and CiteSeerX), we study the knowledge network created at journal/conference level using citation linkage, to identify the development of sub-disciplines. We investigate the collaborative and citation behavior of journals/conferences by analyzing the properties of their co-authorship and citation subgraphs. The paper draws several important conclusions. First, conferences constitute social structures that shape the computer science knowledge. Second, computer science is becoming more interdisciplinary. Third, experts are the key success factor for sustainability of journals/conferences.