An Analysis of the Structure and Evolution of the Scientific Collaboration Network of Computer Intelligence in Games

Scientific collaboration networks are a prime example of complex system. If we create a graph in which nodes represent researchers and edges indicate a collaboration among them, we obtain a complex network exhibitting very specific properties emerging from the particular interaction dynamics in the field under scrutiny.

We have conducted such a study on the community of researchers in computational intelligence and games. Using data grabbed from the DBLP, we have constructed and analyzed the resulting network from a dynamic perspective, studying how it evolves in time when considering either its cumulative state or a moving time-frame. This analysis has been conducted at different scales, from the macroscopic to the microscopic, and has paid special attention to issues such as the growth dynamics of the network (driven by preferential attachment), the collaboration patterns and the community structure. The network is shown to be at an early stage of development, with percolation starting to give rise to a large giant component. Comparisons have been also drawn to other related collaboration networks. Our findings have been published in Physica A. The abstract of the paper follows:

Games constitute a research domain that is attracting the interest of scientists from numerous disciplines. This is particularly true from the perspective of computational intelligence. In order to examine the growing importance of this area in the gaming domain, we present an analysis of the scientific collaboration network of researchers working on computational intelligence in games (CIG). This network has been constructed from bibliographical data obtained from the Digital Bibliography & Library Project (DBLP). We have analyzed from a temporal perspective several properties of the CIG network at the macroscopic, mesoscopic and microscopic levels, studying the large-scale structure, the growth mechanics, and collaboration patterns among other features. Overall, computational intelligence in games exhibits similarities with other collaboration networks such as for example a log-normal degree distribution and sub-linear preferential attachment for new authors. It also has distinctive features, e.g. the number of papers co-authored is exponentially distributed, the internal preferential attachment (new collaborations among existing authors) is linear, and fidelity rates (measured as the relative preference for publishing with previous collaborators) grow super-linearly. The macroscopic and mesoscopic evolution of the network indicates the field is very active and vibrant, but it is still at an early developmental stage. We have also analyzed communities and central nodes and how these are reflected in research topics, thus identifying active research subareas.

The paper can be found following the link below:

R. Lara-Cabrera, C. Cotta, A.J. Fernández Leiva, An Analysis of the Structure and Evolution of the Scientific Collaboration Network of Computer Intelligence in Games, Physica A 395:523–536, 2014 DOI:10.1016/j.physa.2013.10.036, 2013