From Relational Data to Graphs: Inferring Significant Links using Generalized Hypergeometric Ensembles

The inference of network topologies from relational data is an important problem in data analysis. Exemplary applications include the reconstruction of social ties from data on human interactions, the inference of gene co-expression networks from DNA microarray data, or the learning of semantic relationships based on co-occurrences of words in documents. Solving these problems requires techniques to infer significant links in noisy relational data. In this short paper, we propose a new statistical modeling framework to address this challenge. It builds on generalized hypergeometric ensembles, a class of generative stochastic models that give rise to analytically tractable probability spaces of directed, multi-edge graphs. We show how this framework can be used to assess the significance of links in noisy relational data. We illustrate our method in two data sets capturing spatio-temporal proximity relations between actors in a social system. The results show that our analytical framework provides a new approach to infer significant links from relational data, with interesting perspectives for the mining of data on social systems.Comment: 10 pages, 8 figures, accepted at SocInfo201

The Emergence of Computing Disciplines in Communist Czechoslovakia: What’s in a (Sovietized) Name?

Part 1: Eastern EuropeInternational audienceDrawing upon archival evidence from the Czechoslovak government and its ministries from the 1970s, this paper presents a preliminary snapshot of the institutional processes that drove the emergence of computing disciplines separate from the rubric of Soviet cybernetics in Communist Czechoslovakia (nowadays, the Czech Republic and the Slovak Republic). We show that the new disciplines were created by a top-down order of the Czechoslovak government, which, in turn, was motivated by a larger scale initiative in the East Bloc. The disciplines created in the 1970s were as follows: Numerical Mathematics for an area of education akin to computer science, Electronic Computers for an area of education akin to computer engineering, and Automated Management/Control Systems for applied computing education. The evidence suggests that the cybernetics metaphor lost its organizing power in 1973 over the broad field of information processing in Czechoslovakia. This disciplinary shift, albeit not immediate, redistributed power between cybernetics and informatics. Indeed, it appears that even nowadays the distribution of power between the two disciplines in the Czech Republic is still in negotiation; what we term a “residual drift” has continued for almost 50 years as an impressive afterglow of the past fame of cybernetics in the east. In sum, the paper raises awareness of the fact that the emergence of computing disciplines behind the Iron Curtain was very different from the West. It also suggests that while academic research analogous to computer science thrived, other computing disciplines in Czechoslovakia were in more complicated positions. Although this paper focuses on Czechoslovakia, the method is generalizable and the data on enrollments may be compared to other countries. Thus, we provide a framework for the further study of similar disciplinary efforts in the remaining East Bloc countries