Data Science

Knowledge on the move: Reconstructing scientists' mobility network

Knowledge in socio-economic systems - Insights from collaboration, mobility, and data-driven models

Reconstructing signed relations from interaction data

This study presents a statistical network method to infer weighted signed relations from interaction data. The method, called the Φ-method, assumes that a statistical over-representation of interactions signals a positive relation and an under-representation signals a negative relation. TheΦ-method was tested on four classical interaction datasets and showed promising results in predicting reported relations and reconstructing the underlying relational networks of the communities.

When standard network measures fail to rank journals: A theoretical and empirical analysis

The mobility network of scientists: Analyzing temporal correlations in scientific careers

The study of scientist mobility is important for knowledge exchange and understanding the career trajectories of scientists. The researchers analyzed 3.5 million career trajectories of scientists using a novel method of higher-order networks. They found strong evidence for temporal correlations at the level of universities, indicating that scientists tend to move between specific institutions. These correlations also exist at the level of countries but not cities. The results have implications for the efficiency of mobility programs and the institutional path dependence of scientific careers.