Skip to main content


Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Fig. 4 | Applied Network Science

Fig. 4

From: JANUS: A hypothesis-driven Bayesian approach for understanding edge formation in attributed multigraphs

Fig. 4

Ranking of hypotheses for the introductory example. a, b Represent results using the local model and c, d results of the global model. Rankings can be visualized using a, c the marginal likelihood or evidence (y-axis), or b, d using Bayes factors (y-axis) by setting the uniform hypothesis as a baseline to compare with; higher values refer to higher plausibility. The x-axis depicts the concentration parameter κ. For this example, from an individual perspective (local model) authors from the multigraph shown in Fig. 1 appear to prefer to collaborate more often with researchers of the same country rather than due to popularity (i.e., number of articles and citations). In this particular case, the same holds for the global model. Note that all hypotheses outperform the uniform, meaning that they all are reasonable explanations of edge formation for the given graph

Back to article page