Skip to main content


Springer Nature is making Coronavirus research free. View research | View latest news | Sign up for updates

Fig. 5 | Applied Network Science

Fig. 5

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

Fig. 5

Ranking of hypotheses for synthetic attributed multigraph. In a, we show the adjacency matrix of a 100-node 2-color random multigraph with a node correlation of 80% for nodes of the same color and 20% otherwise. One can see the presence of homophily based on more connections between nodes of the same color; the diagonal is zero as there are no self-connections. In b, c we show the ranking of hypotheses based on Bayes factors when compared to the uniform hypothesis for the local and global models respectively. As expected, in general the homophily hypothesis explains the edge formation best (positive Bayes factor and close to the data curve), while the heterophily and selfloop hypotheses provide no good explanations for edge formation in both local and global cases—they show negative Bayes factors

Back to article page