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Fig. 13 | Applied Network Science

Fig. 13

From: A novel framework for community modeling and characterization in directed temporal networks

Fig. 13

Monte Carlo parameter identification (over 100 random link orientations) for the primary school case study. In (a), we show the distribution of the estimated parameter \(\hat \lambda \), which quantifies the role of communities in the network formation process. In average, half of the links are generated following the community structure. In (b), we plot the estimated community strength matrix \(\hat Q\). From the output, we observe that all the students have a large involvement in their classes, whereas teachers are more active in the all-to-all community than in the teacher community. In (c), we plot the estimated irreducible backbone of the network \(\hat R\). We observe that the network is sparse, and most of the nonzero terms are within the classes. These dyadic relationships may represent, e.g., students who sit next to each others

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