In this study, we propose to derive information about the alcohol outlet visits of the residents of different neighborhoods from anonymized mobile phone location data, and investigate whether the derived visits can help better predict DV at the neighborhood level.
Here, we analyze the effect on $\tau_\epsilon$ of network community structure, which can arise when compute nodes/sensors are spatially clustered, for example.
The ubiquity of deep neural networks (DNNs), cloud-based training, and transfer learning is giving rise to a new cybersecurity frontier in which unsecure DNNs have `structural malware' (i. e., compromised weights and activation pathways).
Characterizing the importances of nodes in social, biological, information and technological networks is a core topic for the network-science and data-science communities.
Social and Information Networks Physics and Society 05C82
We introduce the Convex Hull of Admissible Modularity Partitions (CHAMP) algorithm to prune and prioritize different network community structures identified across multiple runs of possibly various computational heuristics.
Social and Information Networks Physics and Society
While each layer provides its own set of information, community structure across layers can be collectively utilized to discover and quantify underlying relational patterns between nodes.