A0795
Title: Signed diverse multiplex networks: Clustering and inference
Authors: Marianna Pensky - University of Central Florida (United States) [presenting]
Abstract: The purpose is to introduce a signed generalized random dot product graph (SGRDPG) model, which is a variant of the generalized random dot product graph (GRDPG), where, in addition, edges can be positive or negative. The setting is extended to a multiplex version, where all layers have the same collection of nodes and follow the SGRDPG. The only common feature of the layers of the network is that they can be partitioned into groups with common subspace structures, while otherwise, matrices of connection probabilities can be all different. The setting above is extremely flexible and includes a variety of existing multiplex network models as its particular cases. By employing novel methodologies, strongly consistent clustering of layers and high accuracy of subspace estimation is ensured, which were attained previously only in much simpler models. All algorithms and theoretical results remain true for both signed and binary networks. In addition, it shows that keeping signs of the edges in the process of network construction leads to a better precision of estimation and clustering and, hence, is beneficial for tackling real-world problems such as, for example, analysis of brain networks.
