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A0625
Title: Topological clustering of multilayer networks Authors:  Asim Kumer Dey - Princeton University and UT Dallas (United States) [presenting]
Monisha Yuvaraj - UT Dallas (United States)
Vyacheslav Lyubchich - UMCES (Canada)
Yulia Gel - University of Texas at Dallas (United States)
Vincent Poor - Princeton University (United States)
Abstract: Multilayer networks continue to gain significant attention in many areas of study, particularly due to their high utility in modeling interdependent systems such as critical infrastructures, human brain connectome, and socio-environmental ecosystems. However, the clustering of multilayer networks, especially using the information on higher-order interactions of the system entities, remains in its infancy. In turn, higher-order connectivity is often the key in such multilayer network applications as developing optimal partitioning of critical infrastructures in order to isolate unhealthy system components under cyber-physical threats and simultaneous identification of multiple brain regions affected by trauma or mental illness. We introduce the concepts of Topological Data Analysis (TDA) to studies of complex multilayer networks and propose a new topological approach for network clustering. This new topological clustering approach allows for systematic accounting for the important heterogeneous higher-order properties of node interactions within and in-between network layers and integrating information from the node neighbors and their interactions. We illustrate the utility of the proposed clustering algorithm by applying it to an emerging problem of societal importance - vulnerability zoning of residential properties to weather- and climate-induced risks in the context of house insurance claim dynamics.