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\nData structured in the form of overlapping or n on-overlapping sets is found in a variety of domains, sometimes explicitly but often subtly. For example, teams, which are of prime importance in indu stry and social science studies are “sets of individuals”; &ldq uo;item sets” in pattern mining of customer transactions are set s and for various types of analysis in language studies a sentence can be c onsidered as a “set or bag of words”. Although building models and inference algorithms for structured data has been an important task in the fields of machine learning and statistics, research on “set-like& rdquo; data still remains less explored. Relationships between pairs of ele ments can be modeled as edges in a graph. However, for modeling relationshi ps that involve all members of a set, hyperedges in a Hypergraph are more n atural representations. This talk describes analyzing complex group structu red data from domains like social networks, customer transaction data and k nowledge graphs, via the lens of Hypergraphs. Specifically:

\n\n Graph v/s Hypergraphs: Motivation for modeling higher-order structures\n Static hypergraphs for modeling higher-order probabilities\n Temporal hypergraphs to capture evolving group structured data with focus o n groups in social networks\n Directed hypergraphs for handling directional relationships with applicatio ns to Knowledge graphs and Chemistry\n Hyperedge2vec: A representation learning framework for hypergraphs which ca n be used for applying deep learning to hypergraphs\n Tool for large-scale distributed computation and predictive modeling for hy pergraph data\n\n DTSTART:20191017T094000 SUMMARY:Hypergraph Analytics: Concepts, Algorithms, and Applications DTEND:20191017T103959 LOCATION: See Description END:VEVENT END:VCALENDAR