Session

DescriptionA common research problem in diverse domains is the extraction of combinatorial patterns from large datasets. For example, most human diseases arise due to the interactions of multiple genetic factors and lifestyle choices; and weather events, such as tornados, manifest due to the complex interactions of a host of meteorological states. Data in such domains is rapidly being gathered, yet identification of these high-dimensional patterns remains difficult due to the combinatorial explosion of the number of groups to be considered. One practical approach leverages the concept of guilt-by-association and models the data as a network. These networks typically represent factors as nodes and relationships between pairs of factors as edges between the corresponding nodes. A key benefit of network modeling is that the computation of simple pair-wise relationships can yield knowledge about unknown high-dimensional relationships. Network models have been widely employed, yet several challenges impede their full potential. This minisymposium focuses on these challenges, discusses current state-of-the-art approaches, and also presents promising directions for future research, such as the computations of 3-way relationships.