Mining and Modeling Network Data
Mining and Modeling Network Data mini-symposium
at SIAM DM '18.
June 7, 2018; Denver, Colorado.
June 7, 2018; Denver, Colorado.
Overview
Discrete mathematics is at the heart of challenging data-driven
problems. Many discrete datasets look like networks, a mathematical
formalism for modeling complex systems by interactions between
entities. This minisymposium highlights several recent advances in
modeling and mining network data with an underlying theme of
connecting theory and applications with real data. The theoretical
tools are quite diverse and include extremal graph theory, numerical
linear algebra, and algorithm design. The applications are equally
diverse, drawing from marketing, machine learning, and social network
analysis, using data from protein interactions, neural systems, email
communications, transportation systems, and more. This minisymposium
will showcase the exciting opportunities at the intersection of
discrete mathematics and data science.
Session I (North Classroom 1003, 9:30AM–11:30AM)
- Austin Benson (Cornell University)
New perspectives on measuring network clustering - Nicole Eikmeier (Purdue University)
Hypergraph Kronecker models for networks - Rediet Abebe (Cornell University)
Mitigating Overexposure in Viral Marketing - Anthony Bonato (Ryerson University)
Modeling and mining dynamic competition networks
Session II (North Classroom 1604, 2:45PM–4:45PM)
- Huda Nassar (Purdue University)
Graph matching via low rank factors - Kathleen Finlinson (University of Colorado Boulder)
Tuning the activity of neural networks at criticality - Dane Taylor (SUNY Buffalo)
Detectability of hierarchical community structure in preprocessed multilayer networks - Amir Ghasemian (University of Colorado Boulder)
Evaluating Overfit and Underfit in Models of Network Community Structure