## Mining and Modeling Network Data

Mining and Modeling Network Data is a mini-symposium
at the 2018 SIAM Conference on Discrete Mathematics in 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

- Austin Benson (Cornell University)

New perspectives on measuring network clustering - David Gleich (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 - Kathleen Finlinson (University of Colorado Boulder)

Tuning the activity of neural networks at criticality

#### Session II

- Huda Nassar (Purdue University)

Graph matching via low rank factors - Charalampos Tsourakakis (Boston University)

Risk-Averse Matchings over Uncertain Graph Databases - 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