Francesco Tudisco

Associate Professor (Reader) in Machine Learning

School of Mathematics, The University of Edinburgh
The Maxwell Institute for Mathematical Sciences
School of Mathematics, Gran Sasso Science Institute JCMB, King’s Buildings, Edinburgh EH93FD UK
email: f dot tudisco at ed.ac.uk

ICIAM19: International Congress on Industrial and Applied Mathematics

I am organizing a 16 speakers (super-)minisymposium and giving a talk at the next ICIAM19 conference in Valencia (Spain), July 15-19.

Minisymposium: Mining and Modeling Evolving and Higher-Order Complex Data and Networks
jointly organized with Austin Benson, Christine Klymko, Eisha Nathan.

Abstract: The analysis of complex networks is a rapidly growing field with applications in many diverse areas. A typical computational paradigm is to reduce the system to a set of pairwise relationships modeled by a graph (matrix) and employ tools within this framework. However, many real-world networks feature temporally evolving structures and higher-order interactions. Such components are often missed when using static and lower-order methods. This minisymposium explores recent advances in models, theory, and algorithms for dynamic and higher-order interactions and data, spanning a broad range of topics including persistent homology, tensor analysis, random walks with memory, and higher-order network analysis.

Group1 – Community detection and clustering

  1. Christine Klymko, LLNL
    Improving seed set expansion with semi-supervised information
  2. Tim La Fond, LLNL
    Representing the Evolution of Communities in Dynamic Networks
  3. Nate Veldt, Purdue
    Algorithmic Advances in Higher-Order Correlation Clustering
  4. Marya Bazzi, ATI
    Community structure in temporal multilayer networks

Group2 – Simplicial complexes

  1. Heather Harrington, Oxford
    Topological data analysis for investigation of dynamics and biological networks
  2. Alice Patania, Indiana
    Null hypothesis for simplicial complexes
  3. Braxton Osting, Utah
    Spectral Sparsification of Simplicial Complexes for Clustering and Label Propagation
  4. Austin Benson, Cornell
    Simplicial closure and higher-order link prediction.

Group3 – Tensor methods and high-performance computing

  1. Francesca Arrigo
    Eigenvector-based Centrality Measures in Multilayer Networks
  2. Orly Alter, Utah
    Multi-Tensor Decompositions for Personalized Cancer Diagnostics, Prognostics, and Therapeutics.
  3. Chunxing Yin, GA Tech
    A New Algorithm Model for Massive-Scale Streaming Graph Analysis
  4. Tahsin Reza, UBC
    Distributed Algorithms for Exact and Fuzzy Graph Pattern Matching

Group4 – Higher-order random walks

  1. Eisha Nathan, LLNL
    Nonbacktracking Walks in Dynamic Graphs
  2. Michael Schaub, MIT
    Random walks on simplicial complexes and the normalized Hodge Laplacian
  3. Keita Iwabuchi, LLNL
  4. Francesco Tudisco, Strathclyde
    Higher-order ergodicity coefficients

This event is part of the research project MAGNET for which I would like to acknowledge support from the Marie Curie individual fellowship scheme.