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AMSI-ANZIAM Lecture Tour, Canberra

AMSI-ANZIAM Lecture Tour, Canberra

Image Credit: Australian Mathematical Sciences Institute (AMSI)

2023 ANZIAM Lecturer

The AMSI-ANZIAM Lecture Tour invites a distinguished international academic in an Applied Mathematical field to speak at universities across Australia after the conclusion of the ANZIAM Conference. It includes a series of talks including Specialist and Public lectures. The tour is organised biennially by AMSI and is supported by ANZIAM.

Date: 17 February 2023
Time: 11am AEDT
Venue: Knibbs Theatre, Ground floor, ABS House, 45 Benjamin Way, Belconnen Canberra, ACT
Title: Random Graph Models, Network Centralities and Graph Clustering

Professor Konstantin Avrachenkov
National Institute for Research in Digital Science and Technology (INRIA)

Konstantin Avrachenkov received his Master degree in Control Theory from St. Petersburg State Polytechnic University (1996), Ph.D. degree in Mathematics from University of South Australia (2000) and Habilitation from University of Nice Sophia Antipolis (2010). Currently, he is a Director of Research at Inria Sophia Antipolis, France. He is an associate editor of the International Journal of Performance Evaluation, Probability in the Engineering and Informational Sciences, ACM TOMPECS, Stochastic Models and IEEE Network Magazine. Konstantin has co-authored two books “Analytic Perturbation Theory and its Applications”, SIAM, 2013 and “Statistical Analysis of Networks”, Now Publishers, 2022. He has won 5 best paper awards. His main theoretical research interests are Markov chains, Markov decision processes, random graphs and singular perturbations. He applies these methodological tools to the modeling and control of networks, and to design data mining and machine learning algorithms.

Talk Abstract: Many real-world complex networks share a number of common properties such as sparsity, heavy-tailed degree distribution, the existence of a giant connected component, small world property and edge transitivity. Firstly, I review several basic random graph models such as Erdos–Renyi random graph, exponential family of random graph models (ERGMs), stochastic block models (SBMs), random geometric graphs, and indicate which model can represent well a given property. Secondly, I describe the main network centrality indices which can be applied to study network structure or to assess network robustness. I conclude with an overview of main methods in graph clustering with a particular emphasis on the methods designed with the help of random graph models and on the methods using centrality indices.