Identifying Positive Community Structures on Networks

PhD Thesis Proposal Defence


Title: "Identifying Positive Community Structures on Networks"

by

Mr. Alexander Tiannan ZHOU


Abstract:

On graphs the problem of community search is the task of identifying closely
connected entities which could be considered as part of a larger collective.
However in the real world not all communities of similar sizes are considered
equal, from both the perspective of the network operator as well as their
participants. In modern community related research, the task now involves being
able to distinguish between different collections of tightly-connected users
via additional semantic information provided by the network.

In this thesis we examine three tasks of modelling communities with positive
connotations on non-standard graphs; (1) groups consisting of a diverse user
make-up regardless of underlying demographic information, (2) groups which
probabilistically share common purchasing behaviours or characteristics as well
as (3) groups of users who largely trust each other without devolving into an
'echo-chamber' (a modern phenomenon linked heavily with the spread of
misinformation or 'fake news'). We discuss the logic behind the overarching
design of our subgraph structures (and how they specifically relate to
real-world requirements) as well describe the algorithms we propose to
efficiently find them.


Date:                   Friday, 15 December 2023

Time:                   12:00noon - 2:00pm

Venue:                  Room 3494
                        lifts 25/26

Committee Members:      Prof. Lei Chen (Supervisor)
                        Dr. Shuai Wang (Chairperson)
                        Prof. Ke Yi
                        Dr. Binhang Yuan


**** ALL are Welcome ****