Research
In Data Mining Lab, we conduct research about modelling and analysing vast and complex real-world data. In particular, we are focusing on research related to the analysis of graph data that can be easily observed in our real life. With the proliferation of the development in IT industry and the mobile industry, social networks can be used anytime and anywhere. In these social networks, we are conducting research such as friend recommendation, influence maximisation, network stability, and identifying core structures. Beyond these research topics, we deeply study relationships that humans form, and thus, we aim to contribute to understanding humans.
Research Topic
Cohesive Subgraph Discovery
The community detection problem is a computational task of finding groups of nodes that are more densely connected with each other than with the rest of the network.
Community Search
The query-centric community discovery problem is the computational challenge of identifying a group of interconnected entities from a network, based on a specific query or set of parameters.
Influence Maximisation
Influence maximization is the strategic process of identifying a set of users who will influence the maximum number of other users to adopt the same behavior through their social connections.
Network Stability
The network stability problem involves ensuring that a network continues to operate efficiently and effectively even when it experiences disruptions, such as traffic surges, failures, or attacks.