A Graph-Neural-Network Approach for Reconstructing Temporal Networks

Reconstructing a minimum reticulation network from phylogenetic trees is used in evolutionary studies. In this thesis, we focus on finding temporal networks using cherry-picking sequences for binary trees with all taxa. Finding such a minimum reticulation temporal network is NP-hard.<br/><br/>We introduce an algorithm to find a minimum...

CCS Reservoir Simulation using Graph Neural Networks: Building ML solutions for efficient CO2 subsurface modelling

Reducing cost and improving computability of reservoir simulation is an important goal in the process of enabling CCS (Carbon Capture \&amp; Storage) as a large-scale technology for mitigating CO2 emissions. In terms of computation time data-driven approaches have potential to outweigh the performance of numerical reservoir simulators, learning...

Graph Neural Networks for Intelligent Transportation Systems: A Survey

Graph neural networks (GNNs) have been extensively used in a wide variety of domains in recent years. Owing to their power in analyzing graph-structured data, they have become broadly popular in intelligent transportation systems (ITS) applications as well. Despite their widespread applications in different transportation domains, there is no...

Cyber Forensic Analysis for Operational Technology Using Graph-Based Deep Learning

The cyber attacks in Ukraine in 2015 and 2016 demonstrated the vulnerability of electrical power grids to cyber threats. They highlighted the significance of Operational Technology (OT) communication-based anomaly detection. Many anomaly detection methods are based on real-time traffic monitoring, i.e., Intrusion Detection Systems (IDS) that may...