Many scientific fields study data with an underlying structure that is a non-Euclidean space. Some examples include social networks in computational social sciences, sensor networks in communications, functional networks in brain imaging and regulatory networks in genetics. For this reason, Graph Neural Networks have recently emerged as an interesting methodology of analysing graphs whilst leveraging the power of deep learning. Being their ability to fit many real-world datasets, which have an inherent graph structure, GNNs have found applications in many different domains including:
Applications of GNNs in computer vision include scene graph generation, point cloud classification and segmentation, action recognition, etc.
Recognising semantic relationships between objects facilitates the understanding of the meaning of a visual scene. Scene graph generation models aim to parse an image into a semantic graph which consists of objects and their semantic relationships. Another application reverses the process by generating realistic images given scene graphs. This hints at the intriguing possibility of synthesising images given textual descriptions.
Traffic Accurately forecasting traffic speed, volume or the density of roads in traffic networks is fundamentally important in a smart transportation system. The authors of address the traffic prediction problem using Spatio-Temporal GNNs.
Recommender Systems Graph-based recommender systems consider items and users as nodes.
Chemistry In the field of chemistry, researchers apply GNNs to study the graph structure of molecules/compounds.
Though GNNs have proven their power in learning graph data, challenges still exist due to the complexity of graphs. This PhD project aims to target some urgent challenges and issues facing the generalisation of GNNs,
Model Depth: the performance of a ConvGNN drops dramatically with an increase in the number of graph convolutional layers. This raises the question of whether going deep is still a good strategy for learning graph data.
Scalability Trade-off The scalability of GNNs is achieved at the price of corrupting graph completeness. To perform the pooling operation to coarsen graphs, some works use sampling , others use clustering, in both approaches, the model will lose part of the graph information. By sampling, a node may miss its influential neighbors. By clustering, a graph may be deprived of a distinct structural pattern. How to trade-off algorithm scalability and graph integrity could be a future research direction.
The aim of this PhD is to develop new algorithms that tackle these challenges and extend GNNs to different usage. This target will be demonstrated by showing the performance improvement in distinct application domains.