Deep Learning On Graph Data

Nowadays, Graph Representation Learning has seen incredible success across a wide range of modern applications such as recommendation systems, drug discovery, computer vision, particle physics, combinatorial optimization, human activity recognition, etc. In general, a graph consists of nodes and edges where edges depict the relationship between nodes. For instance, the users are nodes in a social network, and edges represent their friendship/interactions. In brief, Graph Neural Networks (GNNs) are designed to compute the low dimensional meaningful embeddings for nodes/graphs through utilizing the structural information and node attributes (if available). These latent representations are used in different downstream tasks e.g., node classification, graph classification, link prediction, community detection/clustering, etc. The high-impact applications of GNNs are leading the research towards developing more advanced deep learning methods. In the high-level view, we aim to leverage probabilistic graphical models, self-supervision learning, contrastive learning, tensor decomposition, and Bayesian modeling into developing more robust, interpretable, and explainable GNNs.