Graph Mining with Graph Neural Networks

Abstract

Graphs are ubiquitous data structures in various fields, such as social media, transportation, linguistics and chemistry. To solve downstream graph-related tasks, it is of great significance to learn effective representations for graphs. My research strives to help meet this demand; due to the huge success of deep learning methods, especially graph neural networks, in graph-related problems, my emphasis has primarily been on improving their power for graph representation learning. More specifically, my research spans across the following three main areas: (1) robustness of graph neural networks, where we seek to study the performance of them under random noise and carefully-crafted attacks; (2) self-supervised learning in graph neural networks, where we aim to alleviate their need for costly annotated data by constructing self-supervision to help them fully exploit unlabeled data; and (3) applications of graph neural networks, where my work is to apply graph neural networks in various applications such as traffic flow prediction. This research statement, 'Graph Mining with Graph Neural Networks', is focused on my research endeavors specifically related to the aforementioned three topics.