Solving Graph Theory Problems
Graph theory is a branch of mathematics that studies graphs, which are mathematical structures used to model pairwise relations between objects. A graph in this context is made up of vertices (also called nodes or points) which are connected by edges (also called links or lines). A graph can be directed or undirected, weighted or unweighted. Graphs are one of the prime objects of study in discrete mathematics.
Problems in graph theory include graph or subgraph isomorphism, finding the largest clique in a graph, finding the chromatic number of a graph, finding the shortest path between two vertices, finding the minimum spanning tree of a graph, finding the maximum flow in a graph, etc. Currently, existing classical algorithms can solve these problems effectively. However, these algorithms are not scalable and cannot be applied to large-scaled graphs. In this project, I will explore how to use Deep Learning to solve graph theory problems with targeted large-scaled graphs and applications in drug discovery, social network analysis, etc.
Publications
Duc Q. Nguyen
CS Master Student
My research interests include Generative Models, Graph Representation Learning, and Probabilistic Machine Learning. My application interests include Natural Language Processing, Healthcare, and Education.