PhD Candidate
UC San Diego
Areas of Interest
- Information, Data, Network, and Communication Sciences
- Signal Processing, Theory
Poster
Universal Graph Compression
Abstract
Motivated by the prevalent data science applications of processing and mining large-scale graph data such as social networks, web graphs, and biological networks, as well as the high I/O and communication costs of storing and transmitting such data, lossless compression of data appearing in the form of a labeled graph is investigated. A universal graph compression scheme is proposed, which does not depend on the underlying statistics/distribution of the graph model. For graphs generated by a stochastic block model, which is a widely used random graph model capturing the clustering effects in social networks, the proposed scheme achieves the optimal theoretical limit of lossless compression without the need to know edge probabilities, community labels, or the number of communities.
Bio
Alankrita Bhatt is a PhD student in the department of electrical and computer engineering, at the University of California San Diego. Her research interests lie broadly in the field of information theory. Prior to joining UCSD, she received a bachelors degree in electrical engineering from the Indian Institute of Technology Kanpur.
