Approximate Graph Mining with Label Costs

Aug 1, 2013·
Pranay Anchuri
Pranay Anchuri
· 0 min read
Abstract
We present novel and scalable methods for approximate frequent subgraph mining from exact and probabilistic graphs. By incorporating label costs, the approach yields more informative and compact pattern sets from real-world graphs spanning IT infrastructure to protein interaction networks.
Type
Publication
In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
publications
Graph Mining Approximate Algorithms Data Mining
Pranay Anchuri
Authors
Pranay Anchuri
Senior Research Scientist
Pranay Anchuri is a Senior Research Scientist at Offchain Labs. His research spans blockchain protocols, verifiable computation, and machine learning applied to decentralized systems. He has published at top venues including KDD, JMLR, and ICDM, and is an inventor on seven US patents. He holds a PhD in Computer Science from Rensselaer Polytechnic Institute.