Efficient Batch Dynamic Graphlet Counting

TL;DR

This paper introduces a scalable, efficient algorithm for maintaining fixed-sized graphlet counts in dynamic networks, significantly outperforming previous static approaches.

Contribution

It presents the first practical algorithm for dynamic graphlet counting that updates counts efficiently based on local changes in the network.

Findings

Over 10x faster than baseline methods

Efficiently updates graphlet counts in real-time

Applicable to large, evolving networks

Abstract

Graphlet counting is an important problem as it has numerous applications in several fields, including social network analysis, biological network analysis, transaction network analysis, etc. Most of the practical networks are dynamic. A graphlet is a subgraph with a fixed number of vertices and can be induced or non-induced. There are several works for counting graphlets in a static network where graph topology never changes. Surprisingly, there have been no scalable and practical algorithms for maintaining all fixed-sized graphlets in a dynamic network where the graph topology changes over time. We are the first to propose an efficient algorithm for maintaining graphlets in a fully dynamic network. Our algorithm is efficient because (1) we consider only the region of changes in the graph for updating the graphlet count, and (2) we use an efficient algorithm for counting graphlets in the region of change. We show by experimental evaluation that our technique is more than 10x faster than the baseline approach.