Graph Compression Using Pattern Matching Techniques

TL;DR

This paper introduces novel lossless graph compression algorithms based on pattern matching in adjacency matrices, achieving around 70% reduction in size, facilitating efficient storage and processing of large-scale graph data.

Contribution

The paper proposes new pattern matching-based algorithms for lossless graph compression, significantly reducing storage requirements for large graphs.

Findings

Achieved approximately 70% compression compared to adjacency matrix.

Enabled efficient storage and transfer of large graph data.

Facilitated parallel processing of compressed graphs.

Abstract

Graphs can be used to represent a wide variety of data belonging to different domains. Graphs can capture the relationship among data in an efficient way, and have been widely used. In recent times, with the advent of Big Data, there has been a need to store and compute on large data sets efficiently. However, considering the size of the data sets in question, finding optimal methods to store and process the data has been a challenge. Therefore, in this paper, we study different graph compression techniques and propose novel algorithms to do the same. Specifically, given a graph G = (V, E), where V is the set of vertices and E is the set of edges, and |V| = n, we propose techniques to compress the adjacency matrix representation of the graph. Our algorithms are based on finding patterns within the adjacency matrix data, and replacing the common patterns with specific markers. All the techniques proposed here are lossless compression of graphs. Based on the experimental results, it is observed that our proposed techniques achieve almost 70% compression as compared to adjacency matrix representation. The results show that large graphs can be efficiently stored in smaller memory and exploit the parallel processing power of compute nodes as well as efficiently transfer data between resources.