Scalable Compression of a Weighted Graph

TL;DR

This paper introduces a scalable method for compressing weighted graphs to generate summaries that facilitate understanding large, complex real-world interaction networks, achieving significant performance improvements over existing techniques.

Contribution

It presents a novel scalable compression algorithm specifically designed for weighted graphs, improving summary accuracy and efficiency compared to prior methods.

Findings

Order of magnitude performance gain

Better summarization accuracy

Effective on real-world datasets

Abstract

Graph is a useful data structure to model various real life aspects like email communications, co-authorship among researchers, interactions among chemical compounds, and so on. Supporting such real life interactions produce a knowledge rich massive repository of data. However, efficiently understanding underlying trends and patterns is hard due to large size of the graph. Therefore, this paper presents a scalable compression solution to compute summary of a weighted graph. All the aforementioned interactions from various domains are represented as edge weights in a graph. Therefore, creating a summary graph while considering this vital aspect is necessary to learn insights of different communication patterns. By experimenting the proposed method on two real world and publically available datasets against a state of the art technique, we obtain order of magnitude performance gain and better summarization accuracy.