Scalable Compression of a Weighted Graph
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.
Cite
@article{arxiv.1611.03159,
title = {Scalable Compression of a Weighted Graph},
author = {Kifayat Ullah Khan and Waqas Nawaz and Young-Koo Lee},
journal= {arXiv preprint arXiv:1611.03159},
year = {2016}
}