Scalable Graph Algorithms

TL;DR

This paper reviews a broad spectrum of scalable graph algorithms developed over six years, emphasizing multilevel, parallel, kernelization, and memetic methods that outperform previous approaches in efficiency and solution quality.

Contribution

It introduces novel scalable graph algorithms based on four interconnected pillars, significantly improving performance over prior state-of-the-art methods.

Findings

Algorithms find better solutions than previous methods

Algorithms are more scalable for large networks

Experimental results demonstrate improved efficiency

Abstract

Processing large complex networks recently attracted considerable interest. Complex graphs are useful in a wide range of applications from technological networks to biological systems like the human brain. Sometimes these networks are composed of billions of entities that give rise to emerging properties and structures. Analyzing these structures aids us in gaining new insights about our surroundings. As huge networks become abundant, there is a need for scalable algorithms to perform analysis. A prominent example is the PageRank algorithm, which is one of the measures used by web search engines such as Google to rank web pages displayed to the user. In order to find these patterns, massive amounts of data have to be acquired and processed. Designing and evaluating scalable graph algorithms to handle these data sets is a crucial task on the road to understanding the underlying systems. This habilitation thesis is a summary a broad spectrum of scalable graph algorithms that I developed over the last six years with many coauthors. In general, this research is based on four pillars: multilevel algorithms, practical kernelization, parallelization and memetic algorithms that are highly interconnected. Experiments conducted indicate that our algorithms find better solutions and/or are much more scalable than the previous state-of-the-art.