  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.
