Graphs have emerged as powerful representations of data, from obvious examples such as
social networks, to proximity graphs of high-dimensional metric data. Many of such real …

Today, networks are an integral part of our world. Let it be real-life friendship networks or
social connections that are based on social media. In this thesis, we contribute to the …

Many real world systems across multiple disciplines, like social, biological and information
networks can be described as complex networks, ie, assemblies of nodes and edges having …

Based on solid theoretical foundations, we present strong evidence that a number of real‐
world networks, taken from different domains (such as Internet measurements, biological …

Detecting the dimensionality of graphs is a central topic in machine learning. While the
problem has been tackled empirically as well as theoretically, existing methods have several …

Comparison of graph structure is a ubiquitous task in data analysis and machine learning,
with diverse applications in fields such as neuroscience, cyber security, social network …

Many real-world graphs have been shown to be scale-free---vertex degrees follow power
law distributions, vertices tend to cluster, and the average length of all shortest paths is …

Decomposing a graph into a hierarchical structure via k-core analysis is a standard
operation in any modern graph-mining toolkit. k-core decomposition is a simple and efficient …

Graphs (or networks) naturally represent valuable information for relational data, which are
ubiquitous in real-world applications, such as social networks, recommender systems, and …

What are the relations between the edge weights and the topology in real-world graphs?
Given only the topology of a graph, how can we assign realistic weights to its edges based …