The value of graph-based big data can be unlocked by exploring the topology and metrics of
the networks they represent, and the computational approaches to this exploration take on …

Efficient processing of large-scale graphs in distributed environments has been an
increasingly popular topic of research in recent years. Inter-connected data that can be …

Graphs are found in a plethora of domains, including online social networks, the World Wide
Web and the study of epidemics, to name a few. With the advent of greater volumes of …

In the past, the majority of data analysis use cases was addressed by aggregating relational
data. Since a few years, a trend is evolving, which is called “Big Data” and which has several …

Distributed, shared-nothing architectures of commodity machines are a popular design
choice for the implementation and deployment of big data platforms. The introduction of …

Graphs are found in a plethora of domains, including online social networks, the World Wide
Web and the study of epidemics, to name a few. With the advent of greater volumes of …

Graph databases and distributed graph computing systems have traditionally abstracted the
design and execution of algorithms by encouraging users to take the perspective of lone …

The motivating problem for this dissertation is how to perform machine learning with graph
structured datasets. In existing approaches, graph structured datasets are compressed …

In large-scale graph processing, a fixpoint iterative algorithm is a set of operations where
iterative computation is the core. The aim, in fact, is to perform repetitive operations refining …

The demand for mining large datasets using shared-nothing clusters is steadily on the rise.
Despite the availability of parallel processing paradigms such as MapReduce, scalable data …