There is a growing need to perform a diverse set of real-time analytics (batch and stream
analytics) on evolving graphs to deliver the values of big data to users. The key requirement …

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 …

Graph mining is one of the most important areas in data mining. However, scalable solutions
for graph mining are still lacking as existing studies focus on sequential algorithms. While …

There is no doubt that we are entering the era of big data. The challenge is on how to store,
search, and analyze the huge amount of data that is being generated per second. One of the …

Graph transaction processing poses unique challenges such as random data access due to
the irregularity of graph structures, low throughput and high abort rate due to the relatively …

The recent advancements in big data and natural language processing (NLP) have
necessitated proficient text mining (TM) schemes that can interpret and analyze voluminous …

Given a user-specified minimum degree threshold γ γ, a γ γ-quasi-clique is a subgraph
where each vertex connects to at least γ γ fraction of the other vertices. Quasi-clique is a …

The use of graph visualization approaches to present and analyze complex data is taking a
leading role in conveying information and knowledge to users in many application domains …

The property graph (PG) model is one of the most general graph data model and has been
widely adopted in many graph analytics and processing systems. However, existing systems …

Designing distributed graph systems has drawn a lot of research interests due to the strong
expressiveness of the graph model and rapidly increasing graph volume. Most of them …