The vertex-centric programming model is an established computational paradigm recently
incorporated into distributed processing frameworks to address challenges in large-scale …

The world is becoming a more conjunct place and the number of data sources such as
social networks, online transactions, web search engines, and mobile devices is increasing …

Traditionally distributed graph processing systems have largely focused on scalability
through the optimizations of inter-node communication and load balance. However, they …

Natural graphs with skewed distributions raise unique challenges to distributed graph
computation and partitioning. Existing graph-parallel systems usually use a “one-size-fits-all” …

Graph-structured analytics has been widely adopted in a number of big data applications
such as social computation, web-search and recommendation systems. Though much prior …

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 …

There has been a growing interest in the graph-streaming setting where a continuous
stream of graph updates is mixed with graph queries. In principle, purely-functional trees are …

Graph databases are becoming increasingly popular for modeling different kinds of
networks for data analysis. They are built over the property graph data model, where nodes …

Efficient streaming graph processing systems leverage incremental processing by updating
computed results to reflect the change in graph structure for the latest graph snapshot …

Graph mining is an important category of graph algorithms that aim to discover structural
patterns such as cliques and motifs in a graph. While a great deal of work has been done …