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 …

In this paper, we present GridGraph, a system for processing large-scale graphs on a single
machine. Grid-Graph breaks graphs into 1D-partitioned vertex chunks and 2D-partitioned …

The performance bottlenecks of graph applications depend not only on the algorithm and
the underlying hardware, but also on the size and structure of the input graph. As a result …

Chaos scales graph processing from secondary storage to multiple machines in a cluster.
Earlier systems that process graphs from secondary storage are restricted to a single …

Processing a one trillion-edge graph has recently been demonstrated by distributed graph
engines running on clusters of tens to hundreds of nodes. In this paper, we employ a single …

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 …

Continuous processing of a streaming graph maintains an approximate result of the iterative
computation on a recent version of the graph. Upon a user query, the accurate result on the …

As graphs continue to grow, external memory graph processing systems serve as a
promising alternative to inmemory solutions for low cost and high scalability. Unfortunately …

Various applications model problems as streaming graphs, which need to quickly apply a
stream of updates and run algorithms on the updated graph. Furthermore, many dynamic …