Big data is typically characterized with 4V's: Volume, Velocity, Variety and Veracity. When it
comes to big graphs, these challenges become even more staggering. Each and every of …

This paper studies incremental graph partitioning. Given a (vertex-cut or edge-cut) partition
C (G) of a graph G and updates ΔG to G, it is to compute changes ΔO to C (G), yielding a …

The ever-growing amount of dynamic graph data demands efficient techniques of
incremental graph processing. However, incremental graph algorithms are challenging to …

Incremental algorithms are important to dynamic graph analyses, but are hard to write and
analyze. Few incremental graph algorithms are in place, and even fewer offer performance …

This article proposes an Adaptive Asynchronous Parallel (AAP) model for graph
computations. As opposed to Bulk Synchronous Parallel (BSP) and Asynchronous Parallel …

With the rise of streaming data for dynamic graphs, large-scale graph analytics meets a new
requirement of Incremental Computation because the larger the graph, the higher the cost …

Property graphs are a common form of linked data, with path queries used to traverse and
explore them for enterprise transactions and mining. Temporal property graphs are a recent …

The graph data keep growing over time in real life. The ever-growing amount of dynamic
graph data demands efficient techniques of incremental graph computation. However …

Property graphs are a common form of linked data, with path queries used to traverse and
explore them for enterprise transactions and mining. Temporal property graphs are a recent …

Real-world graphs are constantly evolving, which demands updates of the previous analysis
results to accommodate graph changes. By using the memoized previous computation state …