Graph data modeling and querying arises in many practical application domains such as
social and biological networks where the primary focus is on concepts and their …

In this paper, we present, to our knowledge, the first known I/O efficient solutions for
computing the k-bisimulation partition of a massive directed graph, and performing …

Graph summarization is the process of computing a compact version of an input graph while
preserving chosen features of its structure. We consider semantic graphs where the features …

Bisimulation provides structural conditions to characterize indistinguishability from an
external observer between nodes on labeled graphs. It is a fundamental notion used in …

Bisimulation summaries of graph data have multiple applications, including facilitating graph
exploration and enabling query optimization techniques, but efficient, scalable, summary …

It is well known that graph simulation and bisimulation can capture the semantics of graphs,
described by the type and attribute of the nodes and edges. Graph pattern matching via …

Massive graphs are becoming increasingly common in a variety of domains such as social
networks and web analytics. One approach to overcoming the challenges of size is to …

Summarizing graphs wrt structural features is important to reduce the graph's size and make
tasks like indexing, querying, and visualization feasible. Our generic parallel BRS algorithm …

With the rise in adoption of massive graph data, it be-comes increasingly important to design
graph processing algorithms which have predictable behavior as the graph scales. This …

We developed a flexible parallel algorithm for graph summarization based on vertex-centric
programming and parameterized message passing. The base algorithm supports infinitely …