Besides its NP-completeness, the strict constraints of subgraph isomorphism are making it
impractical for graph pattern matching (GPM) in the context of big data. As a result, relaxed …

Querying heterogeneous and large-scale knowledge graphs is expensive. This paper
studies a graph summarization framework to facilitate knowledge graph search.(1) We …

We propose a class of functional dependencies for graphs, referred to as GFDs. GFDs
capture both attribute-value dependencies and topological structures of entities, and …

This article presents GRAPE, a parallel GRAP h E ngine for graph computations. GRAPE
differs from prior systems in its ability to parallelize existing sequential graph algorithms as a …

We propose techniques for processing SPARQL queries over a large RDF graph in a
distributed environment. We adopt a “partial evaluation and assembly” framework …

We propose graph-pattern association rules (GPARs) for social media marketing. Extending
association rules for itemsets, GPARs help us discover regularities between entities in social …

The incremental problem for a class Q of graph queries aims to compute, given a query Q
in'Q, graph G, output Q (G) and updates Δ G to G as input, changes Δ O to Q (G) such that Q …

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 proposes to deduce certain fixes to graphs G based on data quality rules Σ and
ground truth Γ (ie, validated attribute values and entity matches). We fix errors detected by Σ …

Many Big Data applications in business and science require the management and analysis
of huge amounts of graph data. Previous approaches for graph analytics such as graph …