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 …

Due to the growing need to process large graph and network datasets created by modern
applications, recent years have witnessed a surging interest in developing big graph …

Recent studies showed that single-machine graph processing systems can be as highly
competitive as clusterbased approaches on large-scale problems. While several out-of-core …

Graph analytics systems have gained significant popularity due to the prevalence of graph
data. Many of these systems are designed to run in a shared-nothing architecture whereby a …

It is common for real-world applications to analyze big graphs using distributed graph
processing systems. Popular in-memory systems require an enormous amount of resources …

The problem of finding connected components in undirected graphs has been well studied.
It is an essential pre-processing step to many graph computations, and a fundamental task …

Smartphone applications are becoming increasingly popular. However, these applications
can suffer limited power budgets or malfunctioned sensing issues from their host devices …

Graph Pattern Matching (GPM), usually evaluated through subgraph isomorphism, finds
subgraphs of a large data graph that are similar to an input query graph. It has many …

It is common for real-world applications to analyze big graphs using distributed graph
processing systems. Popular in-memory systems require an enormous amount of resources …

Nowadays, many real-world applications can be represented as machine learning and
graph processing (MLGP) problems, and require sophisticated analysis on massive …