While distributed graph processing engines have become popular for processing large graphs, these engines are typically configured with a static set of servers in the cluster. In other words, they ...

Graphs are a natural fit for modeling concepts used in solving diverse problems in science, commerce, engineering, and governance. Responding to the variety of graph data and algorithms, many parallel ...

The considerable interest in distributed systems that can execute algorithms to process large graphs has led to the creation of many graph processing systems. However, existing systems suffer from two ...

Neo4j is the leader in the burgeoning graph database market, with 17 years in development and thousands of open source users. But the database has a hard limit in terms of scalability, since it ...

We present OPTiC, a multi-tenant scheduler intended for distributed graph processing frameworks. OPTiC proposes opportunistic scheduling, whereby queued jobs can be pre-scheduled at cluster nodes when ...

Efficiently and quickly chewing through one trillion edges of a complex graph is no longer in itself a standalone achievement, but doing so on a single node, albeit with some acceleration and ...

Processing extremely large graphs has been and remains a challenge, but recent advances in Big Data technologies have made this task more practical. Tapad, a startup based in NYC focused on cross-devi ...

Objectivity, Inc., a provider of data management solutions, has released the first version of its enterprise-ready distributed graph database product, following a successful beta program which began ...

Sure, there are graph databases like Neo4j, but graph analysis or graph search may be more useful, depending on the sorts of data relationships you need to explore Graph processing is hot right ...