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 ...

Scalable graph processing system with Neo4j and distributed data pipeline using Kubernetes, Kafka, and Docker for NYC taxi trip analysis. Two-phase project implementing graph algorithms and building a ...

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 ...

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 ...

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 ...

TigerGraph’s graph storage engine and processing engine are implemented in C++. Within the family of general purpose procedural languages, C and C++ are considered lower-level compared to other ...