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 ...

Neo4j is an example of a native graph database that was built from the ground up to store pieces of data as nodes and express their connectedness through edges. Xu considers this “Graph 1.0.” The ...

Trovares Inc., a property graph analytics company started by the co-founder and former chief executive of Cray Inc., has raised $2 million to launch a new graph analytics engine.

Graph Mode will enable developers to ingest and schedule neural networks in TensorFlow or ONNX formats. Supported neural networks will include MobileNetV2, ResNet-50 and VGG16.

Graph algorithms and processing form the backbone of numerous applications across science ... In parallel, the challenge of processing massive graphs has seen innovative solutions through hardware ...

Since the ML Workbench is designed to work with enterprise-level data, it is highly scalable and can be used with very large graphs. TigerGraph lists ML Workbench’s built-in capabilities as the ...

What became apparent in the wake of these graph processing use cases down the scale chain is that for vast amounts of data, ... folks as the comparisons highlight similar benchmarks from other ...

In parallel processing, a software program is written or modified to identify what parts of the computation can be executed on separate processing hardware, Schardl says. Those parts of the ...

Graph algorithms and processing form the backbone of numerous applications across science and industry, ranging from social network analysis to large-scale data management.