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The Neptune ML feature makes it possible to build and train useful machine learning models on large graphs in hours instead of weeks. To accomplish this, Neptune ML uses graph neural network (GNN) technology powered by Amazon SageMaker and the Deep Graph Library (DGL) (which is open-source). Graph neural networks are an emerging field in artificial intelligence (see, for example, A Comprehensive Survey on Graph Neural Networks). For a hands-on tutorial about using GNNs with DGL, see Learning graph neural networks with Deep Graph Library.

Neptune Networks, a fixed income network for disseminating real-time axe data, has announced senior management changes at the firm. Following agreement by the Board, John Robinson will move to the role of Executive Chairman and Byron Cooper-Fogarty will become CEO on April 1, 2024. Robinson is currently CEO of Neptune, having joined the firm in November 2020. Current Neptune COO, Cooper-Fogarty has been at Neptune for seven years, and previously held the role of Interim CEO in 2020.

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NEPTUNE is a proposed system of high-speed fiber- optic submarine cables linking a series of seafloor nodes supporting thousands of assorted measuring instruments, video equipment, and robotic vehicles that could upload power and download data at undersea docks. Unlike conventional telephone cables, which supply power from shore in a straight line, end to end, NEPTUNE would operate like a power grid, distributing power simultaneously and as needed throughout the network. Working much like a campus data network (with nodes analogous to buildings and each instrument like a workstation), NEPTUNE would provide real-time transmission of data and two-way communications.

Forecasting traffic speed, volume or the density of roads in traffic networks is fundamentally important in a smart transportation system. We can address the traffic prediction problem by using STGNNs.

Considering the traffic network as a spatial-temporal graph where the nodes are sensors installed on roads, the edges are measured by the distance between pairs of nodes, and each node has the average traffic speed within a window as dynamic input features.

The application of GNNs is not limited to the above domains and tasks. There have been attempts to apply GNNs to a variety of problems such as program verification, program reasoning, social influence prediction, recommender systems, electrical health records modeling, brain networks, and adversarial attack prevention.

Modeling real-world physical systems is one of the most basic aspects of understanding human intelligence. By representing objects as nodes and relations as edges, we can perform GNN-based reasoning about objects, relations, and physics in an effective way. Interaction networks can be trained to reason about the interactions of objects in a complex physical system. It can make predictions and inferences about various system properties in domains such as collision dynamics

Combinatorial optimization (CO) is a topic that consists of finding an optimal object from a finite set of objects. It is the base of many important applications in finance, logistics, energy, science, and hardware design. Most CO problems are formulated with graphs. In a recent work by DeepMind and Google, graph nets are used for two key subtasks involved in the MILP solver: joint variable assignment and bounding the objective value. Their neural network approach is faster than existing solvers on big datasets

Byron Cooper-Fogarty will become CEO Neptune Networks, the fixed income network for disseminating real-time axe data, and on 1 April 2024 with John Robinson will move to the role of executive chairman. Neptune currently reports supporting over 90 buy-side firms managing US$60 trillion in global AUM.

Robinson is currently CEO of Neptune, having joined the firm in November 2020. During his time in the role, he has moved Neptune to a more modern platform by partnering with Genesis Global. He has also used his network to raise the profile of Neptune in the US Credit and EM Credit markets.

Telecoms services are becoming increasingly complex, demanding ever-increasing bandwidth and service performance guarantees. To keep pace with this exponential traffic growth and complexity a better backhaul transport network is required. This new backhaul transport must have the agility to provide the scalability, fast convergence, ease of configuration and service performance required to support the new services cost effectively.

High-bandwidth access networks are just a starting point on the road to high-speed broadband. A new aggregation and transport network is needed to complete the journey and make broadband for all a reality. With service-aware traffic steering and IP Optical integration, Neptune provides high-capacity next-generation aggregation and transport network required to bridge the gap between the high-bandwidth access pipes and the performance-dependent services these pipes need to transport.

Operators now have a need to retire their SDH/SONET networks and migrate onto a modern IP Transport network. Fortunately, with the capabilities Neptune provides, this is a good time to undertake this migration. We have well established, field proven, processes to make this migration seamless and risk-free and we have the technology to support the legacy SDH/SONET services on the IP Transport network in a manner, which is at least equivalent to, if not better than the legacy SDH/SONET network.

Network analytics tools are a valuable way to analyze the traffic patterns of an autonomous system. They can be used to gain valuable insights into ingress and egress traffic, identify potential peering relationships, and help in troubleshooting a network.

In this article I explore creating a network analytics system with Prometheus and Grafana. Prometheus is a time series database complete with a query language (PromQL), and Grafana is a way to visualize that data. The end result is a dashboard that looks like this:

Funding Acknowledgments: The Nephrotic Syndrome Rare Disease Clinical Research Network III (NEPTUNE) is part of the Rare Diseases Clinical Research Network (RDCRN), which is funded by the National Institutes of Health (NIH) and led by the National Center for Advancing Translational Sciences (NCATS) through its Office of Rare Diseases Research (ORDR). NEPTUNE is funded under grant number U54DK083912 as a collaboration between NCATS and the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). Additional funding and/or programmatic support for this project has also been provided by the University of Michigan, NephCure Kidney International, and the Halpin Foundation. All RDCRN consortia are supported by the network's Data Management and Coordinating Center (DMCC) (U2CTR002818). Funding support for the DMCC is provided by NCATS and the National Institute of Neurological Disorders and Stroke (NINDS).

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