George's got tech, data, and media, and he's not afraid to use them.
As noted in Alibaba’s work, an increasing number of machine learning tasks require dealing with large graph datasets, which capture rich and complex relationships among potentially billions of elements. Graph Neural Networks (GNN) become an effective way to address the graph learning problem.
Graph analytics is a super hot area of research right now because we’re entering a world dominated by machine learning. There are many types of traditional graph analytics which don’t require machine learning. With respect to GraphML, there are three paradigms of processing which typify thousands of individual analysis types: Smart Data Extraction, Data Structure Analysis, Full Throttle GraphML.
Using node classification with graph convolutional networks (GCN) as a case study, we’ll look at how to measure the importance of specific nodes and edges of a graph in the model’s predictions. This will involve exploring the use of saliency maps to look at whether the model’s prediction will change if we remove or add a certain edge, or change node features.
In a connected world, users cannot be considered as independent entities. They have relationships, and we would sometimes like to include such relationships while building our machine learning models. In this post, I am going to be talking about some of the most important graph algorithms you should know and how to implement them using Python.
With the release of version 0.9, NVIDIA cuGraph is coming one step closer to 1.0. As Rees explained, the goal is not just to keep adding algorithms to cuGraph, but to make them work over multiple GPUs, too. This has now been achieved for PageRank. Even in version 0.6, however, cuGraph was already up to 2000 times faster than NetworkX.
If you’re out to shop for a graph database, you will soon realize that there are no universally supported standards, performance evaluation is a dark art, and the vendor space seems to be expanding by the minute. So, what’s all the fuss about? What are some of the things graph databases are being used for, what are they good at, and what are they not so good at?
We need to build a community of AI researchers educated in what graph algorithms can do at scale & how deep learning algorithms can reinforce graph algorithms to build advanced HTAP solutions. We need to make hardware vendors understand the needs of the scaleable graph algorithm community. We need support for high-level declarative graph languages that perform queries over distributed native graph databases.
At some point, a researcher isn’t going to be able to improve the accuracy level of their model any further. By augmenting their feature sets with graph features, like path distance, teams were able to unlock new gains in their model’s accuracy. Machine learning teams model and extract graph features to enhance the accuracy of their predictive models.
Reconciling entities means “providing computers with unambiguous identifications of the entities we talk about.” This patent from Google focuses upon a broader use of the word “Reconciliation” and how it applies to knowledge graphs, to make sure that those take advantage of all of the information from web sources that may be entered into those about entities.
Pinterest is a discovery engine that connects ideas across a taste graph, so for every Pin on Pinterest, there are Related Pins (Pins that are visually and semantically similar to that Pin), which we are always working to keep fresh. When thinking about how to visualize and construct these complex Pinterest Paths, it can be useful to think of a Pinterest Path as a graph.
And so we come to the reason we’re hearing about the “rise of knowledge graphs” in recent books and articles and at conferences: it’s solving a problem in vocabulary architecture that’s becoming increasingly important as the foundation of AI and other technologies (not to mention search).
Temporal dependencies quickly explode into a highly connected network, which best can be handled by a graph DBMS. Complexity must be hidden for both data modelers and business users by way of some higher-level concepts. I wrote this post to provoke vendors into considering this architectural sketch for their future product development.
In July 2017, W3C published SHACL as the standard to validate RDF. Since then, data modellers have the possibility to provide validation services based on SHACL shapes together with their models, however there are considerations to be taken in account when creating them.
NSMNTX is a plugin that enables the use of RDF in Neo4j. RDF is a W3C standard model for data interchange. This effectively means that NSMNTX makes it possible to Store RDF data in Neo4j in a lossless manner, on-demand export property graph data from Neo4j as RDF. Other features in NSMNTX include model mapping and inferencing on Neo4j graphs.
Examples of new features in the Gremlin query and traversal language such as text predicates, changes to valueMap, nested repeat steps, named repeat steps, non-numerical comparisons, and changes to the order step. It is worth pointing out that TinkerPop 3.4 has a few important differences from TinkerPop 3.3. Be sure to review the compatibility notes in the engine releases documentation.
Dgraph’s new release v1.1.0 is here. The new version ships with a plethora of significant changes and new features. In this post, we will cover the most important ones, but you can find all the details in the changelog.
The new k-shortest path feature provides the option to query for all shortest paths between two given vertices, returning sorted results based on path length or path weight. Imagine you transferred a transportation network to a graph dataset and now navigate between two given points. You can query for shortest travel distance, shortest travel time, or any other information you have stored on edges.
In this study, we present the first results of a complete implementation of the LDBC SNB benchmark — interactive short, interactive complex, and business intelligence — in two native graph database systems — Neo4j and TigerGraph.
Deep learning-based predictive analytics and alerting (Siren ML). Deep learning-based time series anomaly detection. Unstructured data discovery with real-time topic clustering. Associative in-dashboard Relational Technology (“Dashboard 360”). State-of-the-art, self-correcting entity resolution (Siren ER).