A quick introduction to 10 basic graph algorithms with examples and visualisationsContinue reading on Towards Data Science »

sheets/ Best, Ryan submitted by /u/RyanAI100

We are in the era of graphs. Graphs are hot. Why? Flexibility is one strong driver: heterogeneous data, integrating new data sources, and analytics all require flexibility. Graphs deliver it in spades. Over the last few years, a number of new graph databases

Subgraphing is a common use case when working with graphs. We often find ourselves wanting to take some small portion of a graph and then operate only upon it. Gremlin provides subgraph() step, which helps to make this operation relatively easy by exposing a way to produce an edge-induced subgraph that is detached from the parent graph.

suited tool to present data where connections and links are important for us to understand it. Like molecules structure that presents a collection of basic atoms which are linked to other, forming complex structure where each atom’s connection in this collection means something’s in terms of the usage or the characteris

Technology research and analyst house Bloor Research places Ontotext as champion among RDF graph providers in its latest update on the graph database market

Graph Databases are a great solution for many modern use cases: Fraud Detection, Knowledge Graphs, Asset Management, Recommendation Engines, IoT, Permission Management … you name it.  All such projects benefit from a database technology capable of analyzing highly connected data points and their relations fast – Graph databases are designed for these tasks. But the nature of graph data poses challenges when it comes to *buzzword alert* scalability. So why is this, and are graph databases capable of scaling? Let’s see... In the following, we will define what we mean by scaling, take a closer look at two challenges potentially hindering scaling with graph databases, and discuss solutions currently available. What Is the “Scalability of Graph Databases”? Let’s quickly define what we mean here by scaling, as it is not “just” putting more data on one machine or throwing it on various ones. What you want when working with large or growing datasets is also an acceptabl

Photo by AndreyPopov on iStockWe all use the Google Knowledge Graph tens of times a day, but maybe not many of us are aware to be actually querying the Graph while making a simple search on Google.When you search for something, for example, “Goldman Sachs”, what you get is a list of snippets of web pages plus an Infobox next to the search results.The Knowledge Graph behind your Google search allows to enhance the search engine with specific and possibly useful features on the “entity” you are looking for (in this case Goldman Sachs), gathered from a variety of sources. So, allegedly, Google Knowledge Graph enhances the result of our search with semantics [4].Let’s now try to consider reasoning.Now, say we are studying Goldman Sachs for some reason and we wish to know whether there is some person x in Goldman Sachs board who is the CEO of some other company y that is in the Tech field?Or in other terms, in a ‘fancy’ logic conjunctive query fashion:∃ x y board(Goldm

https://douroucouli.wordpress.com/2019/05/10/obographviz-0-2-2-released-now-with-visualization-of-equivalence-cliques/

VLog, a new rule based reasoner on #KnowledgeGraphs, with #opensource implementation on #Github #iswc_conf #research #sfotwareengineering h/t

Learn about how AstraZeneca visualized patient journeys, answered important questions about prescriptions and diagnoses, and improved patient outcomes.

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down in that approach. Data analysis (and modeling)

Gremlin Snippets are typically short and fun dissections of some aspect of the Gremlin language. For a full list of all steps in the Gremlin language see the Reference Documentation of Apache TinkerPop™. This snippet is based on Gremlin 3.4.7.Please consider bringing any discussion or questions about this snippet to the Gremlin Users Mailing List.

In part 1 of this article series, I provided a quick primer on graph data structure, acknowledged that there are several graph based algorithms with the notabl…

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Dr Nicolas FigayDigital Enterprises Organisation and Collaboration around Manufacturing and Product Data2w · EditedEmerging Landscape of #graphs related technologies: required move from da facto standards to #ISO open standard?

explorerThe keyword has and hasLabel does all the m

Neo4j’s Alicia Frame explains how life science researchers can exploit graph databases to get truly granular insight into big data to make major leaps forward in medical research.Complex data sets hold the key to advancing medical breakthroughs. These data sets tend to be voluminous and heterogeneous by nature, presenting an insurmountable challenge for traditional data analysis methods as they struggle to link patterns and outcomes. The unfortunate consequence is a slowdown in the progress of research.Anyone who works in life sciences is aware that they are working with highly connected information; the challenge is making sense of these connections. Unfortunately, many scientists are still using relational databases and spreadsheets which makes mapping important patterns and connections unintuitive and difficult, if not impossible.Graph technologyGraph technology is emerging as an enabler for researchers to trawl gargantuan amounts of unstructured data, turning it into valuab

tier_architectureHowever, knowledge graphs propose a paradigm shift to this design blurring the barrier between logic and data. By bringing some of the knowledge of the domain into a graph through rules a knowledge graph captures more than just the data in the system. As a result, rules can make the queries and requests much simpler to write and manage which in turns allows applications to be more flexible, less error prone and faster.This article will introduce a simple example to showcase the impact of rules on query design. The example will be ill

based search systems do not reflect the semantics of individual input words of search queries. For example, a query for the word “house” would not return records for the words “building” or “real estate”. How can such relationships be represented in a technical system? One approach is to include synonyms. Search engines like Elasticsearch provide methods to integrate synonym lists. However, a list of synonyms itself is required for configuration.

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based models have shown their unique advantages in knowledge graph (KG)

According to Reinforcement Knowledge Graph Reasoning for Explainable Recommendation

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triple store which we will query with SPARQL. If you are not yet familiar with knowledge graphs and reasoning, you can read an introduction published on