Graph Analytics at Enterprise Scale

Scalable Graph ML


Graph ML is the future of data analytics

Graph Query

Who can introduce me to x?

Graph Analytics

Who is the most connected person?

Graph ML

Predict potential connections

ArangoDB as the foundation for Graph ML

  • Scalable
    Designed from ground up to scale Enterprise use cases 
  • Simple Ingestion
    Easy integration in existing data infrastructure + connectors to all leading data processing and data ecosystems
  • Open Source
    Extensibility, Community, especially large community maintained library
  • NLP Support
    Built-In Text Processing, Search, and Similarity Ranking


ArangoDB and NetworkX

This notebook will illustrate how the Networkx adapter can be used to perform graph analytics tasks on graph data stored in ArangoDB with the IMDB movie dataset.

Property Graph Queries

In this notebook, we explore some basic graph queries using ArangoDB, including simple traversals and shortest path queries.

NLP with ArangoSearch

ArangoSearch provides information retrieval features, natively integrated into ArangoDB’s query language and with support for all data models. It is primarily a full-text search engine, a much more powerful alternative to the full-text index type.

Graph Analytics
Collaborative Filtering

We have all seen product recommendations like “People who have looked that item x, also bought item y.” For this, we consider a simple dataset with user ratings for movies and then use a technique called Collaborative Filtering to identify which new movies might be worth watching based on other movies we liked.

Graph Analytics
Fraud Detection

As you for sure know, money laundering and fraud is kind of a thing and a bad one. Over the years, fraudsters and money launderers got more sophisticated in hiding their money transfers.

Graph Analytics
Retail Data

This series of notebooks will explore the utility of ideas from graph analytics to analyze data from an online retail store. Analyze customer shopping data to determine loyal customer shopping preferences and improve their shopping experience.

R Driver

This notebook will provide an overview of the steps involved in using R and ArangoDB to work with Graph data. To do so, we will need the ArangoDB R driver.

Graph Embeddings

This notebook provides a first look at generating graph embeddings with our IMDB dataset. Generate movie recommendations with graph embeddings and store them in ArangoDB.

Iterative, Distributed Graph Analytics with Pregel

Distributed graph processing enables you to do online analytical processing directly on graphs stored in ArangoDB. This is intended to help you gain analytical insights on your data, without having to use external processing systems.

ArangoDB Oasis now comes with built-in example guides such as an extension to our Graph Embeddings notebook; create a new deployment and go to the examples tab to get started!

Use Cases

Fraud Detection & Analytics

Today’s criminals are constantly coming up with new techniques to hide their activities by forming fraud networks with stolen or synthetic identities.

In many cases, attacks are launched from multiple vectors and can only be discovered by connecting diverse data sources to uncover difficult-to-detect patterns. Beyond Graph technology is perfect to solve this challenge.
Fraud detection is a multi-dimensional prolem

Enterprise Knowledge Graphs

Enterprise Knowledge Graphs (EKGs) have been on the rise and are incredibly valuable tools for harmonizing internal and external data relevant to an organization into a common semantic model. Enterprises benefit from improved operational efficiency and competitive advantages for their business units.

Taking a Beyond Graph approach to EKGs often leads to advantages.

Talks & Events

Lunch Break:
Graph Analytics with ArangoDB

In this lunch break, ArangoDB Head of Engineering Jörg Schad will explore a number of use cases suitable for Graph Analytics — and in particular leverage ArangoDB’s Graph Algorithms Library.

Graph Analytics with ArangoDB

O'Reilly: Graph Powered
Machine Learning
First Steps

Many powerful machine learning algorithms—including PageRank (Pregel), recommendation engines (collaborative filtering), and text summarization and other NLP tasks—are based on graphs. And there are even more applications once you consider data preprocessing and feature engineering, which are both vital tasks in machine learning pipelines.

O’Reilly Online Training

Graph Analytics

Many powerful Machine Learning algorithms are based on graphs, e.g., Page Rank (Pregel), Recommendation Engines (collaborative filtering), text summarization and other NLP tasks.

In this hands-on workshop, ArangoDB VP of Engineering Jörg Schad and Developer Relations Engineer Chris Woodward will explore a number of use cases suitable for Graph Analytics — and in particular leverage ArangoDB’s Graph Algorithms Library.

Slides and Excercises & Recording

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