Elise Devaux

About Me

Elise Devaux is Marketing Project Manager at chez Linkurious SAS, which provided the data analytics software behind the Paradise Papers, to help cover the emerging graph technology use cases. Prior to Linkurious, she worked in the humanoid robotics industry to support and foster Softbank Robotics developer community.

How Big Data Technology is Transforming Fraud Investigations

When it comes to big data, challenges derive from the nature and the volume of the data. Whether it is a data leak or a financial company’s internal data, the amount of data we are dealing with is considerable. To complicate things, investigations usually start from raw, unstructured data. And it’s impossible to automate or scale the investigation without a predefined-data model or any kind of organizational logic.

Three Types of Fraud Graph Analytics Can Help Defeat

Organizations across industries are adopting graph analytics to reinforce their anti-fraud programs. Most anti-fraud applications are able to connect simple data points together to detect suspicious behavior. But these applications fall short on more complex analysis. The graph databases we’ve seen emerge in the recent years are designed for this purpose. In this post, we examine three types of fraud graph analytics that can help investigators combat insurance fraud, credit card fraud, and VAT fraud.

The list of graph visualization libraries

By helping surface information, visualization tools create a bridge between graph data and viewers. Graph libraries are an important layer of the graph technology landscape. They let you build custom visualization application for network data and you can pick from a large catalog depending on your favorite language, license requirement, budget or project needs. These are mainly JavaScript libraries but you’ll also find other languages. This post lists libraries of various ‘depth’, some offering basic layouts and interaction, other more advanced customization and integration options.

The GraphTech Ecosystem 2019 - Part 1: Graph Databases

The market shares of graph databases keep increasing, as well as the number of products on the market, with seven times more vendors than 5 years ago. Although it seems difficult to agree on exact figures, all reports identify the same growth drivers. In this article, I present the current market, if not exhaustively, at least as well as possible. I divided the graph ecosystem into three main layers, even though the reality is more complex and these stratum are often permeable.

The GraphTech Ecosystem 2019 - Part 2: Graph Analytics

The field of graph theory has spawned multiple algorithms on which analysts can rely on to find insights hidden in graph data.  This article covers the graph analytics landscape. Graph analytics, or computing, frameworks. They consist of a set of tools and methods developed to extract knowledge from data modeled as a graph. They are crucial for many applications because processing large datasets of complex connected data is computationally challenging.

The GraphTech Ecosystem 2019 - Part 3: Graph visualization

Visualization tools represent an important bridge between graph data and analysts. It helps surface information and insights leading to the understanding of a situation, or the solving of a problem. Graph visualization tools turn connected data into graphical network representations that take advantage of the human brain proficiency to recognize visual patterns and more pattern variations. Graph visualization brings many advantages to the analysis of graph data. When you apply visualization methods to data analysis, you are more likely to cut the time spent looking for information.

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