The statistical AI algorithm to analyze communication networks

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Why Linkage ?

Understand your communication networks with the help of AI

Why Linkage Picture

Linkage allows you to cluster the nodes of networks with textual edges while identifying the topics used in communications. You can analyze networks such as email networks or co-authorship networks. Linkage allows you to upload your own network data or can help you build data to be analyzed.

What we do

What does make Linkage so unique ?

How does Linkage Work ?

Linkage is built upon a sound statistical AI model for networks with textual edges and implement an innovative and efficient AI algorithm to uncover patterns in your data. A model selection criteria allows to find in a fully automatic way the best model for your data.

Upload and manage your data securely

Upload all or part of your data on the platform to analyze them with Linkage. You will keep full control on the data you upload and only you will be able to access them.

Focus on data and interpretation

Minimum configuration is required to use Linkage since it selects the most sensible parameters for the data you provide. No scientific background is required to start working and get results. Advanced configuration options are also available if you need specific setups.

Visualize and export the results

Linkage also provides advanced visualization tools to present the uncovered results. It also allows you to export as CSV files the clustering results obtained on your data for further processing.

Linkage Technology

How does Linkage work ?

The statistical AI method behind the platform

The methodology implemented is partly related to an article published in the journal « Statistics and Computing ». The reference to cite in case of academic use of the platform is :

Bouveyron, C., Latouche, P., & Zreik, R. (2018). The stochastic topic block model for the clustering of vertices in networks with textual edges. Statistics and Computing, 28(1), 11-31.

Patent

Linkage technology has been patented in 2020 and is now protected in the US. You can access the official document here :

Method for clustering nodes of a textual network taking into account textual content, computer-readable storage device and system implementing said method. US Patent n°10671936 B2

Key people

The Linkage Team

  • Charles BOUVEYRON

    Charles is the co-inventor of the Linkage technology. He is a Professor of Statistics at Université Côte d'Azur (Nice) and holds a chair in Artificial Intelligence. He mainly works on statistical and machine learning for complexe data.

  • Pierre LATOUCHE

    Pierre is the co-inventor of the Linkage Technology. He is professor of Statistics at Université de Paris and Ecole Polytechnique (Paris). He works mainly on problems related to machine learning and statistics.

  • Carlos OCANTO DÁVILA

    Carlos is a research engineer. He works on the algorithmic aspects of Linkage.

  • Stéphane PETIOT

    Stéphane is a research engineer. He is in charge of the web & vizualisations development. He also works on general software problematics about Linkage.

Key People Picture

Resources

Our blog articles

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COVID-19

Linkage have been used in biology and epidemiology research fields, discover how in this article

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WeakData Picture

Weak signal detection challenge

Linkage allows you to detect weak signals in large datasets. Users can test this usecase on the CSV file that we provide in our demonstration datasets.

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CoAuthor Picture

How to analyze a co-authorship network

This simple tutorial helps you understand how you can use Linkage to analyse co-authorship networks in your field of application.

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