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Data, Intelligence and Graphs (DIG) is a group of researchers at LTCI, Télécom ParisTech who study the fundamental issues raised in data and knowledge management systems, graph mining and artificial intelligence. Research interests cover theoretical foundations of data intelligence and graph systems, practical solutions and applications, as well as cognitive aspects.

DIG has strong academic and industrial collaborations:


Research

Knowledge Graphs

An ontology is a computer-processable collection of knowledge about the world. We are concerned with RDF Ontologies — i.e. with ontologies in the form of graphs. We construct and mine such ontologies.

Graph Mining

Graphs are a near-universal way to represent data. We are concerned with mining graphs for patterns and properties. Our particular focus is on the scalability of such approaches.

  • Logo of scikit-networkscikit-network: scikit-network is a Python package for the analysis of large graphs (clustering, embedding, ranking).
  • EUQLID: The EUQLID project investigates novel methods for managing and mining graph data.

The Social Web

The Web has evolved more and more into a social Web: content is produced and shared by users. In the DIG team, we follow and anticipate developments in this area.

  • Community detection: We are investigating means to detect and distinguish social communities on the Web.
  • Social Relations: We investigate the optimal investment in social relations from a theoretical point of view.

Language and relevance

Computer science is not just about computers. In this area of research, we investigate how humans reason, and what this implies for machines.

  • Simplicity Theory: Simplicity theory seeks to explain the relevance of situations or events to human minds. See http://www.simplicitytheory.science
  • Relevance in natural language: The point is to retro-engineer methods to achieve meaningful and relevant speech from our understanding of human performance. Read this paper. Read more on this.
  • Communication as social signalling: We apply game theory and social simulation to explore conditions in which providing valuable (i.e. relevant) information is a profitable strategy. Read this paper. Read more on this.

Machine Learning for IoT Data Streams

We investigate how to do machine learning in real time using Big Data, contributing to new open source tools:

  • MOA: Massive Online Analytics, the most popular framework for mining data streams, implemented in Java.
  • Apache SAMOA: Scalable Advanced Massive Online Analytics, an open source framework for data stream mining on the Hadoop Ecosystem.

Big Data & Market Insights

We focus in this project on Big data management and mining and their applications in digital marketing.

  • Scalability of the algorithms on large sets of real data
  • Context-aware recommender systems and predictive models: hotel booking, travel recommandation, Points of Interest …
  • Social networks analysis and web information extraction: community detection, centrality, engagement rate …

People

 

Talel Abdessalem Antoine Amarilli Albert Bifet Thomas Bonald Laurent Decreusefond
Jean-Louis Dessalles Pierre Senellart Mauro Sozio Fabian M. Suchanek

Senior

Post-docs

  • Marie Al-Ghossein
  • Pierre-Alexandre Murena.

PhD candidates

Interns

  • Nader Beltaief. Advisor: L. Decreusefond.
  • Armand Boschin. Advisor: Thomas Bonald.
  • Cyril Chhun. Advisor: Jean-Louis Dessalles.
  • Rémi Dupré. Advisors: Antoine Amarilli and Pierre Senellart.
  • Lucas Gréaux. Advisor: Mauro Sozio.
  • Étienne Houzé. Advisor: Jean-Louis Dessalles.
  • Anes Mekki. Advisor: Antoine Amarilli.
  • Julien Panie-Lie. Advisor: Jean-Louis Dessalles.
  • Minh Huong Le NguyenAdvisor: Albert Bifet
  • Wenbin ZhangAdvisor: Albert Bifet
  • Natalia Mordvanyuk.  Advisor: Albert Bifet

Former members

News

Pierre-Alexandre Murena’s PhD Thesis honoured

Pierre-Alexandre Murena‘s thesis: Minimum Complexity Principle for Knowledge Transfer in Artificial Learning (under the supervision of Antoine Cornuéjols and Jean-Louis Dessalles) got the 2nd prize of the best IMT thesis. Read on the IMT page (in French) See Pierre-Alexandre’s video presentation (in English) Read the thesis Congratulations to Pierre-Alexandre!

Open Associate Professor position in Scalable Artificial Intelligence in Paris

The DIG team is opening an Associate Professor position in Scalable Artificial Intelligence at LTCI, Télécom ParisTech in Paris. More information: here University: Télécom ParisTech, https://telecom-paristech.fr/ Location: Palaiseau, near Paris, France Position: Associate Professor (“Maître de conférences”), tenured permanent position Application deadline: Friday, March 15, 2019 Starting date: September 2019 Team: Data Intelligence and Graphs (DIG, https://dig.telecom-paristech.fr/) …

New book “Des intelligences très artificielles” by Jean-Louis Dessalles

Book Website Jean-Louis Dessalles 30 janvier 2019 204 pages EAN13 : 9782738147141 145 x 220 mm L’« IA » fait de plus en plus souvent la une des médias. Les mystérieux algorithmes de nos ordinateurs sont champions du monde d’échecs et de go, ils vont conduire nos voitures, traduire automatiquement en n’importe quelle langue, voire …

Contact

Télécom ParisTech
46 Rue Barrault, 75013 Paris
thomas.bonald@telecom-paristech.fr


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