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Interaction Data Lab

Research team at CRI Paris

About us

We use network science and data analysis to decipher collective phenomena at biological and social scales. In particular, we study collaborative learning and solving using network approaches on large empirical datasets, with the end goal to develop tools fostering collective intelligence for social impact.

Our projects

We leverage the power of large datasets to tackle interdisciplinary projects from various fields: social network analyses of scientific teams, dynamics of Twitter communities, collaborative learning using phone call records,  dynamics of contribution and collaborations in open-source communities on GitHub, evolution of scientific fields and trajectories of scientists across knowledge domains, design of recommender systems for open science projects, or perturbation spread in technological and biological networks.


News

Excellent Thesis Award

We are glad to announce that our Master’s student intern Stephanie Chuah Shin Ju from last summer was awarded the ‘Excellent Thesis Award’ by the Tsinghua University’s School of Public Policy and Management for her thesis “The Online Transition of the Geneva-Tsinghua Initiative SDG Summer School“. She is also in the running for the university-wideContinue reading “Excellent Thesis Award”

Welcome to the Interaction Data Lab

This is our first virtual steps as a team website! Please have a look around to meet the team, see our projects, and enjoy some network art in the virtual gallery!

Network seminars!

Since December 2018, we are organizing along with Liubov Tupikina a network seminar series at CRI Paris. Talks showcase the use of network science in a wide range of disciplines, from physics to mathematics, to archaeology and biology, to social sciences or neurosciences, and are intended for a broad, interdisciplinary audience.

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Luca Aiello – Ten Dimensions of Social Relationships

Massimo Stella – Cognitive network science
Michele Starnini – Emergence of echo chambers and polarization dynamics in social networks

Journal clubs on open communities and recommender systems

These talks have been organized with the Just One Giant Lab (JOGL) open science community to improve our understanding of crowd science and how to enhance collective intelligence using recommender systems on online platforms.

Recommender Systems: Classification, Evaluation | Pedro Ramaciotti Morales
Designing human systems for efficient social interactions | Thomas Maillart
Organization of Crowd Science | Robbie Ward

Intelligent recommendation in Citizen Science | Naama Dayan

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