Matthieu Latapy

Matthieu Latapy is a French computer scientist and network scientist known for contributions to complex networks, graph algorithms, and social network analysis. He has held research and teaching positions at French institutions and contributed widely cited methods and datasets that influenced research in graph theory, social network, distributed computing, and data mining. His work connects theoretical foundations from discrete mathematics to practical analyses used across computer science subfields.

Early life and education

Latapy was educated in France, attending the École Normale Supérieure and completing advanced degrees at institutions including Université Pierre et Marie Curie and research training associated with laboratories such as LIP6 and laboratories under the Centre national de la recherche scientifique. During his formative years he engaged with research communities connected to conferences and organizations like STACS, ICALP, SIAM, ACM SIGCOMM and collaborated with researchers affiliated with universities such as Université Paris Diderot, École Polytechnique, INRIA, and international centers including Los Alamos National Laboratory and Microsoft Research.

Academic career and positions

Latapy has been a researcher within the CNRS and a faculty member at institutions including Université Paris Diderot and associated research units such as LIP6 and UMR teams collaborating with INRIA and other French laboratories. He has served on program committees for venues like ACM SIGCOMM, IEEE INFOCOM, ESA, STOC, and SODA, and contributed to editorial boards of journals tied to publishers such as Springer, ACM, IEEE, and Elsevier. His collaborations span researchers from institutions such as École Normale Supérieure de Lyon, Université de Grenoble Alpes, EPFL, University of California, Berkeley, University of Oxford, and University College London.

Research contributions and notable results

Latapy developed and popularized efficient algorithms and metrics for the analysis of large-scale networks, including contributions to measures related to clustering, transitivity, and sampling. He proposed methods for computing exact and approximate values of clustering coefficients and introduced approaches to handle bipartite structures, motifs, and cores that have been applied in studies involving datasets from platforms like Facebook, Twitter, LiveJournal, YouTube, and measurement projects associated with CAIDA and RIPE NCC. His work on triangle enumeration, k-core decomposition, and community detection influenced algorithmic practice used in contexts such as network science studies of the World Wide Web, citation network analyses involving arXiv, DBLP, and empirical studies of peer-to-peer systems linked to research on Gnutella. He produced foundational datasets and preprocessing techniques used by researchers at institutions such as Stanford University, Massachusetts Institute of Technology, Carnegie Mellon University, Princeton University, and ETH Zurich.

Latapy's theoretical contributions include complexity analyses and proofs concerning graph sparsification, sampling bias, and limits of estimators used in network measurement, engaging with theoretical frameworks from combinatorics and probability theory and practical implications for applied work in data mining and machine learning as deployed at labs like Google Research and Facebook AI Research. He also contributed to the study of dynamic networks, temporal motifs, and link prediction evaluated in benchmarks produced by groups at Cornell University and University of Illinois Urbana–Champaign.

Awards and honors

Latapy's work has been recognized through citations, invited talks at major conferences such as WWW, KDD, NeurIPS, ICML, and honors within French research institutions including distinctions from CNRS units and university accolades. He has been an invited keynote or plenary speaker at workshops and symposia organized by entities like IEEE, ACM, SIAM, and European networks such as ERC-related events and national competitiveness clusters.

Selected publications and impact

Representative publications include widely cited articles on clustering coefficients, triangle counting, k-core decomposition, and bipartite graph analysis appearing in proceedings of ACM SIGMOD, IEEE/ACM Transactions, SIAM Journal on Computing, and conference series like LATIN, ESA, and FAW. His datasets and algorithms are referenced in textbooks and surveys on network analysis and have been used in applied research at organizations including IBM Research, AT&T Research, Cisco Systems, LinkedIn, and academic groups at Harvard University and Yale University. The methodological advances he introduced underpin tools and libraries developed in ecosystems such as NetworkX, igraph, and software used in reproducible studies promoted by initiatives like Open Science and data repositories at universities such as University of Cambridge.

Category:French computer scientists Category:Network scientists