Thibaut Chataing

Thomas Maillart, Thibaut Chataing, Ntorina Antoni et al.

We introduce an explainable machine-learning approach that forecasts the structural precursors of scientific breakthroughs -- the emergence and intensification of links between research concepts -- by modelling how OpenAlex concept networks evolve over time. Using 59 semantic and topological features, a two-stage LightGBM model jointly predicts the formation and the future weight of concept pairs, adding a regression stage that quantifies expected intensity to prior link-existence forecasts. Relative to the state of the art, the approach improves accuracy and explainability at once: comparative validation across four technology and biomedical domains yields ROC-AUC in [0.954, 0.967] at all horizons without re-tuning, exceeding the roughly 0.90 of prior models, while every forecast rests on structural, auditable features rather than opaque embeddings. Classification performance is high (AUC about 0.95) and regression remains stable (RMSLE 0.45 to 0.6 over one to five years). Feature attribution shows that structural factors -- particularly Adamic-Adar similarity and degree-based Hadamard measures -- consistently drive accuracy, suggesting that breakthrough-relevant recombinations emerge in tightly connected sub-networks. Two expert-anchored cases, quantum annealing and AI-enabled quantum architectures, show the model surfacing technological convergence consistent with expert expectations. We then outline a three-layer decision architecture -- detection, expert translation, institutional integration -- that turns these forecasts into evidence-based research strategy and policy, anchored in open data and explainable features.

Thomas Maillart, Thibaut Chataing, David Dosu et al.

Understanding and anticipating scientific change requires models that distinguish between endogenous consolidation and exogenous diffusion of scientific concepts. Using the quantum computing subtree of concepts in OpenAlex, we construct a temporally resolved concept co-occurrence network and track each concept pair through its upstream citation lineage and downstream diffusion. We train LightGBM models on distributional and diversity-aware features to predict four outcomes: endogenous reinforcement, exogenous diffusion, their ratio, and diffusion entropy. After controlling for overall publication growth of the scientific body, endogenous reinforcement proves largely unpredictable in the primary quantum-computing benchmark. In contrast, exogenous diffusion and entropy are strongly predictable ($R^2$ up to $0.78à) and are driven by upstream heterogeneity, citation breadth, and distributional dispersion, as shown by SHAP analyses; replications on robotics, advanced materials, and neuro implants confirm that exogenous diffusion remains the top-ranked target across fields ($R^2_test \sim 0.60-0.87$), while endogenous predictability rises markedly in neuro implants (R^2_test = 0.83), indicating that the quantum-computing asymmetry does not generalise uniformly. Case studies reveal that sharp entropy increases coincide with the opening of new conceptual frontiers, while entropy collapses signal technological convergence or paradigm displacement. These results demonstrate that conceptual diffusion is governed by stable structural regularities embedded in semantic and citation environments. By identifying early diversity-based signals of cross-domain uptake, the approach provides a scalable foundation for anticipatory scientometrics, technology foresight, and innovation-oriented policy analysis in rapidly evolving research fields.

Lucia Gomez Tejeiro, Thibaut Chataing, Julian Jang-Jaccard et al.

We measure cross-boundary collaboration in an open-source software (OSS) ecosystem by reconstructing the bipartite contributor-repository graph of 464 cybersecurity projects and 11,372 contributors active over October 2001-May 2022 (Rawsec Cybersecurity Inventory). Louvain community detection identifies 163 non-singleton communities; per-community contributor count scales superlinearly with repository count (n_contributors ~ n_repos^1.4), and community formation follows a logistic trajectory saturating around 2018. Three patterns support a recognition/repeat-relationship account of cross-boundary work. First, cross-community work concentrates in a thin carrier layer: only nine canonical humans span seven or more communities at the commit level, authoring 14% of 4,015 inter-community merged pull requests; the top 50 cross-community contributors produce 54%. Second, boundary friction is a recognition cost, not a fixed boundary property: inter-community pull-request acceptance rises from 42% at breadth k=1 to 87% at k=5-9, with median latency compressing from 147 h to 49 h. Third, community survival is cohort-structured: per-cohort residualisation hazard rises an order of magnitude between pre-2010 and 2018 cohorts, and external community reach predicts survival mainly through size, leaving late cohorts under-served despite a stable carrier layer. The corpus predates mainstream LLM coding assistants; this baseline of carrier-layer thinness, friction gradient, and cohort hazard informs debates on social coding as a template for digital societies and on what AI-mediated OSS ecosystems should not optimise away.