.. image:: pystat.png :height: 20 :alt: Statistique :target: http://www.xavierdupre.fr/app/ensae_teaching_cs/helpsphinx/td_2a_notions.html#pour-un-profil-plutot-data-scientist .. _l-cluster-graph: Clustering de Graphes +++++++++++++++++++++ *(à venir)* *Lectures* * `Basic models and questions in statistical network analysis `_ * `Trinity: A Distributed Graph Engine on a Memory Cloud `_ * `Dimensionality Reduction for Spectral Clustering `_ * `Compressive Spectral Clustering `_ * `Spectral Clustering on a Budget `_ * `Partitioning Well-Clustered Graphs: Spectral Clustering Works! `_ * `Bipartite Correlation Clustering: Maximizing Agreements `_ * `Correlation Clustering and Biclustering with Locally Bounded Errors `_ * `A Unified Framework for Model-based Clustering `_ * `A Tensor Approach to Learning Mixed Membership Community Models `_ * `Clustering from General Pairwise Observations with Applications to Time-varying Graphs `_ * `A Spectral Algorithm with Additive Clustering for the Recovery of Overlapping Communities in Networks `_ * `A Unified Framework for Structured Graph Learning via Spectral Constraints `_ * `Path-Based Spectral Clustering: Guarantees, Robustness to Outliers, and Fast Algorithms `_ *Lectures - métriques* * `Social Clicks: What and Who Gets Read on Twitter? `_ *Lectures Ranking* * `CoSimRank `_ * `PageRank `_ * `A Local Spectral Method for Graphs: With Applications to Improving Graph Partitions and Exploring Data Graphs Locally `_ *Modules* * `networkx `_ * `neo4j `_, `py2neo `_, `neo4j-python-driver `_ * `snap.py `_ * `GraKeL `_ : algorithmes sur les graphes, plus court chemin, marches aléatoires, méthodes à noyaux * `scikit-network `_ : de nombreux algorithmes comme PageRank, Louvain, ...