Software

• Computing Betweenness Centrality in Link Stream 

By Frédéric Simard, Clémence Magnien, Matthieu Latapy.  Source code in Python.
Please cite:  Computing Betweenness Centrality in Link Stream. Journal of Graph Algorithms and Applications 27:3, 2023

• Local PageRank via Chebyshev polynomials.

By Esteban Bautista Ruiz. Source code in Python.
Please cite: A local updating algorithm for Personalized PageRank via Chebyshev Polynomials. Social Network Analysis and Mining, 2022, vol. 12, no 1, p. 1-11.

• Computing diversity in Heterogeneous Information Networks (python version).

By Pedro Ramaciotti Morales. Source code in Python.
Please cite: Measuring diversity in heterogeneous information networks. Theoretical Computer Science, 2021, vol.859, p. 80-115.

• Computing diversity in Heterogeneous Information Networks (R version).

By Robin Lamarche-Perrin. Source code in R.
Please cite: Measuring diversity in heterogeneous information networks. Theoretical Computer Science, 2021, vol.859, p. 80-115.

• Bitcoin blockchain data analysis.

By Jules Azad Emery. Source code in Python.
Please cite: Full Bitcoin Blockchain Data Made Easy. ASONAM 2021.

• Efficient clique percolation method.

By Alexis Baudin. Source code in C.
Please cite: Clique percolation method: memory efficient almost exact communities. ADMA 2021.

• Gorder – node vertices reordering method for efficient cache management.

By Fabrice Lécuyer. Source code in C++.
Please cite: [Re] Speedup Graph Processing by Graph Ordering. ReScience C 7, 1 (3), 2021.

• Computing diversity in transaction logs.

By Alexandre Wilmet. Python library.
Please cite: Role of the Website Structure in the Diversity of Browsing Behaviors. Hypertext 2019.

• Link prediction in large graphs.

By Lionel Tabourier. Source code in OCaml.
Please cite: RankMerging: a supervised learning-to-rank framework to predict links in large social networks. Machine Learning Journal 2019.

• The stream graph library.

By Yannis Siglidis. Source code in Python.
Uses Distances and latencies in link streams, by Frédéric Simard; Closeness centrality in link streams, by Marwan Ghanem (see below); Maximal clique enumeration in link streams, by Tiphaine Viard (see below).

• Closeness centrality in link streams.

By Marwan Ghanem. Source code in C++.
Please cite: Centrality metrics in dynamic networks: a comparison study. IEEE Transactions on Network Science and Engineering, 2018.

• Maximal clique enumeration in link streams.

By Tiphaine Viard. Source code in Python.
Please cite: Enumerating maximal cliques in link streams with durations. Information Processing Letters, 2018.

• Compression of multidimensional data (with information loss).

By Robin Lamarche-Perrin. Source code in C++.
Please cite: An information-theoretic framework for the lossy compression of link streams. Theoretical Computer Science 2018.

• Enumerating all k-cliques in a graph.

By Maximilien Danisch. Source code in C.
Please cite: Listing k-cliques in Sparse Real-World Graphs. WWW 2018.

• Greedy graph compression.

By Léonard Panichi. Source code in C++.
Please cite: An information-theoretic framework for the lossy compression of link streams. Theoretical Computer Science 2018.

• Density friendly graph decomposition.

By Maximilien Danisch. Source code in C.
Please cite: Large Scale Density-friendly Graph Decomposition via Convex Programming. WWW 2017.

• UDP-ping and UDP-explore.

By Matthieu Latapy and Elie Rotenberg. Source code in C.
Please cite: Rigorous Measurement of the Internet Degree Distribution. Complex Systems 2017.

• Find heaviest k-subgraph.

By Maximilien Danisch. Source code in C.
Please cite: Finding Heaviest k-Subgraphs and Events in Social Media. ICDM Workshops 2016.

• Tracetree and Radar.

By Matthieu Latapy, Clémence Magnien and Frédéric Ouedraogo. Source code in C.
Please cite: A Radar for the Internet. Complex Systems 2011.

• Graph generation with k-edge switches.

By Lionel Tabourier. Source code in OCaml.
Please cite: Generating constrained random graphs using multiple edge switches. Journal of Experimental Algorithmics 2011.

• Compute the bounds of the diameter of a graph.

By Clémence Magnien and Matthieu Latapy. Source code in C.
Please cite: Fast Computation of Empirically Tight Bounds for the Diameter of Massive Graphs. Journal of Experimental Algorithmics 2009.

• Compute the number of triangles in a graph.

By Matthieu Latapy. Source code in C.
Please cite: Main-memory Triangle Computations for Very Large (Sparse (Power-Law)) Graphs. Theoretical Computer Science 2008.

• Basic analysis of bipartite networks.

By Matthieu Latapy. Source code in C.
Please cite: Basic Notions for the Analysis of Large Two-mode Networks. Social Networks 2008.

• Louvain community detection algorithm.

By Vincent Blondel, Jean-Loup Guillaume, Renaud Lambiotte and Etienne Lefebvre. Source code in C++.
Please cite: Fast unfolding of communities in large networks. Journal of Statistical Mechanics 2008.

• Paris traceroute (traceroute without load-balancing bias).

By Paris traceroute team. Source code in C++.
Please cite: Avoiding traceroute anomalies with Paris traceroute. SIGCOMM 2006.

• Walktrap community detection algorithm.

By Pascal Pons. Source code in C++.
Please cite: Computing communities in large networks using random walks. Journal of Graph Algorithms and Applications 2006.

• Generating Random Graphs with a Prescribed Degree Distribution.

By Fabien Viger. Source code in C++.
Please cite: Random generation of large connected simple graphs with prescribed degree distribution. COCOON 2005.