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Empreintes conceptuelles et spatiales pour la caractérisation des réseaux sociaux

Bénédicte Le Grand, Marie-Aude Aufaure et Michel Soto

Conférence EGC 2009 (Extraction et Gestion des Connaissances), Strasbourg, France, 27-30 janvier 2009

In this paper, Formal Concept Analysis and Galois lattices are used for the analysis of complex datasets, online social networks in particular. Lattice-inspired statistics computed on the objects of the lattice provide their « conceptual distribution ». An experimentation conducted on four social networks’ samples shows how these statistics may be used to characterize these networks and filter them automatically.

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Main-memory Triangle Computations for Very Large (Sparse (Power-Law)) Graphs

Matthieu Latapy

Theoretical Computer Science (TCS) 407 (1-3), pages 458-473, 2008

Finding, counting and/or listing triangles (three vertices with three edges) in massive graphs are natural fundamental problems, which received recently much attention because of their importance in complex network analysis. We provide here a detailed survey of proposed main-memory solutions to these problems, in an unified way. We note that previous authors paid surprisingly little attention to space complexity of main-memory solutions, despite its both fundamental and practical interest. We therefore detail space complexities of known algorithms and discuss their implications. We also present new algorithms which are time optimal for triangle listing and beats previous algorithms concerning space needs. They have the additional advantage of performing better on power-law graphs, which we also detail. We finally show with an experimental study that these two algorithms perform very well in practice, allowing to handle cases which were previously out of reach.

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A Radar for the Internet

Matthieu Latapy, Clémence Magnien and Frédéric Ouédraogo

Proceedings of ADN’08: 1st International Workshop on Analysis of Dynamic Networks, in conjunction with IEEE ICDM 2008

In contrast with most internet topology measurement research, our concern here is not to obtain a map as complete and precise as possible of the whole internet. Instead, we claim that each machine’s view of this topology, which we call ego-centered view, is an object worth of study in itself. We design and implement an ego-centered measurement tool, and perform radar-like measurements consisting of repeated measurements of such views of the internet topology. We conduct long-term (several weeks) and high-speed (one round every few minutes) measurements of this kind from more than one hundred monitors, and we provide the obtained data. We also show that these data may be used to detect events in the dynamics of internet topology.

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