Tag Archives: social networks

Papers

Unfolding ego-centered community structures with “a similarity approach”

Maximilien Danisch, Jean-Loup Guillaume and Bénédicte Le Grand

We propose a framework to unfold the ego-centered community structure of a given node in a network. The framework is not based on the optimization of a quality function, but on the study of the irregularity of the decrease of a similarity measure. It is a practical use of the notion of multi-ego-centered community and we validate the pertinence of the approach on a real-world network of wikipedia pages.

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Papers

A Real-World Spreading Experiment in the Blogosphere

Adrien Friggeri, Jean-Philippe Cointet and Matthieu Latapy

We designed an experiment to observe a spreading phenomenon in the blogosphere. This experiment relies on a small applet that participants copy on their own web page. We present the obtained dataset, which we freely provide for study, and conduct basic analysis. We conclude that, despite the classical assumption, in this experiment famous blogs do not necessarily act as super spreaders.
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Plots

Following and Followers in Twitter

Following and Followers in Twitter

> By Guillaume Valadon and Korlam Gautam Twitter is a micro-blogging platform that allows its users to post short messages (aka Tweets) to be displayed on their profile pages. A social network is also embedded in Twitter: users can receive messages from users they follow. In the Twitter terminology, there are following and followers. Given […]

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Plots

Social networks conceptual analysis

Social networks conceptual analysis

> By Bénédicte Le Grand These figures represent Galois lattices built from two samples of online social networks (Myspace and DailyMotion). Galois lattices, from Formal Concept Analysis, cluster data (here the members of a social network sample) according to their common properties (here the members’ contacts). Each node of the lattice is called a concept; […]

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Plots

Direct and indirect influence of blogs

Direct and indirect influence of blogs

> By Matthieu Latapy, Jean-Philippe Cointet and Adrien Friggeri The study of information diffusion among individuals in a social network is a challenging task as there are different possible definitions for a given concept. Consider for example a blogger who publishes a piece of information on his website; there are mainly two ways of quantifying […]

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Papers

Empreintes conceptuelles et spatiales pour la caractérisation des réseaux sociaux

Bénédicte Le Grand, Marie-Aude Aufaure et Michel Soto

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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Plots

Size distribution of communities at different scales

Size distribution of communities at different scales

> By Thomas Aynaud and Jean-Loup Guillaume When you try to detect communities in a complex network, you often build a hierarchical decomposition of the nodes. This decomposition is a tree (called a dendrogram). The leaves of the tree are the nodes, and each of its levels defines a partition: two nodes of the graph […]

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Papers

Evolving networks

Pierre Borgnat, Eric Fleury, Jean-Loup Guillaume, Clémence Magnien, Céline Robardet et Antoine Scherrer

Most real networks often evolve through time: changes of topology can occur if some nodes and/or edges appear and/or disappear, and the types or weights of nodes and edges can also change even if the topology stays static. Mobile devices with wireless capabilities (mobile phones, laptops, etc.) are a typical example of evolving networks where nodes or users are spread in the environment and connections between users can only occur if they are near each other. This whois- near-whom network evolves every time users move and communication services (such as the spread of any information) will deeply rely on the mobility and on the characteristics of the underlying network. This paper presents some recent results concerning the characterization of the dynamics of complex networks through three different angles: evolution of some parameters on snapshots of the network, parameters describing the evolution itself, and intermediate approaches consisting in the study of specific phenomena or users of interest through time.

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