Publication
Network-based kinetic models: Emergence of a statistical description of the graph topology
Related publications (33)
Spikyball Sampling: Exploring Large Networks via an Inhomogeneous Filtered Diffusion
Nicolas Aspert, Volodymyr Miz, Benjamin Ricaud
Studying real-world networks such as social networks or web networks is a challenge. These networks often combine a complex, highly connected structure together with a large size. We propose a new approach for large scale networks that is able to automatic ...
2020Who Started This Rumor? Quantifying the Natural Differential Privacy of Gossip Protocols
Rachid Guerraoui, Hadrien Hendrikx
Gossip protocols (also called rumor spreading or epidemic protocols) are widely used to disseminate information in massive peer-to-peer networks. These protocols are often claimed to guarantee privacy because of the uncertainty they introduce on the node t ...
Schloss Dagstuhl, Leibniz-Zentrum2020Statistical Physics Methods for Community Detection
This thesis is devoted to information-theoretic aspects of community detection. The importance of community detection is due to the massive amount of scientific data today that describes relationships between items from a network, e.g., a social network. I ...
EPFL2019Analysis of a Canonical Labeling Algorithm for the Alignment of Correlated Erdős-Rényi Graphs
Matthias Grossglauser, Negar Kiyavash, Osman Emre Dai
Graph alignment in two correlated random graphs refers to the task of identifying the correspondence between vertex sets of the graphs. Recent results have characterized the exact information-theoretic threshold for graph alignment in correlated Erdös-Rény ...
Association for Computing Machinery (ACM)2019Efficient large-scale graph processing: optimisations for storage, performance and evolving graphs
Graph processing systems are used in a wide variety of fields, ranging from biology to social networks.
Algorithms to mine graphs incur many random accesses, and the sparse nature of the graphs of interest, exacerbates this. As DRAM sustains high bandwidt ...
EPFL2019Time-resolved analysis of dynamic graphs: an extended Slepian design
Dimitri Nestor Alice Van De Ville, Raphaël Pierre Liégeois, Ibrahim Merad
Graphs are extensively used to represent networked data. In many applications, especially when considering large datasets, it is a desirable feature to focus the analysis onto specific subgraphs of interest. Slepian theory and its extension to graphs allow ...
SPIE-INT SOC OPTICAL ENGINEERING2019Adaptive Majority Problems For Restricted Query Graphs And For Weighted Sets
Abhishek Methuku, Balázs Keszegh
Suppose that the vertices of a graph G are colored with two colors in an unknown way. The color that occurs on more than half of the vertices is called the majority color (if it exists), and any vertex of this color is called a majority vertex. We study th ...
COMENIUS UNIV2019New Notions and Constructions of Sparsification for Graphs and Hypergraphs
A sparsifier of a graph G (Bencztir and Karger; Spielman and Teng) is a sparse weighted subgraph (G) over tilde that approximately retains the same cut structure of G. For general graphs, non-trivial sparsification is possible only by using weighted graphs ...
IEEE COMPUTER SOC2019Robust and Efficient Data Clustering with Signal Processing on Graphs
Data is pervasive in today's world and has actually been for quite some time. With the increasing volume of data to process, there is a need for faster and at least as accurate techniques than what we already have. In particular, the last decade recorded t ...
EPFL2018Stationary signal processing on graphs
Pierre Vandergheynst, Nathanaël Perraudin
Graphs are a central tool in machine learning and information processing as they allow to conveniently capture the structure of complex datasets. In this context, it is of high importance to develop flexible models of signals defined over graphs or network ...
Institute of Electrical and Electronics Engineers2017