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Fast Approximate Spectral Clustering for Dynamic Networks

Publications associées (32)

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Sublinear Algorithms for Spectral Graph Clustering

Aidasadat Mousavifar

This thesis focuses on designing spectral tools for graph clustering in sublinear time. With the emergence of big data, many traditional polynomial time, and even linear time algorithms have become prohibitively expensive. Processing modern datasets requir ...

EPFL2021

Local approximation of the Maximum Cut in regular graphs

Etienne Michel François Bamas

This paper is devoted to the distributed complexity of finding an approximation of the maximum cut (MAXCUT) in graphs. A classical algorithm consists in letting each vertex choose its side of the cut uniformly at random. This does not require any communica ...

ELSEVIER2020

Fast and Space Efficient Spectral Sparsification in Dynamic Streams

Mikhail Kapralov, Jakab Tardos, Navid Nouri, Aidasadat Mousavifar

In this paper, we resolve the complexity problem of spectral graph sparcification in dynamic streams up to polylogarithmic factors. Using a linear sketch we design a streaming algorithm that uses (O) over tilde (n) space, and with high probability, recover ...

ASSOC COMPUTING MACHINERY2020

On the Experimental Transferability of Spectral Graph Convolutional Networks

Spectral Graph Convolutional Networks (GCNs) are generalisations of standard convolutional for graph-structured data using the Laplacian operator. Recent work has shown that spectral GCNs have an intrinsic transferability. This work verifies this by studyi ...

2020

Local Approximation of the Maximum Cut in Regular Graphs

Etienne Michel François Bamas

This paper is devoted to the distributed complexity of finding an approximation of the maximum cut in graphs. A classical algorithm consists in letting each vertex choose its side of the cut uniformly at random. This does not require any communication and ...

SPRINGER INTERNATIONAL PUBLISHING AG2019

New Graph Algorithms via Polyhedral Techniques

Jakub Tarnawski

In this thesis we give new algorithms for two fundamental graph problems. We develop novel ways of using linear programming formulations, even exponential-sized ones, to extract structure from problem instances and to guide algorithms in making progress. S ...

EPFL2019

String Matching: Communication, Circuits, and Learning

Mika Tapani Göös

String matching is the problem of deciding whether a given n-bit string contains a given k-bit pattern. We study the complexity of this problem in three settings. - Communication complexity. For small k, we provide near-optimal upper and lower bounds on th ...

Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH, Wadern/Saarbruecken, Germany2019

Robust and Efficient Data Clustering with Signal Processing on Graphs

Lionel Jérémie Martin

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 ...

EPFL2018

Novel Algorithms For Clustering

James Peter Newling

Clustering is a method for discovering structure in data, widely used across many scientific disciplines. The two main clustering problems this dissertation considers are K-means and K-medoids. These are NP-hard problems in the number of samples and cluste ...

EPFL2018

The Sketching Complexity of Graph and Hypergraph Counting

Mikhail Kapralov, John Michael Goddard Kallaugher

Subgraph counting is a fundamental primitive in graph processing, with applications in social network analysis (e.g., estimating the clustering coefficient of a graph), database processing and other areas. The space complexity of subgraph counting has been ...

IEEE COMPUTER SOC2018