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Representing graphs through data with learning and optimal transport

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Downsampling of Signals on Graphs Via Maximum Spanning Trees

Quy Ha Nguyen, Minh Do

Downsampling of signals living on a general weighted graph is not as trivial as of regular signals where we can simply keep every other samples. In this paper we propose a simple, yet effective downsampling scheme in which the underlying graph is approxima ...

Ieee-Inst Electrical Electronics Engineers Inc2015

Robust Principal Component Analysis on Graphs

Pierre Vandergheynst, Michael Bronstein, Xavier Bresson, Nauman Shahid, Vasilis Kalofolias

Principal Component Analysis (PCA) is the most widely used tool for linear dimensionality reduction and clustering. Still it is highly sensitive to outliers and does not scale well with respect to the number of data samples. Robust PCA solves the first iss ...

2015

Orchestration Graphs

Pierre Dillenbourg

By modeling pedagogical scenarios as directed geometrical graphs and proposing an associated modeling language, this book describes how rich learning activities, often designed for small classes, can be scaled up for use with thousands of participants. Wit ...

EPFL Press2015

From Compact Semi-Toric Systems To Hamiltonian S-1-Spaces

Silvia Sabatini

We show how any labeled convex polygon associated to a compact semi-toric system, as de fined by V (u) over tilde Ngoc, determines Karshon's labeled directed graph which classifies the underlying Hamiltonian S-1-space up to isomorphism. Then we characteriz ...

Amer Inst Mathematical Sciences2015

Motivic Donaldson-Thomas invariants for the one-loop quiver with potential

We compute the motivic Donaldson-Thomas invariants of the one-loop quiver, with an arbitrary potential. This is the first computation of motivic Donaldson-Thomas invariants to use in an essential way the full machinery of (mu) over cap -equivariant motives ...

Geometry & Topology Publications2015

Scale-up Graph Processing in the Cloud: Challenges and Solutions

Willy Zwaenepoel, Amitabha Roy, Jasmina Malicevic

Processing large graphs is an important part of the big-data problem. Recently a number of scale-up systems such as X-Stream, Graphchi and Turbograph have been proposed for processing large graphs using secondary storage on a single machine. The design and ...

PrefEdge: SSD Prefetcher for Large-Scale Graph Traversal

Amitabha Roy

Mining large graphs has now become an important aspect of multiple diverse applications and a number of computer systems have been proposed to provide runtime support. Recent interest in this area has led to the construction of single machine graph computa ...

2014

Bootstrap Percolation in Power-Law Random Graphs

Léo Hamed Amini

A bootstrap percolation process on a graph is an "infection" process which evolves in rounds. Initially, there is a subset of infected nodes and in each subsequent round each uninfected node which has at least infected neighbours becomes infected and remai ...

Springer2014

Graph-based Image Inpainting

Michaël Defferrard

The project goal was to explore the applications of spectral graph theory to address the inpainting problem of large missing chunks. We used a non-local patch graph representation of the image and proposed a structure detector which leverages the graph rep ...

2014

A Bayesian Method for Matching Two Similar Graphs without Seeds

Matthias Grossglauser, Pedram Pedarsani

Approximate graph matching (AGM) refers to the problem of mapping the vertices of two structurally similar graphs, which has applications in social networks, computer vision, chemistry, and biology. Given its computational cost, AGM has mostly been limited ...

2013