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Product Multicommodity Flow in Wireless Networks

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Learning Bollobas-Riordan Graphs Under Partial Observability

Ali H. Sayed, Michele Cirillo

This work examines the problem of learning the topology of a network (graph learning) from the signals produced at a subset of the network nodes (partial observability). This challenging problem was recently tackled assuming that the topology is drawn acco ...

IEEE2021

Graph Learning Under Partial Observability

Ali H. Sayed, Augusto José Rabelo Almeida Santos

Many optimization, inference, and learning tasks can be accomplished efficiently by means of decentralized processing algorithms where the network topology (i.e., the graph) plays a critical role in enabling the interactions among neighboring nodes. There ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

On The Choice of Graph Neural Network Architectures

Pascal Frossard, Guillermo Ortiz Jimenez, Clément Arthur Yvon Vignac

Seminal works on graph neural networks have primarily targeted semi-supervised node classification problems with few observed labels and high-dimensional signals. With the development of graph networks, this setup has become a de facto benchmark for a sign ...

2020

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

Actuator Placement for Optimizing Network Performance under Controllability Constraints

Maryam Kamgarpour, Tyler Summers, Baiwei Guo, Orcun Karaca

With the rising importance of large-scale network control, the problem of actuator placement has received increasing attention. Our goal in this paper is to find a set of actuators minimizing the metric that measures the average energy consumption of the c ...

IEEE2019

Filtering Random Graph Processes Over Random Time-Varying Graphs

Andreas Loukas

Graph filters play a key role in processing the graph spectra of signals supported on the vertices of a graph. However, despite their widespread use, graph filters have been analyzed only in the deterministic setting, ignoring the impact of stochasticity i ...

Institute of Electrical and Electronics Engineers2017

How to learn a graph from smooth signals

Vasilis Kalofolias

We propose a framework that learns the graph structure underlying a set of smooth signals. Given a real m by n matrix X whose rows reside on the vertices of an unknown graph, we learn the edge weights w under the smoothness assumption that trace(X^TLX) is ...

2016

On the energy benefit of compute-and-forward for multiple unicasts

Michael Christoph Gastpar, Jasper Goseling

Compute-and-forward (CF) is a technique which exploits broadcast and superposition in wireless networks. In this paper, the CF energy benefit is studied for networks with unicast sessions and modeled by connected graphs. This benefit is defined as the rati ...

Ieee2016

Node-weighted network design and maximum sub-determinants

Carsten Moldenhauer

We consider the Node-weighted Steiner Forest problem on planar graphs. Demaine et al. showed that a generic primal-dual algorithm gives a 6-approximation. We present two different proofs of an approximation factor of~

3

. Then, we draw a connection to Goem ...

EPFL2014

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

Node-weighted network design and maximum sub-determinants

Carsten Moldenhauer

3

. Then, we draw a connection to Goem ...

EPFL2014