Andreas Loukas

According to our database1, Andreas Loukas authored at least 49 papers between 2008 and 2020.

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Bibliography

2020
Dynamic Balanced Graph Partitioning.
SIAM J. Discret. Math., 2020

Multi-Head Attention: Collaborate Instead of Concatenate.
CoRR, 2020

Building powerful and equivariant graph neural networks with message-passing.
CoRR, 2020

Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs.
CoRR, 2020

How hard is graph isomorphism for graph neural networks?
CoRR, 2020

What graph neural networks cannot learn: depth vs width.
Proceedings of the 8th International Conference on Learning Representations, 2020

On the Relationship between Self-Attention and Convolutional Layers.
Proceedings of the 8th International Conference on Learning Representations, 2020

Graph Coarsening with Preserved Spectral Properties.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Forecasting Time Series With VARMA Recursions on Graphs.
IEEE Trans. Signal Process., 2019

Graph Reduction with Spectral and Cut Guarantees.
J. Mach. Learn. Res., 2019

Stationary time-vertex signal processing.
EURASIP J. Adv. Signal Process., 2019

Discriminative structural graph classification.
CoRR, 2019

Some limitations of norm based generalization bounds in deep neural networks.
CoRR, 2019

Approximating Spectral Clustering via Sampling: a Review.
CoRR, 2019

Extrapolating Paths with Graph Neural Networks.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

2018
A Time-Vertex Signal Processing Framework: Scalable Processing and Meaningful Representations for Time-Series on Graphs.
IEEE Trans. Signal Process., 2018

<i>rDAN</i>: Toward robust demand-aware network designs.
Inf. Process. Lett., 2018

Graph reduction by local variation.
CoRR, 2018

Fast Approximate Spectral Clustering for Dynamic Networks.
Proceedings of the 35th International Conference on Machine Learning, 2018

Spectrally Approximating Large Graphs with Smaller Graphs.
Proceedings of the 35th International Conference on Machine Learning, 2018

2017
Filtering Random Graph Processes Over Random Time-Varying Graphs.
IEEE Trans. Signal Process., 2017

Autoregressive Moving Average Graph Filtering.
IEEE Trans. Signal Process., 2017

A Time-Vertex Signal Processing Framework.
CoRR, 2017

Towards Communication-Aware Robust Topologies.
CoRR, 2017

How Close Are the Eigenvectors of the Sample and Actual Covariance Matrices?
Proceedings of the 34th International Conference on Machine Learning, 2017

Spinner: Scalable Graph Partitioning in the Cloud.
Proceedings of the 33rd IEEE International Conference on Data Engineering, 2017

Towards stationary time-vertex signal processing.
Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

Learning time varying graphs.
Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

Autoregressive moving average graph filters a stable distributed implementation.
Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

Predicting the evolution of stationary graph signals.
Proceedings of the 51st Asilomar Conference on Signals, Systems, and Computers, 2017

2016
Predicting the evolution of stationary graph signals.
CoRR, 2016

Frequency Analysis of Temporal Graph Signals.
CoRR, 2016

Distributed Time-Varying Graph Filtering.
CoRR, 2016

Online Balanced Repartitioning.
Proceedings of the Distributed Computing - 30th International Symposium, 2016

Staffetta: Smart Duty-Cycling for Opportunistic Data Collection.
Proceedings of the 14th ACM Conference on Embedded Network Sensor Systems, SenSys 2016, 2016

Frequency analysis of time-varying graph signals.
Proceedings of the 2016 IEEE Global Conference on Signal and Information Processing, 2016

Separable autoregressive moving average graph-temporal filters.
Proceedings of the 24th European Signal Processing Conference, 2016

2015
Distributed Graph Filters.
PhD thesis, 2015

Distributed Autoregressive Moving Average Graph Filters.
IEEE Signal Process. Lett., 2015

Graph scale-space theory for distributed peak and pit identification.
Proceedings of the 14th International Conference on Information Processing in Sensor Networks, 2015

Stochastic graph filtering on time-varying graphs.
Proceedings of the 6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2015

2014
Sybil-Resistant Meta Strategies for the Forwarder's Dilemma.
Proceedings of the Eighth IEEE International Conference on Self-Adaptive and Self-Organizing Systems, 2014

Lightweight neighborhood cardinality estimation in dynamic wireless networks.
Proceedings of the IPSN'14, 2014

How to identify global trends from local decisions? Event region detection on mobile networks.
Proceedings of the 2014 IEEE Conference on Computer Communications, 2014

2013
Fairness for All, Rate Allocation for Mobile Wireless Networks.
Proceedings of the IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems, 2013

Think globally, act locally: on the reshaping of information landscapes.
Proceedings of the 12th International Conference on Information Processing in Sensor Networks (co-located with CPS Week 2013), 2013

2012
On distributed computation of information potentials.
Proceedings of the FOMC'12, 2012

2011
On mining sensor network software repositories.
Proceedings of the 2nd Workshop on Software Engineering for Sensor Network Applications, 2011

2008
A software platform for developing multi-player pervasive games using small programmable object technologies.
Proceedings of the IEEE 5th International Conference on Mobile Adhoc and Sensor Systems, 2008


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