Publications related to Learning Bollobas-Riordan Graphs Under Partial Observability

Estimating the Topology of Preferential Attachment Graphs Under Partial Observability

This work addresses the problem of learning the topology of a network from the signals emitted by the network nodes. These signals are generated over time through a linear diffusion process, where neighboring nodes exchange messages according to the underl ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Two-layer adaptive signal control framework for large-scale networks combining efficient Max-Pressure and Perimeter Control

Nikolaos Geroliminis, Dimitrios Tsitsokas, Anastasios Kouvelas

This work investigates the benefits of a two-layer adaptive signal control framework combining multi-region perimeter control (PC) with distributed Max Pressure (MP) control in selected network intersections. Motivated by MP’s questionable performance in o ...

2022

Efficient Max-Pressure Traffic Management for Large-Scale Congested Urban Networks

Nikolaos Geroliminis, Dimitrios Tsitsokas, Anastasios Kouvelas

Traffic responsive signal control systems bear high potential in reducing delays in congested networks due to their ability of dynamically adjusting right-of-way assignment among conflicting movements, based on real-time traffic measurements. In this work, ...

2022

Optimized Quantization in Distributed Graph Signal Filtering

Pascal Frossard, Isabela Cunha Maia Nobre

Distributed graph signal processing algorithms require the network nodes to communicate by exchanging messages in order to achieve a common objective. These messages have a finite precision in realistic networks, which may necessitate to implement message ...

2019

Learning Neural Connectivity from Firing Activity: Scalable Algorithms with Provable Guarantees

Martin Vetterli, Amin Karbasi, Amir Hesam Salavati

The connectivity of a neuronal network has a major effect on its functionality and role. It is generally believed that the complex network structure of the brain provides a physiological basis for information processing. Therefore, identifying the network’ ...

Springer New York LLC2018

On the Structure of Cortical Microcircuits Inferred from Small Sample Sizes

Rodrigo de Campos Perin

The structure in cortical microcircuits deviates from what would be expected in a purely random network, which has been seen as evidence of clustering. To address this issue, we sought to reproduce the nonrandom features of cortical circuits by considering ...

Soc Neuroscience2017

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

An affected core drives network integration deficits of the structural connectome in 22q11.2 deletion syndrome

Alessandra Griffa, Patric Hagmann, Marie Schaer, Frantisek Vasa

Chromosome 22q11.2 deletion syndrome (22q11DS) is a genetic disease known to lead to cerebral structural alterations, which we study using the framework of the macroscopic white-matter connectome. We create weighted connectomes of 44 patients with 22q11DS ...

Elsevier Sci Ltd2016

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Learning Bollobas-Riordan Graphs Under Partial Observability

Estimating the Topology of Preferential Attachment Graphs Under Partial Observability

Two-layer adaptive signal control framework for large-scale networks combining efficient Max-Pressure and Perimeter Control

Quantization for Distributed Processing and Learning of Structured Data

Efficient Max-Pressure Traffic Management for Large-Scale Congested Urban Networks

Optimized Quantization in Distributed Graph Signal Filtering

Learning Neural Connectivity from Firing Activity: Scalable Algorithms with Provable Guarantees

On the Structure of Cortical Microcircuits Inferred from Small Sample Sizes

Alignment and Assembly

Filtering Random Graph Processes Over Random Time-Varying Graphs

An affected core drives network integration deficits of the structural connectome in 22q11.2 deletion syndrome

Graph Chatbot

Chat with Graph Search

Two-layer adaptive signal control framework for large-scale networks combining efficient Max-Pressure and Perimeter Control

Estimating the Topology of Preferential Attachment Graphs Under Partial Observability

Efficient Max-Pressure Traffic Management for Large-Scale Congested Urban Networks

Quantization for Distributed Processing and Learning of Structured Data

Alignment and Assembly

On the Structure of Cortical Microcircuits Inferred from Small Sample Sizes

Filtering Random Graph Processes Over Random Time-Varying Graphs

An affected core drives network integration deficits of the structural connectome in 22q11.2 deletion syndrome

Optimized Quantization in Distributed Graph Signal Filtering

Learning Neural Connectivity from Firing Activity: Scalable Algorithms with Provable Guarantees