Publications related to Locating the Source of Diffusion in Large-Scale Networks

Data-Driven Reactive Power Optimization of Distribution Networks via Graph Attention Networks

Reactive power optimization of distribution networks is traditionally addressed by physical model based methods, which often lead to locally optimal solutions and require heavy online inference time consumption. To improve the quality of the solution and r ...

State Grid Electric Power Research Inst2024

Estimating the Topology of Preferential Attachment Graphs Under Partial Observability

Ali H. Sayed, Michele Cirillo

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

Information Retrieval Under Network Uncertainty: Robust Internet Ranking

Ralf Seifert, Anna Timonina-Farkas

Internet ranking algorithms play a crucial role in information technologies and numerical analysis due to their efficiency in high dimensions and wide range of possible applications, including scientometrics and systemic risk in finance (SinkRank, DebtRank ...

INFORMS2022

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

True scale-free networks hidden by finite size effects

Andrea Rinaldo, Samir Suweis, Amos Maritan

We analyze about 200 naturally occurring networks with distinct dynamical origins to formally test whether the commonly assumed hypothesis of an underlying scale-free structure is generally viable. This has recently been questioned on the basis of statisti ...

NATL ACAD SCIENCES2021

Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances

Wulfram Gerstner, Clément Hongler, Johanni Michael Brea, Francesco Spadaro, Berfin Simsek, Arthur Jacot

We study how permutation symmetries in overparameterized multi-layer neural networks generate `symmetry-induced' critical points. Assuming a network with

L

layers of minimal widths

r_1^*, \ldots, r_{L-1}^*

reaches a zero-loss minimum at $ r_1^*! \c ...

2021

The physics of higher-order interactions in complex systems

Enrico Amico, Benedetta Franceschiello

Network representations of complex systems are limited to pairwise interactions, but real-world systems often involve higher-order interactions. This Perspective looks at the new physics emerging from attempts to characterize these interactions. Complex ne ...

NATURE PORTFOLIO2021

Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing

Dimitri Nestor Alice Van De Ville, Maude Schneider, Daniela Maria Zöller, Anjali Bagunu Tarun

Causal interactions between specific psychiatric symptoms could contribute to the heterogenous clinical trajectories observed in early psychopathology. Current diagnostic approaches merge clinical manifestations that co-occur across subjects and could sign ...

eLIFE SCIENCES PUBL LTD2021

Spikyball Sampling: Exploring Large Networks via an Inhomogeneous Filtered Diffusion

Nicolas Aspert, Volodymyr Miz, Benjamin Ricaud

Studying real-world networks such as social networks or web networks is a challenge. These networks often combine a complex, highly connected structure together with a large size. We propose a new approach for large scale networks that is able to automatic ...

2020

Locating the Source of Diffusion in Large-Scale Networks

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Data-Driven Reactive Power Optimization of Distribution Networks via Graph Attention Networks

Estimating the Topology of Preferential Attachment Graphs Under Partial Observability

Information Retrieval Under Network Uncertainty: Robust Internet Ranking

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

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

True scale-free networks hidden by finite size effects

Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances

The physics of higher-order interactions in complex systems

Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing

Spikyball Sampling: Exploring Large Networks via an Inhomogeneous Filtered Diffusion

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

The physics of higher-order interactions in complex systems

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

Data-Driven Reactive Power Optimization of Distribution Networks via Graph Attention Networks

Information Retrieval Under Network Uncertainty: Robust Internet Ranking

True scale-free networks hidden by finite size effects

Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances

Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing

Spikyball Sampling: Exploring Large Networks via an Inhomogeneous Filtered Diffusion