Publications related to Simplifying complex networks

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

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

NICEpath: Finding metabolic pathways in large networks through atom-conserving substrate-product pairs

Vassily Hatzimanikatis, Jasmin Maria Hafner

Motivation: Finding biosynthetic pathways is essential for metabolic engineering of organisms to produce chemicals, biodegradation prediction of pollutants and drugs, and for the elucidation of bioproduction pathways of secondary metabolites. A key step in ...

OXFORD UNIV PRESS2021

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

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

Consensus Control for Decentralized Deep Learning

Martin Jaggi, Sebastian Urban Stich, Anastasiia Koloskova, Tao Lin, Lingjing Kong

Decentralized training of deep learning models enables on-device learning over networks, as well as efficient scaling to large compute clusters. Experiments in earlier works reveal that, even in a data-center setup, decentralized training often suffers fro ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021

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

Local Vulnerabilities and Global Robustness of Coupled Dynamical Systems on Complex Networks

Melvyn Sandy Tyloo

Coupled dynamical systems are omnipresent in everyday life. In general, interactions between individual elements composing the system are captured by complex networks. The latter greatly impact the way coupled systems are functioning and evolving in time. ...

EPFL2020

Morphology and morphodynamics of braided rivers: an experimental investigation

Daniel Vito Papa

Braided rivers form some of the most fascinating fluvial patterns found on Earth. They are identifiable by their unique morphology of complex networks of intertwined channels that spread across wide floodplains. Detailed knowledge of their dynamics is need ...

EPFL2020

Local Tomography of Large Networks Under the Low-Observability Regime

Ali H. Sayed, Augusto José Rabelo Almeida Santos

This article studies the problem of reconstructing the topology of a network of interacting agents via observations of the state-evolution of the agents. We focus on the large-scale network setting with the additional constraint of partial observations, wh ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Simplifying complex networks

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

True scale-free networks hidden by finite size effects

NICEpath: Finding metabolic pathways in large networks through atom-conserving substrate-product pairs

The physics of higher-order interactions in complex systems

Learning Bollobas-Riordan Graphs Under Partial Observability

Consensus Control for Decentralized Deep Learning

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

Local Vulnerabilities and Global Robustness of Coupled Dynamical Systems on Complex Networks

Morphology and morphodynamics of braided rivers: an experimental investigation

Local Tomography of Large Networks Under the Low-Observability Regime

The physics of higher-order interactions in complex systems

NICEpath: Finding metabolic pathways in large networks through atom-conserving substrate-product pairs

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

Local Vulnerabilities and Global Robustness of Coupled Dynamical Systems on Complex Networks

True scale-free networks hidden by finite size effects

Local Tomography of Large Networks Under the Low-Observability Regime

Learning Bollobas-Riordan Graphs Under Partial Observability

Consensus Control for Decentralized Deep Learning

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

Morphology and morphodynamics of braided rivers: an experimental investigation