Publication

Modeling Network Populations via Graph Distances

Related publications (31)

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Understanding epidemic propagation in large networks is an important but challenging task, especially since we usually lack information, and the information that we have is often counter-intuitive. An illustrative example is the dependence of the final siz ...

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Deep MinCut: Learning Node Embeddings by Detecting Communities

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We present Deep MinCut (DMC), an unsupervised approach to learn node embeddings for graph -structured data. It derives node representations based on their membership in communities. As such, the embeddings directly provide insights into the graph structure ...

ELSEVIER SCI LTD2022

The kinectome: A comprehensive kinematic map of human motion in health and disease

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Human voluntary movement stems from the coordinated activations in space and time of many musculoskeletal segments. However, the current methodological approaches to study human movement are still limited to the evaluation of the synergies among a few body ...

WILEY2022

Virtual Capacitor Concept for Partitioning of Large Converter Systems for RT-HIL Simulations

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2022

Applicability of the landscape evolution model in the absence of rills

David Andrew Barry, Paolo Perona, Seifeddine Jomaa, Andrea Rinaldo, Mohsen Cheraghi, Andrea Cimatoribus

Despite numerous applications of physically-based models for incised landscapes, their applicability for overland flow on unchanneled surfaces is not known. This work challenges a widely used landscape evolution model for the case of non-uniform rainfall a ...

2022

Investigating cognitive ability using action-based models of structural brain networks

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Recent developments in network neuroscience have highlighted the importance of developing techniques for analysing and modelling brain networks. A particularly powerful approach for studying complex neural systems is to formulate generative models that use ...

OXFORD UNIV PRESS2022

Quantization for Distributed Processing and Learning of Structured Data

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In the domains of machine learning, data science and signal processing, graph or network data, is becoming increasingly popular. It represents a large portion of the data in computer, transportation systems, energy networks, social, biological, and other s ...

EPFL2022

Large-scale traffic signal control and multimodal network design

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Traffic congestion constitutes one of the most frequent, yet challenging, problems to address in the urban space. Caused by the concentration of population, whose mobility needs surpass the serving capacity of urban networks, congestion cannot be resolved ...

EPFL2022

Atomic subgraphs and the statistical mechanics of networks

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We develop random graph models where graphs are generated by connecting not only pairs of vertices by edges, but also larger subsets of vertices by copies of small atomic subgraphs of arbitrary topology. This allows for the generation of graphs with extens ...

2021

node2coords: Graph Representation Learning with Wasserstein Barycenters

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In order to perform network analysis tasks, representations that capture the most relevant information in the graph structure are needed. However, existing methods learn representations that cannot be interpreted in a straightforward way and that are relat ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Representation Learning for Multi-relational Data

Eda Bayram

Recent years have witnessed a rise in real-world data captured with rich structural information that can be better depicted by multi-relational or heterogeneous graphs. However, research on relational representation learning has so far mostly focused on th ...

EPFL2021