This paper introduces two novel scores for detecting local perturbations in networks. For this,
we consider a non-Euclidean representation of networks, namely, their embedding onto the …

Change-point detection in dynamic networks is a challenging task which is particularly due
to the complex nature of temporal graphs. Existing approaches are based on the extraction …

The evidence indicates that intracranial EEG connectivity, as estimated from daily resting
state recordings from epileptic patients, may be capable of identifying preictal states. In this …

Graph theoretical approaches have been proven to be effective in the characterization of
connected systems, as well as in quantifying their dysfunction due to perturbation. In this …

Human migration patterns influence the redistribution of population characteristics over the
geography and since such distributions are closely related to social and economic …

We analyse the geometric instability of embeddings produced by graph neural networks
(GNNs). Existing methods are only applicable for small graphs and lack context in the graph …

We analyse the geometric instability of embeddings produced by graph neural networks
(GNNs). Existing methods are only applicable for small graphs and lack context in the graph …

Graph autoencoders (GAE), also known as graph embedding methods, learn latent
representations of the nodes of a graph in a low-dimensional space where the structural …

Subsurface modeling deals with data-related issues like cognitive and sampling biases, and
model-related challenges including statistical assumptions, misspecification, and algorithmic …

Unsupervised Inductive node representation learning for dynamic graphs | Research
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