
Improving Robustness of Graph Neural Networks with HeterophilyInspired Designs
Recent studies have exposed that many graph neural networks (GNNs) are s...
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Signal processing on simplicial complexes
Higherorder networks have so far been considered primarily in the conte...
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Local, global and scaledependent node roles
This paper reexamines the concept of node equivalences like structural ...
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What are higherorder networks?
Modeling complex systems and data using the language of graphs and netwo...
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Finite Impulse Response Filters for Simplicial Complexes
In this paper, we study linear filters to process signals defined on sim...
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Signal Processing on HigherOrder Networks: Livin' on the Edge ... and Beyond
This tutorial paper presents a didactic treatment of the emerging topic ...
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Modularity maximisation for graphons
Networks are a widelyused tool to investigate the largescale connectiv...
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Detectability of hierarchical communities in networks
We study the problem of recovering a planted hierarchy of partitions in ...
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Hierarchical community structure in networks
Modular and hierarchical structures are pervasive in realworld complex ...
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Blind identification of stochastic block models from dynamical observations
We consider a blind identification problem in which we aim to recover a ...
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Graphbased SemiSupervised & Active Learning for Edge Flows
We present a graphbased semisupervised learning (SSL) method for learn...
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Spectral partitioning of timevarying networks with unobserved edges
We discuss a variant of `blind' community detection, in which we aim to ...
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Flow Smoothing and Denoising: Graph Signal Processing in the EdgeSpace
This paper focuses on devising graph signal processing tools for the tre...
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Random Walks on Simplicial Complexes and the normalized Hodge Laplacian
Modeling complex systems and data with graphs has been a mainstay of the...
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Structured networks and coarsegrained descriptions: a dynamical perspective
This chapter discusses the interplay between structure and dynamics in c...
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Simplicial Closure and Higherorder Link Prediction
Networks provide a powerful formalism for modeling complex systems, by r...
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Centrality measures for graphons
Graphs provide a natural mathematical abstraction for systems with pairw...
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Michael T. Schaub
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