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Joint network topology inference in the presence of hidden nodes
We investigate the increasingly prominent task of jointly inferring multiple networks from
nodal observations. While most joint inference methods assume that observations are …
nodal observations. While most joint inference methods assume that observations are …
Recovering missing node features with local structure-based embeddings
Node features bolster graph-based learning when exploited jointly with network structure.
However, a lack of nodal attributes is prevalent in graph data. We present a framework to …
However, a lack of nodal attributes is prevalent in graph data. We present a framework to …
Limits of dense simplicial complexes
We develop a theory of limits for sequences of dense abstract simplicial complexes, where a
sequence is considered convergent if its homomorphism densities converge. The limiting …
sequence is considered convergent if its homomorphism densities converge. The limiting …
Mitigating subpopulation bias for fair network topology inference
We consider fair network topology inference from nodal observations. Real-world networks
often exhibit biased connections based on sensitive nodal attributes. Hence, different …
often exhibit biased connections based on sensitive nodal attributes. Hence, different …
Online Network Inference from Graph-Stationary Signals with Hidden Nodes
Graph learning is the fundamental task of estimating unknown graph connectivity from
available data. Typical approaches assume that not only is all information available …
available data. Typical approaches assume that not only is all information available …
Joint graph learning from Gaussian observations in the presence of hidden nodes
Graph learning problems are typically approached by focusing on learning the topology of a
single graph when signals from all nodes are available. However, many contemporary …
single graph when signals from all nodes are available. However, many contemporary …
Scalable Implicit Graphon Learning
Graphons are continuous models that represent the structure of graphs and allow the
generation of graphs of varying sizes. We propose Scalable Implicit Graphon Learning …
generation of graphs of varying sizes. We propose Scalable Implicit Graphon Learning …
Multiview Graph Learning with Consensus Graph
Graph topology inference is a significant task in many application domains. Existing
approaches are mostly limited to learning a single graph assuming that the observed data is …
approaches are mostly limited to learning a single graph assuming that the observed data is …
Nonparametric Two-Sample Test for Networks Using Joint Graphon Estimation
This paper focuses on the comparison of networks on the basis of statistical inference. For
that purpose, we rely on smooth graphon models as a nonparametric modeling strategy that …
that purpose, we rely on smooth graphon models as a nonparametric modeling strategy that …
[PDF][PDF] Graphon models for network data: estimation, extensions and applications
B Sischka - 2023 - edoc.ub.uni-muenchen.de
Network data are nowadays prevalent in various fields such as social and political sciences,
economics, biology, neurosciences, and others. This is due to the fact that the structure …
economics, biology, neurosciences, and others. This is due to the fact that the structure …