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Found 6 papers, 0 papers with code
Bayesian Detection of Mesoscale Structures in Pathway Data on Graphs
We usually detect mesoscale structures under the assumption of independence of interactions.
Bayesian Inference of Transition Matrices from Incomplete Graph Data with a Topological Prior
Lastly, we show that this higher accuracy improves the results for downstream network analysis tasks like cluster detection and node ranking, which highlights the practical relevance of our method for analyses of various networked systems.
One Graph to Rule them All: Using NLP and Graph Neural Networks to analyse Tolkien's Legendarium
Natural Language Processing and Machine Learning have considerably advanced Computational Literary Studies.
De Bruijn goes Neural: Causality-Aware Graph Neural Networks for Time Series Data on Dynamic Graphs
Our architecture builds on multiple layers of higher-order De Bruijn graphs, an iterative line graph construction where nodes in a De Bruijn graph of order k represent walks of length k-1, while edges represent walks of length k. We develop a graph neural network architecture that utilizes De Bruijn graphs to implement a message passing scheme that follows a non-Markovian dynamics, which enables us to learn patterns in the causal topology of a dynamic graph.
Predicting Influential Higher-Order Patterns in Temporal Network Data
We compare MOGen-based centralities to equivalent measures for network models and path data in a prediction experiment where we aim to identify influential nodes in out-of-sample data.
HOTVis: Higher-Order Time-Aware Visualisation of Dynamic Graphs
Addressing this gap, we present a novel dynamic graph visualisation algorithm that utilises higher-order graphical models of causal paths in time series data to compute time-aware static graph visualisations.
