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Subgraph classification through neighborhood pooling
(2023-06-01)
Subgraph classification is an emerging field in graph representation learning where the task is to classify a group of nodes (i.e., a subgraph) within a graph. Graph neural networks (GNNs) are the de facto solution for ...
Scalable subgraph representation learning through simplification
(2023-06-01)
Link prediction on graphs is a fundamental problem. Subgraph representation learning approaches (SGRLs), by transforming link prediction to graph classification on the subgraphs around the links, have achieved state-of-the-art ...
An investigation into the use of ConvNext within IICS/IIDS framework for person Re-ID
(2023-05-01)
In this thesis, we explore the integration of ConvNeXt, a CNN-based network inspired by vision transformers, into the Intra and Inter Camera Similarity (IICS) and Intra and Inter Domain Similarity (IIDS) frameworks for ...
Discovery of trend dependencies over time-series
(2023-11-01)
We improve constraint-based data quality using trend dependency (TD) discovery, extending existing order dependencies (ODs) to allow variations and exceptions. Unlike ODs, TDs capture approximate functional mappings between ...