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This is a PyTorch implementation of the GraphRTA algorithm, which tries to address the open-set graph domain adaptation problem, where the goal is to not only correctly classify target nodes into the ...

Abstract: Deep networks require large, well-labeled data for effective training, but acquiring such data can be prohibitively costly or impractical. Domain adaptation (DA) addresses this challenge by ...

Graphs and data visualizations are all around us—charting our steps, our election results, our favorite sports teams’ stats, and trends across our world. But too often, people glance at a graph ...

Graphs are a ubiquitous data structure and a universal language for representing objects and complex interactions. They can model a wide range of real-world systems, such as social networks, chemical ...

Abstract: Graphs that have each edge corresponding to a positive or negative sign are called signed graphs. These graphs are essential for applications in social sciences and social networks, where ...

The recent works proposing transformer-based models for graphs have proven the inadequacy of Vanilla Transformer for graph representation learning. To understand this inadequacy, there is a need to ...