4 papers
ASEHybrid: When Geometry Matters Beyond Homophily in Graph Neural Networks
Shalima Binta Manir, Tim Oates
Standard message-passing graph neural networks (GNNs) often struggle on graphs with low homophily, yet homophily alone does not explain this behavior, as graphs with similar homoph…
Hierarchical Learning for Maze Navigation: Emergence of Mental Representations via Second-Order Learning
Shalima Binta Manir, Tim Oates
Mental representation, characterized by structured internal models mirroring external environments, is fundamental to advanced cognition but remains challenging to investigate empi…
One Model, Two Minds: A Context-Gated Graph Learner that Recreates Human Biases
Shalima Binta Manir, Tim Oates
We introduce a novel Theory of Mind (ToM) framework inspired by dual-process theories from cognitive science, integrating a fast, habitual graph-based reasoning system (System 1),…
Predicting the Performance of Graph Convolutional Networks with Spectral Properties of the Graph Laplacian
Shalima Binta Manir, Tim Oates
A common observation in the Graph Convolutional Network (GCN) literature is that stacking GCN layers may or may not result in better performance on tasks like node classification a…