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Found 7 papers, 6 papers with code
BrainOOD: Out-of-distribution Generalizable Brain Network Analysis
Graph Neural Networks (GNNs) have shown promising in analyzing brain networks, but there are two major challenges in using GNNs: (1) distribution shifts in multi-site brain network data, leading to poor Out-of-Distribution (OOD) generalization, and (2) limited interpretability in identifying key brain regions critical to neurological disorders.
ABXI: Invariant Interest Adaptation for Task-Guided Cross-Domain Sequential Recommendation
Another challenge lies in aligning the domain-specific and cross-domain sequences.
Multi-Atlas Brain Network Classification through Consistency Distillation and Complementary Information Fusion
Some recent methods have proposed utilizing multiple atlases, but they neglect consistency across atlases and lack ROI-level information exchange.
Contrasformer: A Brain Network Contrastive Transformer for Neurodegenerative Condition Identification
It generates a prior-knowledge-enhanced contrast graph to address the distribution shifts across sub-populations by a two-stream attention mechanism.
CPMR: Context-Aware Incremental Sequential Recommendation with Pseudo-Multi-Task Learning
To dually improve the performance of temporal states evolution and incremental recommendation, we design a Pseudo-Multi-Task Learning (PMTL) paradigm by stacking the incremental single-target recommendations into one multi-target task for joint optimization.
Contrastive Graph Pooling for Explainable Classification of Brain Networks
Our contributions underscore the potential of ContrastPool for advancing the understanding of brain networks and neurodegenerative conditions.
Union Subgraph Neural Networks
We first investigate different kinds of connectivities existing in a local neighborhood and identify a substructure called union subgraph, which is able to capture the complete picture of the 1-hop neighborhood of an edge.
