PSP-HDC encodes a directed PSP graph into hyperdimensional vectors via a trainable encoder and graph-aligned operations to predict sheet-resistance regimes with 0.91 accuracy and intrinsic explanations.
Composition- based multi-relational graph convolutional networks
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
DiffTSP applies discrete diffusion to knowledge graph triple set prediction, recovering all missing triples simultaneously via edge-masking noise reversal and a structure-aware transformer, achieving SOTA on three datasets.
citing papers explorer
-
Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction
PSP-HDC encodes a directed PSP graph into hyperdimensional vectors via a trainable encoder and graph-aligned operations to predict sheet-resistance regimes with 0.91 accuracy and intrinsic explanations.
-
One Pass for All: A Discrete Diffusion Model for Knowledge Graph Triple Set Prediction
DiffTSP applies discrete diffusion to knowledge graph triple set prediction, recovering all missing triples simultaneously via edge-masking noise reversal and a structure-aware transformer, achieving SOTA on three datasets.