TIDE decomposes graph information into feature-specific, structure-specific, and joint components to retain only label-relevant joint signals and improve OOD detection over standard supervised learning.
Kipf and Max Welling , title =
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TRN-R1-Zero is an RL-only post-training method that lets LLMs perform zero-shot node, edge, and graph reasoning on text-rich networks without supervised data or larger-model distillation.
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What Information Matters? Graph Out-of-Distribution Detection via Tri-Component Information Decomposition
TIDE decomposes graph information into feature-specific, structure-specific, and joint components to retain only label-relevant joint signals and improve OOD detection over standard supervised learning.
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TRN-R1-Zero: Text-rich Network Reasoning via LLMs with Reinforcement Learning Only
TRN-R1-Zero is an RL-only post-training method that lets LLMs perform zero-shot node, edge, and graph reasoning on text-rich networks without supervised data or larger-model distillation.