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New STAMP method improves domain adaptation for time-split graph data

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced STAMP, a novel message-passing operator designed for domain adaptation in graphs that are split chronologically. This method addresses the performance degradation that occurs when models trained on historical graph data are applied to more recent data, due to temporal distribution shifts. STAMP works by balancing messages across timestamps, ensuring first-moment invariance and maintaining scalability. The approach has demonstrated state-of-the-art results on benchmarks like OGBN-Arxiv and OGBN-MAG, and consistently improves performance on the temporal-split Pokec dataset. AI

IMPACT This research offers a new technique for improving the robustness of graph-based AI models when dealing with time-varying data distributions.

RANK_REASON The cluster contains an academic paper detailing a new method for graph learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New STAMP method improves domain adaptation for time-split graph data

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The cluster contains an academic paper detailing a new method for graph learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Joo Young Park, Sejun Park, Hyunwoo Park ·

    STAMP: Symmetric Temporal Averaging Message Passing for Domain Adaptation in Chronologically Split Graphs

    arXiv:2411.10957v2 Announce Type: replace-cross Abstract: This paper addresses domain adaptation in graph-structured data under chronological splits. We consider transductive semi-supervised node classification (SSNC), in which labels of historical nodes are used to predict label…

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