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ShareGNNs enhance MPNNs with structure-aware weight sharing

By PulseAugur Editorial · [2 sources] ·

Researchers have developed ShareGNNs, a novel approach to message-passing neural networks (MPNNs) that enhances their ability to capture structural patterns in graph-structured data. This method incorporates graph structure directly into weight sharing, indexing weights by user-chosen graph invariants. This allows for systematic reuse across structurally equivalent subgraphs, offering explicit control over model complexity and improving expressivity beyond standard MPNNs. Experiments show consistent improvements on various tasks, including subgraph counting, and demonstrate scalability to large datasets. AI

IMPACT Introduces a novel method for graph representation learning, potentially improving performance on structured data tasks.

RANK_REASON The cluster contains an academic paper detailing a new model architecture and methodology.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

ShareGNNs enhance MPNNs with structure-aware weight sharing

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The cluster contains an academic paper detailing a new model architecture and methodology.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Florian Seiffarth ·

    Invariant-Based Weight Sharing for Message Passing

    arXiv:2605.25750v1 Announce Type: new Abstract: Message-passing neural networks (MPNNs) are a powerful framework for learning representations of graph-structured domains. However, weights in MPNNs act on features only, limiting their ability to capture structural patterns. We int…

  2. arXiv cs.LG TIER_1 English(EN) · Florian Seiffarth ·

    Invariant-Based Weight Sharing for Message Passing

    Message-passing neural networks (MPNNs) are a powerful framework for learning representations of graph-structured domains. However, weights in MPNNs act on features only, limiting their ability to capture structural patterns. We introduce a novel structure-aware weight sharing pr…

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