验证了图自编码器中权重共享的优越性，推荐普遍采用。
To Share or Not to Share: Investigating Weight Sharing in Variational Graph Autoencoders
- 通过系统实验分析权重共享在变分图自编码器中的影响。
- 发现共享权重能提升模型性能且不显著损失精度。
- 适合希望简化模型并增强泛化能力的研究者参考。
本文深入研究了变分图自编码器（VGAE）中尚未被充分探讨的权重共享（WS）实践。尽管权重共享在模型设计和节点嵌入学习中兼具优势与弊端，但其整体价值尚不明确，是否应采纳也未有定论。我们通过在多种图结构和VGAE变体上的广泛实验，严谨分析其影响，结果表明权重共享的优势始终超过其缺点。基于此，我们建议将权重共享作为优化、正则化和简化VGAE模型的有效策略，且不会带来显著性能损失。
原文摘要 · Abstract (English)
This paper investigates the understudied practice of weight sharing (WS) in variational graph autoencoders (VGAE). WS presents both benefits and drawbacks for VGAE model design and node embedding learning, leaving its overall relevance unclear and the question of whether it should be adopted unresolved. We rigorously analyze its implications and, through extensive experiments on a wide range of graphs and VGAE variants, demonstrate that the benefits of WS consistently outweigh its drawbacks. Based on our findings, we recommend WS as an effective approach to optimize, regularize, and simplify VGAE models without significant performance loss.
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