揭穿图学习中五个常见误解，助研究者聚焦真实问题。
Oversmoothing, Oversquashing, Heterophily, Long-Range, and more: Demystifying Common Beliefs in Graph Machine Learning
- 通过反例揭示过度平滑、过度挤压等普遍认知的局限性
- 指出同质性-异质性二分法在实际中的模糊边界
- 适合对图神经网络原理有深入探究需求的研究者
在图机器学习领域经历深度学习视角下的复兴后，研究重点转向对消息传递机制优势与局限的深层理解。本文发现，关于过度平滑、过度挤压、同质性-异质性二分法以及长程任务等话题的快速进展，伴随着一系列被广泛接受的普遍性信念——这些信念常以绝对化陈述形式出现，但并非总是成立，也难以与其他概念区分。这导致了对研究问题的模糊理解，阻碍了精准研究问题的提出与解决，引发大量误解。本文旨在显式揭示这些常见信念，鼓励批判性思考，通过简单但形式充分的反例反驳绝对化断言。最终目标是厘清概念差异，帮助研究者更清晰、有针对性地定义和应对具体问题。
原文摘要 · Abstract (English)
After a renaissance phase in which researchers revisited the message-passing paradigm through the lens of deep learning, the graph machine learning community shifted its attention towards a deeper and practical understanding of message-passing's benefits and limitations. In this paper, we notice how the fast pace of progress around the topics of oversmoothing and oversquashing, the homophily-heterophily dichotomy, and long-range tasks, came with the consolidation of commonly accepted beliefs and assumptions -- under the form of universal statements -- that are not always true nor easy to distinguish from each other. We argue that this has led to ambiguities around the investigated problems, preventing researchers from focusing on and addressing precise research questions while causing a good amount of misunderstandings. Our contribution is to make such common beliefs explicit and encourage critical thinking around these topics, refuting universal statements via simple yet formally sufficient counterexamples. The end goal is to clarify conceptual differences, helping researchers address more clearly defined and targeted problems.
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