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.

翻译：在研究者通过深度学习视角重新审视消息传递范式并经历复兴阶段后，图机器学习社区将注意力转向对消息传递优势与局限的更深入且实用的理解。本文指出，围绕过平滑与过挤压、同配性-异配性二分法以及长程任务等议题的快速进展，伴随着以普遍性陈述形式出现的常见认知与假设的固化——这些陈述并非总是成立，且彼此之间不易区分。我们认为这导致所研究问题存在模糊性，阻碍研究者聚焦并解决精确的研究问题，同时引发大量误解。本文的贡献在于明确阐述此类常见认知，并鼓励围绕这些议题展开批判性思考，通过简单但形式充分的反例驳斥普遍性陈述。最终目标是厘清概念差异，帮助研究者处理定义更清晰、目标更明确的问题。