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[2505.15547] Oversmoothing, Oversquashing, Heterophily, Long-Range, and more: Demystifying Common Beliefs in Graph Machine Learning
Summary
This article examines common misconceptions in graph machine learning, focusing on oversmoothing, oversquashing, and the homophily-heterophily dichotomy, aiming to clarify these concepts and encourage critical thinking in the field.
Why It Matters
Understanding the nuances of graph machine learning is crucial for researchers and practitioners. This paper addresses prevalent misconceptions that can hinder progress and clarity in research, promoting a more accurate understanding of the challenges and opportunities within the field.
Key Takeaways
- Oversmoothing and oversquashing are often misunderstood concepts in graph ML.
- The homophily-heterophily dichotomy requires careful distinction to avoid confusion.
- Common beliefs in the field may not hold universally and need critical examination.
- Clarifying these concepts can lead to more targeted and effective research questions.
- Counterexamples are provided to challenge established assumptions.
Computer Science > Machine Learning arXiv:2505.15547 (cs) [Submitted on 21 May 2025 (v1), last revised 19 Feb 2026 (this version, v3)] Title:Oversmoothing, Oversquashing, Heterophily, Long-Range, and more: Demystifying Common Beliefs in Graph Machine Learning Authors:Adrian Arnaiz-Rodriguez, Federico Errica View a PDF of the paper titled Oversmoothing, Oversquashing, Heterophily, Long-Range, and more: Demystifying Common Beliefs in Graph Machine Learning, by Adrian Arnaiz-Rodriguez and 1 other authors View PDF HTML (experimental) Abstract: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 u...
