深图学习需融合网络科学，否则将停滞不前。
Deep Graph Learning will stall without Network Science
- 提出六项行动呼吁，推动深图学习与网络科学结合
- 强调忽略网络科学基础原理将导致发展瓶颈
- 适合关注图神经网络长期发展的研究者
深度图学习致力于构建灵活且可泛化的模型，以自动化方式捕捉图结构数据中的模式。网络科学则关注基于明确假设的模型与度量，揭示复杂系统的组织原则。两领域目标一致：更好地建模和理解图结构数据。然而，深度图学习过度追求经验性能，忽视了网络科学的核心洞见。本文主张，若不引入网络科学的深层启示，深度图学习将陷入停滞。为此，我们提出六项行动呼吁，旨在挖掘网络科学中未被充分利用的洞见，解决当前深度图学习面临的关键问题，确保该领域持续进步。
原文摘要 · Abstract (English)
Deep graph learning focuses on flexible and generalizable models that learn patterns in an automated fashion. Network science focuses on models and measures revealing the organizational principles of complex systems with explicit assumptions. Both fields share the same goal: to better model and understand patterns in graph-structured data. However, deep graph learning prioritizes empirical performance but ignores fundamental insights from network science. Our position is that deep graph learning will stall without insights from network science. In this position paper, we formulate six Calls for Action to leverage untapped insights from network science to address current issues in deep graph learning, ensuring the field continues to make progress.
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