Social networks represent a common form of interconnected data frequently depicted as graphs within the domain of deep learning-based inference. These communities inherently form dynamic systems, achieving stability through continuous internal communications and opinion exchanges among social actors along their social ties. In contrast, neural message passing in deep learning provides a clear and intuitive mathematical framework for understanding information propagation and aggregation among connected nodes in graphs. Node representations are dynamically updated by considering both the connectivity and status of neighboring nodes. This research harmonizes concepts from sociometry and neural message passing to analyze and infer the behavior of dynamic systems. Drawing inspiration from opinion dynamics in sociology, we propose ODNet, a novel message passing scheme incorporating bounded confidence, to refine the influence weight of local nodes for message propagation. We adjust the similarity cutoffs of bounded confidence and influence weights of ODNet and define opinion exchange rules that align with the characteristics of social network graphs. We show that ODNet enhances prediction performance across various graph types and alleviates oversmoothing issues. Furthermore, our approach surpasses conventional baselines in graph representation learning and proves its practical significance in analyzing real-world co-occurrence networks of metabolic genes. Remarkably, our method simplifies complex social network graphs solely by leveraging knowledge of interaction frequencies among entities within the system. It accurately identifies internal communities and the roles of genes in different metabolic pathways, including opinion leaders, bridge communicators, and isolators.

翻译：社交网络是深度学习推理中常见的数据互联形式，通常以图结构呈现。这些社群本质上是动态系统，通过社会行动者沿着社交纽带持续进行的内部沟通和观点交流实现稳定。相比之下，深度学习中的神经消息传递为理解图中连通节点间的信息传播与聚合提供了清晰直观的数学框架。节点表示通过同时考虑邻接节点的连接性和状态进行动态更新。本研究将社会测量学概念与神经消息传递相融合，用于分析和推断动态系统的行为。受社会学中观点动力学的启发，我们提出了一种融合有限置信度的新型消息传递方案ODNet，以精细化局部节点在消息传播中的影响权重。我们调整了ODNet的有限置信度相似性阈值和影响权重，并定义了符合社交网络图特性的观点交换规则。实验证明，ODNet能提升多种图类型的预测性能，并缓解过平滑问题。此外，我们的方法在图表示学习中超越了传统基线，并在分析真实代谢基因共现网络时验证了其实用价值。值得注意的是，该方法仅利用系统内实体间的交互频率信息即可简化复杂社交网络图，准确识别内部社群以及基因在不同代谢通路中的角色，包括意见领袖、桥接传播者和孤立者。