Graph research, the systematic study of interconnected data points represented as graphs, plays a vital role in capturing intricate relationships within networked systems. However, in the real world, as graphs scale up, concerns about data security among different data-owning agencies arise, hindering information sharing and, ultimately, the utilization of graph data. Therefore, establishing a mutual trust mechanism among graph agencies is crucial for unlocking the full potential of graphs. Here, we introduce a Cooperative Network Learning (CNL) framework to ensure secure graph computing for various graph tasks. Essentially, this CNL framework unifies the local and global perspectives of GNN computing with distributed data for an agency by virtually connecting all participating agencies as a global graph without a fixed central coordinator. Inter-agency computing is protected by various technologies inherent in our framework, including homomorphic encryption and secure transmission. Moreover, each agency has a fair right to design or employ various graph learning models from its local or global perspective. Thus, CNL can collaboratively train GNN models based on decentralized graphs inferred from local and global graphs. Experiments on contagion dynamics prediction and traditional graph tasks (i.e., node classification and link prediction) demonstrate that our CNL architecture outperforms state-of-the-art GNNs developed at individual sites, revealing that CNL can provide a reliable, fair, secure, privacy-preserving, and global perspective to build effective and personalized models for network applications. We hope this framework will address privacy concerns in graph-related research and integrate decentralized graph data structures to benefit the network research community in cooperation and innovation.

翻译：图研究作为对以图结构表示的数据点间关系进行系统性分析的学科，在捕获网络系统中复杂关联方面具有关键作用。然而在现实场景中，随着图规模持续扩大，不同数据持有机构间的数据安全问题日益凸显，这阻碍了信息共享并最终制约了图数据的价值挖掘。因此，建立图机构间的互信机制对于充分释放图数据的潜能至关重要。本文提出协同网络学习（CNL）框架，旨在为各类图任务提供安全的图计算支持。本质上，该框架通过将参与机构虚拟连接成无固定中心协调的全局图，统一了机构内分布式数据在图神经网络计算中的局部与全局视角。机构间计算过程受到框架内置的多种技术保护，包括同态加密与安全传输。此外，各机构享有公平权利，可从自身局部或全局视角设计或采用不同的图学习模型。因此，CNL能够基于从局部与全局图推断出的去中心化图结构，协同训练图神经网络模型。在传染病动力学预测及传统图任务（节点分类与链路预测）上的实验表明，本框架显著优于各站点独立开发的最先进图神经网络，证明CNL能为网络应用提供可靠、公平、安全、隐私保护且具备全局视野的高效个性化模型构建方案。期望该框架能解决图相关研究中的隐私顾虑，通过整合去中心化图数据结构，推动网络研究领域的协作创新。