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.

建立一种合作网络学习（CNL）框架以确保各种图任务的安全图计算，并解决图相关研究中的隐私问题和整合分布式图数据结构，从而促进网络研究社区的合作和创新。