Cloud-edge Collaboration for Robust Network Embeddings

Learning network representations, also known as network embeddings, has attracted significant attention in recent years. Real-world scenarios often involve networks with multiple views, where each view captures a distinct aspect of the network’s structure. Existing network embedding methods mainly focus on the global information from each view, neglecting the implied relations among multiple views. Additionally, maintaining the scalability of node embeddings while adapting to changes in network topology remains a major challenge. To this end, this paper proposes a C loud-edge C ollaboration Net work (CC-Net) to learn robust node embeddings in multi-view networks. Specifically, we design a decomposition and regrouping module to capture implied relations within multi-view networks, enabling the generation of comprehensive node representations that integrate information from all sub-networks. Besides, by leveraging the hybrid approach of cloud and edge computing, our proposed CC-Net can efficiently handle the complexities and dynamics of multi-view networks without retraining the entire network. Extensive experiments and analyses on real-world Twitter and YouTube datasets demonstrate the superiority of our approach compared to several benchmark methods, and validate its effectiveness in capturing implied relations and generating robust node embeddings.