GraphMF: QoS Prediction for Large Scale Blockchain Service Selection

Abstract

Blockchain-as-a-service (BaaS) experienced a dramatical growth in recent years, making it a hot research topic. With the expanding scale of distributed services deployed on the blockchain system, it is increasingly urgent to evaluate quality of service (QoS) attributes of blockchain services and in-blockchain peers-clients connections. The complicated association of service invocation and network environment naturally form a graph, making it possible to extract features through graph neural networks (GNN). To incorporate graph-structured information in QoS prediction, we proposed a graph matrix factorization (GraphMF) take advantages of both GNNs and collaborative filtering to estimate missing QoS values in the data matrix. Experiment conducted on a real-world dataset demonstrated the effectiveness of our model.