SCode: A Spherical Code Metric Learning Approach to Continuously Monitoring Predictive Events in Networked Data

Dynamic graphs are common in many applications to conveniently model heterogeneous data integrated from multiple sources. We study the monitoring of predictive events in dynamic graphs. Treating the problem as a continuous multi-label classification, we use deep metric learning to manage the embedding space and to create spherical codes where each codeword is an embedding vector representing a cluster of data state embeddings with the same results of the predictive events. By continuously training data embeddings from a dynamic graph neural network (DGNN) model and a code generator together, our method, called SCode, achieves significantly better accuracy than DGNN baselines. Moreover, SCode is also about twice as fast as the DGNN baselines, owing to its efficient matching between data state embedding and codewords for multiple events together. Finally, our training sample complexity analysis also sheds light on the generalizability of the online learning.