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Recommender systems are able to learn user preferences based on user and itemrepresentations via their historical behaviors. To improve representationlearning, recent recommendation models start leveraging information fromvarious behavior types exhibited by users. In real-world scenarios, the userbehavioral graph is not only multiplex but also dynamic, i.e., the graphevolves rapidly over time, with various types of nodes and edges added ordeleted, which causes the Neighborhood Disturbance. Nevertheless, most existingmethods neglect such streaming dynamics and thus need to be retrained once thegraph has significantly evolved, making them unsuitable in the online learningenvironment. Furthermore, the Neighborhood Disturbance existing in dynamicgraphs deteriorates the performance of neighbor-aggregation based graph models.To this end, we propose SUPA, a novel graph neural network for dynamicmultiplex heterogeneous graphs. Compared to neighbor-aggregation architecture,SUPA develops a sample-update-propagate architecture to alleviate neighborhooddisturbance. Specifically, for each new edge, SUPA samples an influencedsubgraph, updates the representations of the two interactive nodes, andpropagates the interaction information to the sampled subgraph. Furthermore, totrain SUPA incrementally online, we propose InsLearn, an efficient workflow forsingle-pass training of large dynamic graphs. Extensive experimental results onsix real-world datasets show that SUPA has a good generalization ability and issuperior to sixteen state-of-the-art baseline methods. The source code isavailable at https://github.com/shatter15/SUPA.

Instant Representation Learning for Recommendation over Large Dynamic Graphs