Instant Representation Learning for Recommendation over Large Dynamic
  Graphs
Cheng Wu Chaokun Wang Jingcao Xu Ziwei Fang Tiankai Gu Changping Wang Yang Song Kai Zheng Xiaowei Wang Guorui Zhou
Recommender systems are able to learn user preferences based on user and item
representations via their historical behaviors. To improve representation
learning, recent recommendation models start leveraging information from
various behavior types exhibited by users. In real-world scenarios, the user
behavioral graph is not only multiplex but also dynamic, i.e., the graph
evolves rapidly over time, with various types of nodes and edges added or
deleted, which causes the Neighborhood Disturbance. Nevertheless, most existing
methods neglect such streaming dynamics and thus need to be retrained once the
graph has significantly evolved, making them unsuitable in the online learning
environment. Furthermore, the Neighborhood Disturbance existing in dynamic
graphs deteriorates the performance of neighbor-aggregation based graph models.
To this end, we propose SUPA, a novel graph neural network for dynamic
multiplex heterogeneous graphs. Compared to neighbor-aggregation architecture,
SUPA develops a sample-update-propagate architecture to alleviate neighborhood
disturbance. Specifically, for each new edge, SUPA samples an influenced
subgraph, updates the representations of the two interactive nodes, and
propagates the interaction information to the sampled subgraph. Furthermore, to
train SUPA incrementally online, we propose InsLearn, an efficient workflow for
single-pass training of large dynamic graphs. Extensive experimental results on
six real-world datasets show that SUPA has a good generalization ability and is
superior to sixteen state-of-the-art baseline methods. The source code is
available at https://github.com/shatter15/SUPA.