NeutronStream: A Dynamic GNN Training Framework with Sliding Window for
  Graph Streams
Chaoyi Chen Dechao Gao Yanfeng Zhang Qiange Wang Zhenbo Fu Xuecang Zhang Junhua Zhu Yu Gu Ge Yu
Existing Graph Neural Network (GNN) training frameworks have been designed to
help developers easily create performant GNN implementations. However, most
existing GNN frameworks assume that the input graphs are static, but ignore
that most real-world graphs are constantly evolving. Though many dynamic GNN
models have emerged to learn from evolving graphs, the training process of
these dynamic GNNs is dramatically different from traditional GNNs in that it
captures both the spatial and temporal dependencies of graph updates. This
poses new challenges for designing dynamic GNN training frameworks. First, the
traditional batched training method fails to capture real-time structural
evolution information. Second, the time-dependent nature makes parallel
training hard to design. Third, it lacks system supports for users to
efficiently implement dynamic GNNs. In this paper, we present NeutronStream, a
framework for training dynamic GNN models. NeutronStream abstracts the input
dynamic graph into a chronologically updated stream of events and processes the
stream with an optimized sliding window to incrementally capture the
spatial-temporal dependencies of events. Furthermore, NeutronStream provides a
parallel execution engine to tackle the sequential event processing challenge
to achieve high performance. NeutronStream also integrates a built-in graph
storage structure that supports dynamic updates and provides a set of
easy-to-use APIs that allow users to express their dynamic GNNs. Our
experimental results demonstrate that, compared to state-of-the-art dynamic GNN
implementations, NeutronStream achieves speedups ranging from 1.48X to 5.87X
and an average accuracy improvement of 3.97%.