  Many real-world datasets have an underlying dynamic graph structure, where
entities and their interactions evolve over time. Machine learning models
should consider these dynamics in order to harness their full potential in
downstream tasks. Previous approaches for graph representation learning have
focused on either sampling k-hop neighborhoods, akin to breadth-first search,
or random walks, akin to depth-first search. However, these methods are
computationally expensive and unsuitable for real-time, low-latency inference
on dynamic graphs. To overcome these limitations, we propose graph-sprints a
general purpose feature extraction framework for continuous-time-dynamic-graphs
(CTDGs) that has low latency and is competitive with state-of-the-art, higher
latency models. To achieve this, a streaming, low latency approximation to the
random-walk based features is proposed. In our framework, time-aware node
embeddings summarizing multi-hop information are computed using only single-hop
operations on the incoming edges. We evaluate our proposed approach on three
open-source datasets and two in-house datasets, and compare with three
state-of-the-art algorithms (TGN-attn, TGN-ID, Jodie). We demonstrate that our
graph-sprints features, combined with a machine learning classifier, achieve
competitive performance (outperforming all baselines for the node
classification tasks in five datasets). Simultaneously, graph-sprints
significantly reduce inference latencies, achieving close to an order of
magnitude speed-up in our experimental setting.
