Dynamic link prediction is an important problem considered by many recent
works proposing various approaches for learning temporal edge patterns. To
assess their efficacy, models are evaluated on publicly available benchmark
datasets involving continuous-time and discrete-time temporal graphs. However,
as we show in this work, the suitability of common batch-oriented evaluation
depends on the datasets' characteristics, which can cause two issues: First,
for continuous-time temporal graphs, fixed-size batches create time windows
with different durations, resulting in an inconsistent dynamic link prediction
task. Second, for discrete-time temporal graphs, the sequence of batches can
additionally introduce temporal dependencies that are not present in the data.
In this work, we empirically show that this common evaluation approach leads to
skewed model performance and hinders the fair comparison of methods. We
mitigate this problem by reformulating dynamic link prediction as a link
forecasting task that better accounts for temporal information present in the
data. We provide implementations of our new evaluation method for commonly used
graph learning frameworks.

本研究通过重新构建动态连接预测作为链接预测任务，更好地考虑了数据中的时间信息，解决了常见评估方法引起的模型性能不准确和方法公平比较困难的问题。