Recent Link Classification on Temporal Graphs Using Graph Profiler
Abstract: The performance of Temporal Graph Learning (TGL) methods are typically evaluated on the future link prediction task, i.e., whether two nodes will get connected and dynamic node classification task, i.e., whether a node's class will change. Comparatively, recent link classification, i.e., to what class an emerging edge belongs to, is investigated much less even though it exists in many industrial settings. In this work, we first formalize recent link classification on temporal graphs as a benchmark downstream task and introduce corresponding benchmark datasets. Secondly, we evaluate the performance of state-of-the-art methods with a statistically meaningful metric Matthews Correlation Coefficient, which is more robust to imbalanced datasets, in addition to the commonly used average precision and area under the curve. We propose several design principles for tailoring models to specific requirements of the task and the dataset including modifications on message aggregation schema, readout layer and time encoding strategy which obtain significant improvement on benchmark datasets. Finally, we propose an architecture that we call Graph Profiler, which is capable of encoding previous events' class information on source and destination nodes. The experiments show that our proposed model achieves an improved Matthews Correlation Coefficient on most cases under interest. We believe the introduction of recent link classification as a benchmark task for temporal graph learning will be useful for the evaluation of prospective methods within the field.
Submission Length: Long submission (more than 12 pages of main content)
Changes Since Last Submission: Added another baseline as per our reviewer's suggestion.
Assigned Action Editor: ~Yan_Liu1
Submission Number: 2065
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