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Learning representations for graph-structured data is essential for graphanalytical tasks. While remarkable progress has been made on static graphs,researches on temporal graphs are still in its beginning stage. The bottleneckof the temporal graph representation learning approach is the neighborhoodaggregation strategy, based on which graph attributes share and gatherinformation explicitly. Existing neighborhood aggregation strategies fail tocapture either the short-term features or the long-term features of temporalgraph attributes, leading to unsatisfactory model performance and even poorrobustness and domain generality of the representation learning method. Toaddress this problem, we propose a Frame-level Timeline Modeling (FTM) methodthat helps to capture both short-term and long-term features and thus learnsmore informative representations on temporal graphs. In particular, we presenta novel link-based framing technique to preserve the short-term features andthen incorporate a timeline aggregator module to capture the intrinsic dynamicsof graph evolution as long-term features. Our method can be easily assembledwith most temporal GNNs. Extensive experiments on common datasets show that ourmethod brings great improvements to the capability, robustness, and domaingenerality of backbone methods in downstream tasks. Our code can be found athttps://github.com/yeeeqichen/FTM.

FTM: A Frame-level Timeline Modeling Method for Temporal Graph Representation Learning