A Strong Node Classification Baseline for Temporal Graphs
Farimah Poursafaei, Z. Zilic, Reihaneh Rabbany
SDM
Abstract
Many real-world complex systems can be modelled by temporal networks. Representation learning on these networks often captures their dynamic evolution and is a first step for performing further analysis, e.g. node classification. Node classification is a fundamental task for graph analysis in general and in the context of temporal graph, is often employed to categories nodes based on their activity patterns. Analysis of existing real world networks from different high-stake domains reveals that the rate of the malicious activities is on uptick, resulting in catastrophic social or economic consequences. This strongly motivates designing accurate node classification methods for temporal graphs. In this paper, we propose TGBASE, for node classification on weighted temporal networks. TGBASE efficiently extracts key features to consider the structural characteristics of each node and its neighborhood as well as the intensity and timestamp of the interactions among node pairs. These features accurately differentiate different classes of nodes, as shown on eight realworld benchmark datasets, outperforming multiple state-of-theart (SOTA) deep/complex models. Our strong yet simple model is also generic, whereas the SOTA contenders are designed often for their specific (class of) datasets.