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The Dissemination Process Classification (DPC) is a popular application oftemporal graph classification. The aim of DPC is to classify differentspreading patterns of information or pestilence within a community representedby discrete-time temporal graphs. Recently, a reservoir computing-based modelnamed Dynamical Graph Echo State Network (DynGESN) has been proposed forprocessing temporal graphs with relatively high effectiveness and lowcomputational costs. In this study, we propose a novel model which combines anovel data augmentation strategy called snapshot merging with the DynGESN fordealing with DPC tasks. In our model, the snapshot merging strategy is designedfor forming new snapshots by merging neighboring snapshots over time, and thenmultiple reservoir encoders are set for capturing spatiotemporal features frommerged snapshots. After those, the logistic regression is adopted for decodingthe sum-pooled embeddings into the classification results. Experimental resultson six benchmark DPC datasets show that our proposed model has betterclassification performances than the DynGESN and several kernel-based models.

Dynamical Graph Echo State Networks with Snapshot Merging for Dissemination Process Classification