Dynamical Graph Echo State Networks with Snapshot Merging for
  Dissemination Process Classification
Ziqiang Li Kantaro Fujiwara Gouhei Tanaka
The Dissemination Process Classification (DPC) is a popular application of
temporal graph classification. The aim of DPC is to classify different
spreading patterns of information or pestilence within a community represented
by discrete-time temporal graphs. Recently, a reservoir computing-based model
named Dynamical Graph Echo State Network (DynGESN) has been proposed for
processing temporal graphs with relatively high effectiveness and low
computational costs. In this study, we propose a novel model which combines a
novel data augmentation strategy called snapshot merging with the DynGESN for
dealing with DPC tasks. In our model, the snapshot merging strategy is designed
for forming new snapshots by merging neighboring snapshots over time, and then
multiple reservoir encoders are set for capturing spatiotemporal features from
merged snapshots. After those, the logistic regression is adopted for decoding
the sum-pooled embeddings into the classification results. Experimental results
on six benchmark DPC datasets show that our proposed model has better
classification performances than the DynGESN and several kernel-based models.