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Learning representation for graph classification turns a variable-size graphinto a fixed-size vector (or matrix). Such a representation works nicely withalgebraic manipulations. Here we introduce a simple method to augment anattributed graph with a virtual node that is bidirectionally connected to allexisting nodes. The virtual node represents the latent aspects of the graph,which are not immediately available from the attributes and local connectivitystructures. The expanded graph is then put through any node representationmethod. The representation of the virtual node is then the representation ofthe entire graph. In this paper, we use the recently introduced Column Networkfor the expanded graph, resulting in a new end-to-end graph classificationmodel dubbed Virtual Column Network (VCN). The model is validated on two tasks:(i) predicting bio-activity of chemical compounds, and (ii) finding softwarevulnerability from source code. Results demonstrate that VCN is competitiveagainst well-established rivals.

Graph Classification via Deep Learning with Virtual Nodes