graph2vec: Learning Distributed Representations of Graphs
Table 5: Malware Clustering - Results
['[BOLD] Method', '[BOLD] ARI (as %)']
From the table, the following inferences are drawn: [leftmargin=*] At the outset, we observe all the method obtain lesser ARIs in this experiment, as the malware clustering task is inherently more complex than two classification tasks considered previously. Similar to the classification tasks, both node2vec and sub2vec perform poorer than the kernels and graph2vec. This reinforces the inference that adopting node2vec and sub2vec for graph embedding will yield subpar results. Both WL and Deep WL kernel perform much better than the two aforementioned embedding approaches. However, different from the classification tasks, in this task, the former methods outperform the latter methods by more than 2 folds. In this experiment, graph2vec outperforms all the other compared approaches highly significantly. In particular, it outperforms the substructure embedding techniques by more than 39% and the kernels by more than 5%. This reinforces the findings inferred from the classification experiments.