Structure-Sensitive Graph Dictionary Embedding for Graph Classification
Guangbu Liu Tong Zhang Xudong Wang Wenting Zhao Chuanwei Zhou Zhen Cui
Graph structure expression plays a vital role in distinguishing various
graphs. In this work, we propose a Structure-Sensitive Graph Dictionary
Embedding (SS-GDE) framework to transform input graphs into the embedding space
of a graph dictionary for the graph classification task. Instead of a plain use
of a base graph dictionary, we propose the variational graph dictionary
adaptation (VGDA) to generate a personalized dictionary (named adapted graph
dictionary) for catering to each input graph. In particular, for the
adaptation, the Bernoulli sampling is introduced to adjust substructures of
base graph keys according to each input, which increases the expression
capacity of the base dictionary tremendously. To make cross-graph measurement
sensitive as well as stable, multi-sensitivity Wasserstein encoding is proposed
to produce the embeddings by designing multi-scale attention on optimal
transport. To optimize the framework, we introduce mutual information as the
objective, which further deduces to variational inference of the adapted graph
dictionary. We perform our SS-GDE on multiple datasets of graph classification,
and the experimental results demonstrate the effectiveness and superiority over
the state-of-the-art methods.