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.

提出了一种结构敏感的图形字典嵌入 (SS-GDE) 框架，并使用 Bernoulli 抽样来调整基本图形键的子结构以适应每个输入。为了使跨图测量敏感并保持稳定，提出了多敏感性 Wasserstein 编码，通过设计多尺度注意力来产生嵌入。在多个图形分类数据集上表明了方法的优越性。