  Recently, more and more research has focused on using Graph Neural Networks
(GNN) to solve the Graph Similarity Computation problem (GSC), i.e., computing
the Graph Edit Distance (GED) between two graphs. These methods treat GSC as an
end-to-end learnable task, and the core of their architecture is the feature
fusion modules to interact with the features of two graphs. Existing methods
consider that graph-level embedding is difficult to capture the differences in
local small structures between two graphs, and thus perform fine-grained
feature fusion on node-level embedding can improve the accuracy, but leads to
greater time and memory consumption in the training and inference phases.
However, this paper proposes a novel graph-level fusion module Different
Attention (DiffAtt), and demonstrates that graph-level fusion embeddings can
substantially outperform these complex node-level fusion embeddings. We posit
that the relative difference structure of the two graphs plays an important
role in calculating their GED values. To this end, DiffAtt uses the difference
between two graph-level embeddings as an attentional mechanism to capture the
graph structural difference of the two graphs. Based on DiffAtt, a new GSC
method, named Graph Edit Distance Learning via Different Attention (REDRAFT),
is proposed, and experimental results demonstrate that REDRAFT achieves
state-of-the-art performance in 23 out of 25 metrics in five benchmark
datasets. Especially on MSE, it respectively outperforms the second best by
19.9%, 48.8%, 29.1%, 31.6%, and 2.2%. Moreover, we propose a quantitative test
Remaining Subgraph Alignment Test (RESAT) to verify that among all graph-level
fusion modules, the fusion embedding generated by DiffAtt can best capture the
structural differences between two graphs.
