Graph Edit Distance Computation via Graph Neural Networks
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by
Yunsheng Bai, Hao Ding, Song Bian, Ting Chen, Yizhou Sun, Wei Wang
2018
Abstract
Graph similarity search is among the most important graph-based applications,
e.g. finding the chemical compounds that are most similar to a query compound.
Graph similarity/distance computation, such as Graph Edit Distance (GED) and
Maximum Common Subgraph (MCS), is the core operation of graph similarity search
and many other applications, but very costly to compute in practice. Inspired
by the recent success of neural network approaches to several graph
applications, such as node or graph classification, we propose a novel neural
network based approach to address this classic yet challenging graph problem,
aiming to alleviate the computational burden while preserving a good
performance.
The proposed approach, called SimGNN, combines two strategies. First, we
design a learnable embedding function that maps every graph into an embedding
vector, which provides a global summary of a graph. A novel attention mechanism
is proposed to emphasize the important nodes with respect to a specific
similarity metric. Second, we design a pairwise node comparison method to
supplement the graph-level embeddings with fine-grained node-level information.
Our model can be trained in an end-to-end fashion, achieves better
generalization on unseen graphs, and in the worst case runs in quadratic time
with respect to the number of nodes in two graphs. Taking GED computation as an
example, experimental results on three real graph datasets demonstrate the
effectiveness and efficiency of our approach. Specifically, our model achieves
smaller error rate and great time reduction compared against a series of
baselines, including several approximation algorithms on GED computation, and
many existing graph neural network based models. Our study suggests SimGNN
provides a new direction for future research on graph similarity computation
and graph similarity search.
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