1 research outputs found
Hierarchical Large-scale Graph Similarity Computation via Graph Coarsening and Matching
In this work, we focus on large graph similarity computation problem and
propose a novel "embedding-coarsening-matching" learning framework, which
outperforms state-of-the-art methods in this task and has significant
improvement in time efficiency. Graph similarity computation for metrics such
as Graph Edit Distance (GED) is typically NP-hard, and existing
heuristics-based algorithms usually achieves a unsatisfactory trade-off between
accuracy and efficiency. Recently the development of deep learning techniques
provides a promising solution for this problem by a data-driven approach which
trains a network to encode graphs to their own feature vectors and computes
similarity based on feature vectors. These deep-learning methods can be
classified to two categories, embedding models and matching models. Embedding
models such as GCN-Mean and GCN-Max, which directly map graphs to respective
feature vectors, run faster but the performance is usually poor due to the lack
of interactions across graphs. Matching models such as GMN, whose encoding
process involves interaction across the two graphs, are more accurate but
interaction between whole graphs brings a significant increase in time
consumption (at least quadratic time complexity over number of nodes). Inspired
by large biological molecular identification where the whole molecular is first
mapped to functional groups and then identified based on these functional
groups, our "embedding-coarsening-matching" learning framework first embeds and
coarsens large graphs to coarsened graphs with denser local topology and then
matching mechanism is deployed on the coarsened graphs for the final similarity
scores. Detailed experiments have been conducted and the results demonstrate
the efficiency and effectiveness of our proposed framework