Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Combinatorial Learning of Graph Edit Distance via Dynamic Embedding   Graph Edit Distance (GED) is a popular similarity measurement for pairwise
graphs and it also refers to the recovery of the edit path from the source
graph to the target graph. Traditional A* algorithm suffers scalability issues
due to its exhaustive nature, whose search heuristics heavily rely on human
prior knowledge. This paper presents a hybrid approach by combing the
interpretability of traditional search-based techniques for producing the edit
path, as well as the efficiency and adaptivity of deep embedding models to
achieve a cost-effective GED solver. Inspired by dynamic programming,
node-level embedding is designated in a dynamic reuse fashion and suboptimal
branches are encouraged to be pruned. To this end, our method can be readily
integrated into A* procedure in a dynamic fashion, as well as significantly
reduce the computational burden with a learned heuristic. Experimental results
on different graph datasets show that our approach can remarkably ease the
search process of A* without sacrificing much accuracy. To our best knowledge,
this work is also the first deep learning-based GED method for recovering the
edit path.

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The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
18
The categories of this paper are: econ