Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Structural Generalizability: The Case of Similarity Search   Graph similarity search algorithms usually leverage the structural properties
of a database. Hence, these algorithms are effective only on some structural
variations of the data and are ineffective on other forms, which makes them
hard to use. Ideally, one would like to design a data analytics algorithm that
is structurally robust, i.e., it returns essentially the same accurate results
over all possible structural variations of a dataset. We propose a novel
approach to create a structurally robust similarity search algorithm over graph
databases. We leverage the classic insight in the database literature that
schematic variations are caused by having constraints in the database. We then
present RelSim algorithm which is provably structurally robust under these
variations. Our empirical studies show that our proposed algorithms are
structurally robust while being efficient and as effective as or more effective
than the state-of-the-art similarity search algorithms.

26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math
4
The categories of this paper are: cond-mat
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc