Authors:
Fan Wang
1
;
Weiren Yu
2
;
Hai Wang
1
and
Victor Chang
1
Affiliations:
1
Aston University, Birmingham B4 7ET, U.K.
;
2
University of Warwick CV4 7AL, U.K.
Keyword(s):
Web Search, Similarity Search, Link Analysis.
Abstract:
Role-based similarity search, predicated on the topological structure of graphs, is a highly effective and widely
applicable technique for various real-world information extraction applications. Although the prominent rolebased similarity algorithm, RoleSim, successfully provides the automorphic (role) equivalence of similarity
between pairs of nodes, it does not effectively differentiate nodes that exhibit exact automorphic equivalence
but differ in terms of structural equivalence within a given graph. This limitation arises from disregarding most
adjacency similarity information between pairs of nodes during the RoleSim computation. To address this
research gap, we propose a novel single-source role similarity search algorithm, named FaRS, which employs
the top Γ maximum similarity matching technique to capture more information from the classes of neighboring
nodes, ensuring both automorphic equivalence and structural equivalence of role similarity. Furthermore, we
establi
sh the convergence of FaRS and demonstrate its adherence to various axioms, including uniqueness,
symmetry, boundedness, and triangular inequality. Additionally, we introduce the Opt FaRS algorithm, which
optimizes the computation of FaRS through two acceleration components: path extraction tracking and precomputation (P-speedup and Out-speedup approach). Experimental results on real datasets demonstrate that
FaRS and Opt FaRS outperform baseline algorithms in terms of both accuracy and efficiency.
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