Abstract

Understanding how a given pair of graphs align with each other (also known as the graph matching problem) is a critical task in many search, classification, and analysis applications. Unfortunately, the problem of maximum common subgraph isomorphism between two graphs is a well known NP-hard problem, rendering it impractical to search for exact graph alignments. While there are several heuristics, most of these analyze and encode global and local structural information for every node of the graph and then rank pairs of nodes across the two graphs based on their structural similarities. Moreover, many algorithms involve a post-processing (or refinement) step which aims to improve the initial matching accuracy. In this paper1 we note that the expensive refinement phase of graph matching algorithms is not practical in any application where scalability is critical. It is also impractical to seek structural similarity between all pairs of nodes. We argue that a more practical and scalable solution is to seek structural keynodes of the input graphs that can be used to limit the amount of time needed to search for alignments. Naturally, these keynodes need to be selected carefully to prevent any degradations in accuracy during the alignment process. Given this motivation, in this paper, we first present a structural keynode extraction (SKE) algorithm and then use structural keynodes obtained during off-line processing for keynode-driven scalable graph matching (KSGM). Experiments show that the proposed keynode-driven scalable graph matching algorithms produce alignments that are as accurate as (or better than) the state-of-the-art algorithms, with significantly faster online executions.

Original languageEnglish (US)
Title of host publicationInternational Conference on Information and Knowledge Management, Proceedings
PublisherAssociation for Computing Machinery
Pages1101-1110
Number of pages10
Volume19-23-Oct-2015
ISBN (Print)9781450337946
DOIs
StatePublished - Oct 17 2015
Event24th ACM International Conference on Information and Knowledge Management, CIKM 2015 - Melbourne, Australia
Duration: Oct 19 2015Oct 23 2015

Other

Other24th ACM International Conference on Information and Knowledge Management, CIKM 2015
CountryAustralia
CityMelbourne
Period10/19/1510/23/15

Fingerprint

Graph
Alignment
Node
Isomorphism
Experiment
NP-hard
Matching problem
Heuristics
Degradation
Scalability

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

Cite this

Chen, X., Candan, K., Sapino, M. L., & Shakarian, P. (2015). KSGM: Keynode-driven scalable graph matching. In International Conference on Information and Knowledge Management, Proceedings (Vol. 19-23-Oct-2015, pp. 1101-1110). Association for Computing Machinery. https://doi.org/10.1145/2806416.2806577

KSGM : Keynode-driven scalable graph matching. / Chen, Xilun; Candan, Kasim; Sapino, Maria Luisa; Shakarian, Paulo.

International Conference on Information and Knowledge Management, Proceedings. Vol. 19-23-Oct-2015 Association for Computing Machinery, 2015. p. 1101-1110.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Chen, X, Candan, K, Sapino, ML & Shakarian, P 2015, KSGM: Keynode-driven scalable graph matching. in International Conference on Information and Knowledge Management, Proceedings. vol. 19-23-Oct-2015, Association for Computing Machinery, pp. 1101-1110, 24th ACM International Conference on Information and Knowledge Management, CIKM 2015, Melbourne, Australia, 10/19/15. https://doi.org/10.1145/2806416.2806577
Chen X, Candan K, Sapino ML, Shakarian P. KSGM: Keynode-driven scalable graph matching. In International Conference on Information and Knowledge Management, Proceedings. Vol. 19-23-Oct-2015. Association for Computing Machinery. 2015. p. 1101-1110 https://doi.org/10.1145/2806416.2806577
Chen, Xilun ; Candan, Kasim ; Sapino, Maria Luisa ; Shakarian, Paulo. / KSGM : Keynode-driven scalable graph matching. International Conference on Information and Knowledge Management, Proceedings. Vol. 19-23-Oct-2015 Association for Computing Machinery, 2015. pp. 1101-1110
@inproceedings{65b8f3799132426bb46973bef01d8b96,
title = "KSGM: Keynode-driven scalable graph matching",
abstract = "Understanding how a given pair of graphs align with each other (also known as the graph matching problem) is a critical task in many search, classification, and analysis applications. Unfortunately, the problem of maximum common subgraph isomorphism between two graphs is a well known NP-hard problem, rendering it impractical to search for exact graph alignments. While there are several heuristics, most of these analyze and encode global and local structural information for every node of the graph and then rank pairs of nodes across the two graphs based on their structural similarities. Moreover, many algorithms involve a post-processing (or refinement) step which aims to improve the initial matching accuracy. In this paper1 we note that the expensive refinement phase of graph matching algorithms is not practical in any application where scalability is critical. It is also impractical to seek structural similarity between all pairs of nodes. We argue that a more practical and scalable solution is to seek structural keynodes of the input graphs that can be used to limit the amount of time needed to search for alignments. Naturally, these keynodes need to be selected carefully to prevent any degradations in accuracy during the alignment process. Given this motivation, in this paper, we first present a structural keynode extraction (SKE) algorithm and then use structural keynodes obtained during off-line processing for keynode-driven scalable graph matching (KSGM). Experiments show that the proposed keynode-driven scalable graph matching algorithms produce alignments that are as accurate as (or better than) the state-of-the-art algorithms, with significantly faster online executions.",
author = "Xilun Chen and Kasim Candan and Sapino, {Maria Luisa} and Paulo Shakarian",
year = "2015",
month = "10",
day = "17",
doi = "10.1145/2806416.2806577",
language = "English (US)",
isbn = "9781450337946",
volume = "19-23-Oct-2015",
pages = "1101--1110",
booktitle = "International Conference on Information and Knowledge Management, Proceedings",
publisher = "Association for Computing Machinery",

}

TY - GEN

T1 - KSGM

T2 - Keynode-driven scalable graph matching

AU - Chen, Xilun

AU - Candan, Kasim

AU - Sapino, Maria Luisa

AU - Shakarian, Paulo

PY - 2015/10/17

Y1 - 2015/10/17

N2 - Understanding how a given pair of graphs align with each other (also known as the graph matching problem) is a critical task in many search, classification, and analysis applications. Unfortunately, the problem of maximum common subgraph isomorphism between two graphs is a well known NP-hard problem, rendering it impractical to search for exact graph alignments. While there are several heuristics, most of these analyze and encode global and local structural information for every node of the graph and then rank pairs of nodes across the two graphs based on their structural similarities. Moreover, many algorithms involve a post-processing (or refinement) step which aims to improve the initial matching accuracy. In this paper1 we note that the expensive refinement phase of graph matching algorithms is not practical in any application where scalability is critical. It is also impractical to seek structural similarity between all pairs of nodes. We argue that a more practical and scalable solution is to seek structural keynodes of the input graphs that can be used to limit the amount of time needed to search for alignments. Naturally, these keynodes need to be selected carefully to prevent any degradations in accuracy during the alignment process. Given this motivation, in this paper, we first present a structural keynode extraction (SKE) algorithm and then use structural keynodes obtained during off-line processing for keynode-driven scalable graph matching (KSGM). Experiments show that the proposed keynode-driven scalable graph matching algorithms produce alignments that are as accurate as (or better than) the state-of-the-art algorithms, with significantly faster online executions.

AB - Understanding how a given pair of graphs align with each other (also known as the graph matching problem) is a critical task in many search, classification, and analysis applications. Unfortunately, the problem of maximum common subgraph isomorphism between two graphs is a well known NP-hard problem, rendering it impractical to search for exact graph alignments. While there are several heuristics, most of these analyze and encode global and local structural information for every node of the graph and then rank pairs of nodes across the two graphs based on their structural similarities. Moreover, many algorithms involve a post-processing (or refinement) step which aims to improve the initial matching accuracy. In this paper1 we note that the expensive refinement phase of graph matching algorithms is not practical in any application where scalability is critical. It is also impractical to seek structural similarity between all pairs of nodes. We argue that a more practical and scalable solution is to seek structural keynodes of the input graphs that can be used to limit the amount of time needed to search for alignments. Naturally, these keynodes need to be selected carefully to prevent any degradations in accuracy during the alignment process. Given this motivation, in this paper, we first present a structural keynode extraction (SKE) algorithm and then use structural keynodes obtained during off-line processing for keynode-driven scalable graph matching (KSGM). Experiments show that the proposed keynode-driven scalable graph matching algorithms produce alignments that are as accurate as (or better than) the state-of-the-art algorithms, with significantly faster online executions.

UR - http://www.scopus.com/inward/record.url?scp=84958244969&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84958244969&partnerID=8YFLogxK

U2 - 10.1145/2806416.2806577

DO - 10.1145/2806416.2806577

M3 - Conference contribution

SN - 9781450337946

VL - 19-23-Oct-2015

SP - 1101

EP - 1110

BT - International Conference on Information and Knowledge Management, Proceedings

PB - Association for Computing Machinery

ER -