Abstract
Graph alignment in two correlated random graphs refers to the task of identifying the correspondence between vertex sets of the graphs. Recent results have characterized the exact informationtheoretic threshold for graph alignment in correlated Erdos-Rényi graphs. However, very little is known about the existence of efficient algorithms to achieve graph alignment without seeds. In this work we identify a region in which a straightforward O(n11/5 logn)-time canonical labeling algorithm, initially introduced in the context of graph isomorphism, succeeds in aligning correlated Erd?s-Rényi graphs. The algorithm has two steps. In the first step, all vertices are labeled by their degrees and a trivial minimum distance alignment (i.e., sorting vertices according to their degrees) matches a fixed number of highest degree vertices in the two graphs. Having identified this subset of vertices, the remaining vertices are matched using a alignment algorithm for bipartite graphs.
| Original language | English (US) |
|---|---|
| Title of host publication | SIGMETRICS Performance 2019 - Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 97-98 |
| Number of pages | 2 |
| ISBN (Electronic) | 9781450366786 |
| DOIs | |
| State | Published - Jun 20 2019 |
| Event | 14th Joint Conference of International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2019 and IFIP Performance Conference 2019, SIGMETRICS/Performance 2019 - Phoenix, United States Duration: Jun 24 2019 → Jun 28 2019 |
Publication series
| Name | SIGMETRICS Performance 2019 - Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems |
|---|
Conference
| Conference | 14th Joint Conference of International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2019 and IFIP Performance Conference 2019, SIGMETRICS/Performance 2019 |
|---|---|
| Country | United States |
| City | Phoenix |
| Period | 6/24/19 → 6/28/19 |
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All Science Journal Classification (ASJC) codes
- Hardware and Architecture
- Computer Networks and Communications
- Computational Theory and Mathematics
Cite this
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Analysis of a canonical labeling algorithm for the alignment of correlated Erdos-Rényi graphs. / Dai, Osman Emre; Cullina, Daniel; Kiyavash, Negar; Grossglauser, Matthias.
SIGMETRICS Performance 2019 - Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems. Association for Computing Machinery, Inc, 2019. p. 97-98 (SIGMETRICS Performance 2019 - Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Analysis of a canonical labeling algorithm for the alignment of correlated Erdos-Rényi graphs
AU - Dai, Osman Emre
AU - Cullina, Daniel
AU - Kiyavash, Negar
AU - Grossglauser, Matthias
PY - 2019/6/20
Y1 - 2019/6/20
N2 - Graph alignment in two correlated random graphs refers to the task of identifying the correspondence between vertex sets of the graphs. Recent results have characterized the exact informationtheoretic threshold for graph alignment in correlated Erdos-Rényi graphs. However, very little is known about the existence of efficient algorithms to achieve graph alignment without seeds. In this work we identify a region in which a straightforward O(n11/5 logn)-time canonical labeling algorithm, initially introduced in the context of graph isomorphism, succeeds in aligning correlated Erd?s-Rényi graphs. The algorithm has two steps. In the first step, all vertices are labeled by their degrees and a trivial minimum distance alignment (i.e., sorting vertices according to their degrees) matches a fixed number of highest degree vertices in the two graphs. Having identified this subset of vertices, the remaining vertices are matched using a alignment algorithm for bipartite graphs.
AB - Graph alignment in two correlated random graphs refers to the task of identifying the correspondence between vertex sets of the graphs. Recent results have characterized the exact informationtheoretic threshold for graph alignment in correlated Erdos-Rényi graphs. However, very little is known about the existence of efficient algorithms to achieve graph alignment without seeds. In this work we identify a region in which a straightforward O(n11/5 logn)-time canonical labeling algorithm, initially introduced in the context of graph isomorphism, succeeds in aligning correlated Erd?s-Rényi graphs. The algorithm has two steps. In the first step, all vertices are labeled by their degrees and a trivial minimum distance alignment (i.e., sorting vertices according to their degrees) matches a fixed number of highest degree vertices in the two graphs. Having identified this subset of vertices, the remaining vertices are matched using a alignment algorithm for bipartite graphs.
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UR - http://www.scopus.com/inward/citedby.url?scp=85069147139&partnerID=8YFLogxK
U2 - 10.1145/3309697.3331505
DO - 10.1145/3309697.3331505
M3 - Conference contribution
AN - SCOPUS:85069147139
T3 - SIGMETRICS Performance 2019 - Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems
SP - 97
EP - 98
BT - SIGMETRICS Performance 2019 - Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems
PB - Association for Computing Machinery, Inc
ER -