TY - GEN
T1 - A guide to selecting a network similarity method
AU - Soundarajan, Sucheta
AU - Eliassi-Rad, Tina
AU - Gallagher, Brian
N1 - Publisher Copyright:
Copyright © SIAM.
PY - 2014
Y1 - 2014
N2 - We consider the problem of determining how similar two networks (without known node-correspondences) are. This problem occurs frequently in real-world applications such as transfer learning and change detection. Many networksimilarity methods exist; and it is unclear how one should select from amongst them. We provide the first empirical study on the relationships between different networksimilarity methods. Specifically, we present (1) an approach for identifying groups of comparable network-similarity methods and (2) an approach for computing the consensus among a given set of network-similarity methods. We compare and contrast twenty network-similarity methods by applying our approaches to a variety of real datasets spanning multiple domains. Our experiments demonstrate that (1) different network-similarity methods are surprisingly well correlated, (2) some complex network-similarity methods can be closely approximated by a much simpler method, and (3) a few network-similarity methods produce rankings that are very close to the consensus ranking.
AB - We consider the problem of determining how similar two networks (without known node-correspondences) are. This problem occurs frequently in real-world applications such as transfer learning and change detection. Many networksimilarity methods exist; and it is unclear how one should select from amongst them. We provide the first empirical study on the relationships between different networksimilarity methods. Specifically, we present (1) an approach for identifying groups of comparable network-similarity methods and (2) an approach for computing the consensus among a given set of network-similarity methods. We compare and contrast twenty network-similarity methods by applying our approaches to a variety of real datasets spanning multiple domains. Our experiments demonstrate that (1) different network-similarity methods are surprisingly well correlated, (2) some complex network-similarity methods can be closely approximated by a much simpler method, and (3) a few network-similarity methods produce rankings that are very close to the consensus ranking.
UR - http://www.scopus.com/inward/record.url?scp=84959911503&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84959911503&partnerID=8YFLogxK
U2 - 10.1137/1.9781611973440.118
DO - 10.1137/1.9781611973440.118
M3 - Conference contribution
AN - SCOPUS:84959911503
T3 - SIAM International Conference on Data Mining 2014, SDM 2014
SP - 1037
EP - 1045
BT - SIAM International Conference on Data Mining 2014, SDM 2014
A2 - Zaki, Mohammed
A2 - Obradovic, Zoran
A2 - Ning-Tan, Pang
A2 - Banerjee, Arindam
A2 - Kamath, Chandrika
A2 - Parthasarathy, Srinivasan
PB - Society for Industrial and Applied Mathematics Publications
T2 - 14th SIAM International Conference on Data Mining, SDM 2014
Y2 - 24 April 2014 through 26 April 2014
ER -