@inproceedings{10c89337e34c4fecb102187382e2571c,
title = "Comparing Massive Networks via Moment Matrices",
abstract = "In this paper, a novel similarity measure for comparing massive complex networks based on moment matrices is proposed. We consider the corresponding adjacency matrix of a graph as a real random variable of the algebraic probability space with a state. It is shown that the spectral distribution of the matrix can be expressed as a unique discrete probability measure. Then we use the geodesic distance between positive definite moment matrices for comparing massive networks. It is proved that this distance is graph invariant and sub-structure invariant. Numerical simulations demonstrate that the proposed method outperforms state-of-art method in collaboration network classification and its computational cost is extremely cheap.",
author = "Hayoung Choi and Yifei Shen and Yuanming Shi",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Symposium on Information Theory, ISIT 2018 ; Conference date: 17-06-2018 Through 22-06-2018",
year = "2018",
month = aug,
day = "15",
doi = "10.1109/ISIT.2018.8437785",
language = "English",
isbn = "9781538647806",
series = "IEEE International Symposium on Information Theory - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "656--660",
booktitle = "2018 IEEE International Symposium on Information Theory, ISIT 2018",
address = "United States",
}