@inproceedings{8bfef70b1cf3489baf9337c001c160f0,

title = "Comparative network analysis using KronFit",

abstract = "Comparative network analysis is an emerging line of research that provides insights into the structure and dynamics of networks by finding similarities and discrepancies in their topologies. Unfortunately, comparing networks directly is not feasible on large scales. Existing works resort to representing networks with vectors of features extracted from their topologies and employ various distance metrics to compare between these feature vectors. In this paper, instead of relying on feature vectors to represent the studied networks, we suggest fitting a network model (such as Kronecker Graph) to encode the network structure. We present the directed fitting-distance measure, where the distance from a network A to another network B is captured by the quality of B{\textquoteright}s fit to the model derived from A. Evaluation on five classes of real networks shows that KronFit based distances perform surprisingly well.",

keywords = "Comparative analysis, Complex networks, Distance metrics, Generative models",

author = "Gupta Sukrit and Puzis Rami and Kilimnik Konstantin",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 7th Workshop on Complex Networks CompleNet, 2016 ; Conference date: 23-03-2016 Through 25-03-2016",

year = "2016",

month = jan,

day = "1",

doi = "10.1007/978-3-319-30569-1_28",

language = "English",

isbn = "9783319305684",

series = "Studies in Computational Intelligence",

publisher = "Springer Verlag",

pages = "363--375",

editor = "Hocine Cherifi and Bruno Goncalves and Ronaldo Menezes and Roberta Sinatra",

booktitle = "Complex Networks VII - Proceedings of the 7th Workshop on Complex Networks CompleNet 2016",

address = "Germany",

}