Paper Info
Title
Network similarity via multiple social theories
Abstract
Given a set of k networks, possibly with different sizes and no overlaps in nodes or links, how can we quickly assess similarity between them? Analogously, are there a set of social theories which, when represented by a small number of descriptive, numerical features, effectively serve as a “signature” for the network? Having such signatures will enable a wealth of graph mining and social network analysis tasks, including clustering, outlier detection, visualization, etc. We propose a novel, effective, and scalable method, called NetSimile, for solving the above problem. Our approach has the following desirable properties: (a) It is supported by a set of social theories. (b) It gives similarity scores that are size-invariant. (c) It is scalable, being linear on the number of links for graph signature extraction. In extensive experiments on numerous synthetic and real networks from disparate domains, NetSimile outperforms baseline competitors. We also demonstrate how our approach enables several mining tasks such as clustering, visualization, discontinuity detection, network transfer learning, and re-identification across networks.
Year
DOI
Venue
2013
10.1145/2492517.2492582
Advances in Social Networks Analysis and Mining
Keywords
Field
DocType
outlier detection,discontinuity detection,real network,graph signature extraction,network transfer learning,multiple social theory,social theory,mining task,social network analysis task,k network,network similarity,graph mining,data mining,graph theory
Network science,Graph theory,Dynamic network analysis,Organizational network analysis,Data mining,Computer science,Social network analysis,Geometric networks,Artificial intelligence,Cluster analysis,Graph (abstract data type),Machine learning
Conference
Citations 
PageRank 
References 
20
0.73
8
Authors
4
Name
Order
Citations
PageRank
Michele Berlingerio151028.92
Danai Koutra286847.66
Tina Eliassi-Rad31597108.63
Christos Faloutsos4279724490.38