Instance-Based Learning Algorithms
Machine Learning
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
The Two-Point Correlation Function: A Measure of Interclass Separability
Journal of Mathematical Imaging and Vision
The dynamics of viral marketing
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Statistical properties of community structure in large social and information networks
Proceedings of the 17th international conference on World Wide Web
Graph Clustering Via a Discrete Uncoupling Process
SIAM Journal on Matrix Analysis and Applications
Pattern Recognition, Fourth Edition
Pattern Recognition, Fourth Edition
You are who you know: inferring user profiles in online social networks
Proceedings of the third ACM international conference on Web search and data mining
Empirical comparison of algorithms for network community detection
Proceedings of the 19th international conference on World wide web
An overview of statistical learning theory
IEEE Transactions on Neural Networks
On community detection in real-world networks and the importance of degree assortativity
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Overlapping community detection using seed set expansion
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
A separability framework for analyzing community structure
ACM Transactions on Knowledge Discovery from Data (TKDD) - Casin special issue
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Three major factors govern the intricacies of community extraction in networks: (1) the application domain includes a wide variety of networks of fundamentally different natures, (2) the literature offers a multitude of disparate community detection algorithms, and (3) there is no consensus characterizing how to discriminate communities from non-communities. In this paper, we present a comprehensive analysis of community properties through a class separability framework. Our approach enables the assessement of the structural dissimilarity among the output of multiple community detection algorithms and between the output of algorithms and communities that arise in practice. To demostrate this concept, we furnish our method with a large set of structural properties and multiple community detection algorithms. Applied to a diverse collection of large scale network datasets, the analysis reveals that (1) the different detection algorithms extract fundamentally different structures; (2) the structure of communities that arise in practice is closest to that of communities that random-walk-based algorithms extract, although still siginificantly different from that of the output of all the algorithms; and (3) a small subset of the properties are nearly as discriminative as the full set, while making explicit the ways in which the algorithms produce biases. Our framework enables an informed choice of the most suitable community detection method for a given purpose and network and allows for a comparison of existing community detection algorithms while guiding the design of new ones.