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» On Randomness Measures for Social Networks.
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SOCIALCOM
2010
13 years 5 months ago
Measuring Similarity between Sets of Overlapping Clusters
The typical task of unsupervised learning is to organize data, for example into clusters, typically disjoint clusters (eg. the K-means algorithm). One would expect (for example) a...
Mark K. Goldberg, Mykola Hayvanovych, Malik Magdon...
WWW
2011
ACM
13 years 2 months ago
Estimating sizes of social networks via biased sampling
Online social networks have become very popular in recent years and their number of users is already measured in many hundreds of millions. For various commercial and sociological...
Liran Katzir, Edo Liberty, Oren Somekh
ISAAC
2007
Springer
102views Algorithms» more  ISAAC 2007»
14 years 1 months ago
Depth of Field and Cautious-Greedy Routing in Social Networks
Social networks support efficient decentralized search: people can collectively construct short paths to a specified target in the network. Rank-based friendship—where the prob...
David Barbella, George Kachergis, David Liben-Nowe...
SDM
2010
SIAM
115views Data Mining» more  SDM 2010»
13 years 8 months ago
Reconstructing Randomized Social Networks
In social networks, nodes correspond to entities and edges to links between them. In most of the cases, nodes are also associated with a set of features. Noise, missing values or ...
Niko Vuokko, Evimaria Terzi
SDM
2008
SIAM
164views Data Mining» more  SDM 2008»
13 years 8 months ago
Randomizing Social Networks: a Spectrum Preserving Approach
Understanding the general properties of real social networks has gained much attention due to the proliferation of networked data. The nodes in the network are the individuals and...
Xiaowei Ying, Xintao Wu