Open AccessProceedings Article
Randomization Techniques for Graphs
Sami Hanhijärvi,Gemma C. Garriga,Kai Puolamäki +2 more
- pp 780-791
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TLDR
This paper focuses on randomization techniques for unweighted undirected graphs for graph mining within the framework of statistical hypothesis testing, and describes three alternative algorithms based on local edge swapping and Metropolis sampling.Abstract:
Mining graph data is an active research area Several data mining methods and algorithms have been proposed to identify structures from graphs; still, the evaluation of those results is lacking Within the framework of statistical hypothesis testing, we focus in this paper on randomization techniques for unweighted undirected graphs Randomization is an important approach to assess the statistical significance of data mining results Given an input graph, our randomization method will sample data from the class of graphs that share certain structural properties with the input graph Here we describe three alternative algorithms based on local edge swapping and Metropolis sampling We test our framework with various graph data sets and mining algorithms for two applications, namely graph clustering and frequent subgraph miningread more
Citations
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Systematic integration of biomedical knowledge prioritizes drugs for repurposing.
Daniel Himmelstein,Antoine Lizee,Christine Hessler,Leo Brueggeman,Leo Brueggeman,Sabrina L Chen,Sabrina L Chen,Dexter Hadley,Ari J. Green,Pouya Khankhanian,Pouya Khankhanian,Sergio E. Baranzini +11 more
TL;DR: In this article, an integrative network encoding knowledge from millions of biomedical studies is used to predict whether a compound treats a disease and improve the economy and success rate of drug approval.
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Social Network Analysis and Mining for Business Applications
TL;DR: The main contribution of this article is to provide a state-of-the-art overview of current techniques while providing a critical perspective on business applications of social network analysis and mining.
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A Survey of Privacy-Preservation of Graphs and Social Networks
TL;DR: This chapter surveys the very recent research development on privacy preserving publishing of graphs and social network data, and categorizes the state-of-the-art anonymization methods on simple graphs in three main categories: K-anonymity based privacy Preservation via edge modification, probabilistic privacy preservation via edge randomization, and privacy preservation through generalization.
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Systematic integration of biomedical knowledge prioritizes drugs for repurposing
Daniel Himmelstein,Antoine Lizee,Christine Hessler,Leo Brueggeman,Sabrina L Chen,Dexter Hadley,Ari J. Green,Pouya Khankhanian,Sergio E. Baranzini +8 more
TL;DR: The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval, and help prioritize drug repurposing candidates is described.
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Anonymization of Centralized and Distributed Social Networks by Sequential Clustering
Tamir Tassa,D. J. Cohen +1 more
TL;DR: This work considers the distributed setting in which the network data is split between several data holders and offers two variants of an anonymization algorithm which is based on sequential clustering (Sq), which significantly outperform the SaNGreeA algorithm due to Campan and Truta.
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