Theory refinement on Bayesian networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
A Bayesian method for constructing Bayesian belief networks from databases
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining complex models from arbitrarily large databases in constant time
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An accelerated Chow and Liu algorithm: fitting tree distributions to high dimensional sparse data
An accelerated Chow and Liu algorithm: fitting tree distributions to high dimensional sparse data
Statistical mechanics of complex networks
Statistical mechanics of complex networks
Beyond Independence: Probabilistic Models for Query Approximation on Binary Transaction Data
IEEE Transactions on Knowledge and Data Engineering
Tractable learning of large Bayes net structures from sparse data
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
A Survey of Statistical Network Models
Foundations and Trends® in Machine Learning
A method for measuring co-authorship relationships in MediaWiki
WikiSym '08 Proceedings of the 4th International Symposium on Wikis
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Improvements in data collection and the birth of online communities made it possible to obtain very large social networks (graphs). Several communities have been involved in modeling and analyzing these graphs. Usage of graphical models, such as Bayesian Networks (BN), to analyze massive data has become increasingly popular, due to their scalability and robustness to noise. In the literature BNs are primarily used for compact representation of joint distributions and to perform inference, i.e. answer queries about the data. In this work we learn Bayes Nets using the previously proposed SBNS algorithm [14]. We look at the learned networks for the purpose of analyzing the graph structure itself. We also point out a few improvements over the SBNS algorithm. The usefulness of Bayes Net structures to understand social networks is an open area. We discuss possible interpretations using a small subgraph of the Medline publications and hope to provoke some discussion and interest in further analysis.