Markov random field modeling in computer vision
Markov random field modeling in computer vision
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting graph-based spatial outliers: algorithms and applications (a summary of results)
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Anomaly Detection over Noisy Data using Learned Probability Distributions
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
SimRank: a measure of structural-context similarity
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Novelty detection: a review—part 1: statistical approaches
Signal Processing
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Semi-Supervised Adapted HMMs for Unusual Event Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Neighborhood Formation and Anomaly Detection in Bipartite Graphs
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Clustering with Bregman Divergences
The Journal of Machine Learning Research
IEEE Transactions on Knowledge and Data Engineering
A probabilistic framework for relational clustering
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
SCAN: a structural clustering algorithm for networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A tutorial on spectral clustering
Statistics and Computing
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
ACM Transactions on Knowledge Discovery from Data (TKDD)
Scalable community discovery on textual data with relations
Proceedings of the 17th ACM conference on Information and knowledge management
Combining link and content for community detection: a discriminative approach
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering Contexts and Contextual Outliers Using Random Walks in Graphs
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Graph-based data warehousing using the core-facets model
ICDM'11 Proceedings of the 11th international conference on Advances in data mining: applications and theoretical aspects
MEI: mutual enhanced infinite generative model for simultaneous community and topic detection
DS'11 Proceedings of the 14th international conference on Discovery science
Integrating community matching and outlier detection for mining evolutionary community outliers
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Community trend outlier detection using soft temporal pattern mining
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
On detecting association-based clique outliers in heterogeneous information networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
A probabilistic graphical model for brand reputation assessment in social networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Data Mining and Knowledge Discovery
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Linked or networked data are ubiquitous in many applications. Examples include web data or hypertext documents connected via hyperlinks, social networks or user profiles connected via friend links, co-authorship and citation information, blog data, movie reviews and so on. In these datasets (called "information networks"), closely related objects that share the same properties or interests form a community. For example, a community in blogsphere could be users mostly interested in cell phone reviews and news. Outlier detection in information networks can reveal important anomalous and interesting behaviors that are not obvious if community information is ignored. An example could be a low-income person being friends with many rich people even though his income is not anomalously low when considered over the entire population. This paper first introduces the concept of community outliers (interesting points or rising stars for a more positive sense), and then shows that well-known baseline approaches without considering links or community information cannot find these community outliers. We propose an efficient solution by modeling networked data as a mixture model composed of multiple normal communities and a set of randomly generated outliers. The probabilistic model characterizes both data and links simultaneously by defining their joint distribution based on hidden Markov random fields (HMRF). Maximizing the data likelihood and the posterior of the model gives the solution to the outlier inference problem. We apply the model on both synthetic data and DBLP data sets, and the results demonstrate importance of this concept, as well as the effectiveness and efficiency of the proposed approach.