GLAD: group anomaly detection in social media analysis

@article{Yu2015GLADGA,
  title={GLAD: group anomaly detection in social media analysis},
  author={Rose Yu and Xinran He and Yan Liu},
  journal={Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining},
  year={2015}
}
  • Rose Yu, Xinran He, Yan Liu
  • Published 24 August 2014
  • Computer Science
  • Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining
Traditional anomaly detection on social media mostly focuses on individual point anomalies while anomalous phenomena usually occur in groups. Therefore it is valuable to study the collective behavior of individuals and detect group anomalies. Existing group anomaly detection approaches rely on the assumption that the groups are known, which can hardly be true in real world social media applications. In this paper, we take a generative approach by proposing a hierarchical Bayes model: Group… 

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References

SHOWING 1-10 OF 33 REFERENCES

Group Anomaly Detection using Flexible Genre Models

TLDR
The Flexible Genre Model (FGM) is proposed, designed to characterize data groups at both the point level and the group level so as to detect various types of group anomalies.

Hierarchical Probabilistic Models for Group Anomaly Detection

TLDR
Generative models for detecting group anomalies, which are larger scale phenomena that only become apparent when groups of points are considered, are proposed.

Bayesian anomaly detection methods for social networks

TLDR
The first stage uses simple, conjugate Bayesian models for discrete time counting processes to track the pairwise links of all nodes in the graph to assess normality of behavior, and the second stage applies standard network inference tools on a greatly reduced subset of potentially anomalous nodes.

One-Class Support Measure Machines for Group Anomaly Detection

TLDR
It is shown that various types of VKDEs can be considered as solutions to a class of regularization problems studied in this paper, bridging the gap between large-margin methods and kernel density estimators.

ArnetMiner: extraction and mining of academic social networks

TLDR
The architecture and main features of the ArnetMiner system, which aims at extracting and mining academic social networks, are described and a unified modeling approach to simultaneously model topical aspects of papers, authors, and publication venues is proposed.

Anomaly detection: A survey

TLDR
This survey tries to provide a structured and comprehensive overview of the research on anomaly detection by grouping existing techniques into different categories based on the underlying approach adopted by each technique.

Probabilistic topic models with biased propagation on heterogeneous information networks

TLDR
This paper proposes a novel topic model with biased propagation (TMBP) algorithm to directly incorporate heterogeneous information network with topic modeling in a unified way and extensively evaluates the proposed approach and compares to the state-of-the-art techniques on several datasets.

Community detection in graphs

Latent Dirichlet Allocation

Outlier Detection : A Survey

TLDR
This survey provides a comprehensive overview of existing outlier detection techniques by classifying them along different dimensions.