Scan Statistics on Enron Graphs

By Carey E. Priebe, John M. Conroy, David J. Marchette, Youngser Park. Computational and mathematical organization theory, 2005

365 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Epidemiology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/398489cbda7aa1449c4700343fad3bb69f5f4eb7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Charu C. Aggarwal
2. Leman Akoglu
3. Tao Li
4. Danai Koutra
5. Jiawei Han
6. Philip S. Yu
7. Hanghang Tong
8. Jing Gao

In Epidemiology

1. Jiawei Han
2. Charu C. Aggarwal
3. Fei Wang
4. Manish Gupta
5. Leman Akoglu

In Statistical and Nonlinear Physics

1. Charu C. Aggarwal
2. Danai Koutra
3. Jiawei Han
4. Tao Li
5. Leman Akoglu
6. Hanghang Tong
7. Jing Gao
8. Pauline Lienhua Chou

Citations in the corpus

• Outlier Detection for Temporal Data: A Survey (2014, methodology): “[157] study the communication graph of the Enron data with respect to the maximum degree and digraph size scan statistics.”
• Graph based anomaly detection and description: a survey (2014, methodology): “SCAN STATISTICS [Priebe et al., 2005] 3 7 3 7 3 3 scan statistics 3 3 scan stat.”
• Community discovery using nonnegative matrix factorization (2011, methodology): “We use the Enron email data set( Priebe et al. 2005 ) to evaluate the effectiveness of our ANMF method on directed networks.”
• Outlier Analysis (2013, methodology): “The work in [375] uses the history of node’s neighborhood in order to detect anomalies.”
• Anomaly detection in dynamic networks: a survey (2015, neutral): “In this section we identify and formalize four types of anomalies that arise in dynamic networks.”
• Communication Networks from the Enron Email Corpus “It's Always About the People. Enron is no Different” (2005, methodology): “Priebe et al. (2005) used scan statistics, which slides a window over equally sized portions of longitudinal data, in order to search 206 DIESNER, FRANTZ AND CARLEY”
• Anomaly detection in online social networks (2014, methodology): “ons. It could be arguedthat all social networksare dynamic, however it is often useful to analyse social networks as if they were static. As an example, consider the Enron email data set (analysed in Priebe et al. (2005); Akoglu et al. (2010); Eberle and Holder (2007, 2009)), which consists of a dyn”