Workshop Program

The workshop schedule contains two keynote talks (40 minutes each) and six paper presentations (20 minutes each). Speakers are suggested to spare at least 5 minutes of their time for Q-and-A. Last 15 minutes are reserved to discuss the future perspective of ODD^x and possible journal (special issue) publications.

Workshop proceedings are available for download

Please note the workshop location has changed! The new location according to KDD 2014 program is
Sheraton Times Square Hotel 811 7th Ave, New York, NY 10019

2:00 - 2:05 pm       Opening
2:05 - 2:45 pm       Keynote Talk 1: An ODE (Outlier Detection and Explanation) to Analyzing Data with
Anomalies: Algorithms and Applications
      Prof. Srinivasan Parthasarathy
2:45 - 3:05 pm       Anomaly Detection in Networks with Changing Trends
      Timothy La Fond, Jennifer Neville and Brian Gallagher
3:05 - 3:25 pm       Change-point detection in temporal networks using hierarchical random graphs
      Leto Peel and Aaron Clauset
3:25 - 3:45 pm       Real time contextual collective anomaly detection over multiple data streams
      Yexi Jiang, Chunqiu Zeng, Jian Xu and Tao Li
3:45 - 4:05 pm       STOUT: Spatio-Temporal Outlier detection Using Tensors
      Yanan Sun and Vandana Janeja
4:05 - 4:30 pm       Coffee Break
4:30 - 5:10 pm       Keynote Talk 2: Detection of Significant Network Processes
      Prof. Ambuj Singh
5:10 - 5:30 pm       An Ensemble Approach for Event Detection and Characterization in Dynamic Graphs
      Shebuti Rayana and Leman Akoglu
5:30 - 5:50 pm       Learning Outlier Ensembles: The Best of Both Worlds - Supervised and Unsupervised
      Barbora Micenkova, Brian McWilliams and Ira Assent
5:50 - 6:05 pm       Discussions for Future ODD^x

Invited Keynote Talks

  • Prof. Srinivasan Parthasarathy (Ohio State University)
    An ODE (Outlier Detection and Explanation) to Analyzing Data with Anomalies: Algorithms and Applications
    Abstract: In this talk I will briefly discuss recent advances in outlier detection, with a focus on distance-based techniques and discuss possible future directions in the context of rank-driven interactive analysis and data-guided explanations and visualizations. Time permitting we will examine such techniques in the context of real world analysis of multi-modal data including time series, graphs, text and factorial designs to help understand interactions among various optimizations for such algorithms.
  • Srini_Parthasarathy

  • Prof. Ambuj Singh (University of California Santa Barbara)
    Detection of Significant Network Processes
    Abstract: In order to understand complex networks, we need to characterize the dynamic processes occurring within them. Examples of such processes include network congestion in the Internet or a transportation network, or the spread of malware through an online social network. The detection of such dynamic processes requires a model of underlying behavior using which inferences about significance or anomalous behavior can be made. Detecting anomalies in networks is a well-understood problem when restricted to only the graph structure (e.g., communities, structural holes), but there has been limited work on networks with node/edge attributes. When node attributes are allowed to change over time, the smooth evolution of network substructures can be used to detect significant network processes that grow, shrink, and merge over time. I will discuss an approach that compares the value at a node/edge with a background distribution, and uses a positive score to indicate significance. I will discuss methods for detecting highest scoring subgraphs (fixed substructures, varying time intervals), and for detecting smoothly varying substructures. Finally, I will discuss methods that detect significant network substructures over two classes of networks in order to explain their differences. Examples will be drawn from information networks and brain networks to illustrate the methods.
  • Ambuj_Singh

Workshop Description

We consider outlier mining as a three-fold challenge: outlier detectionoutlier description, and outlier modeling under data diversity. Outlier detection refers to the quantitative mining of the outliers and tries to identify outliers from large datasets. On the other hand, outlier description concerns with the qualitative mining of outliers and tries to interpret or explain outlier properties. In addition to detection and description, one faces the curse of data diversity, with the increase in complexity of real-world datasets including temporal, spatial, sequence, graph, and multi-dimensional space anomalies. The topics of detection and description for diverse data are rarely considered in unison, and literature for these tasks is spread over different research communities. The main goal of ODD2 is to bridge this gap and provide a venue for knowledge exchange between these different research areas.

For today's outlier mining applications to be successful, three directions should be addressed: (i) how to create outlier detection models, (ii) how to serve the needs of certain applications with respect to interpretability, and (iii) how to create outlier models that can handle datasets consisting of diverse sources. We remark that those directions are not disjoint, in contrast they are quite intertwined: as the diversity and thus complexity of the data increases, outlier description not only becomes more challenging but also even more necessary.

Most of today's data sources are heterogeneous: multi-/high-dimensional data points, evolving data streams, text, heterogeneous graphs, spatio-temporal data, and so on. For example consider social media platforms such as Twitter or Facebook. The diversity of data in such platforms is immense; ranging from relations among users (graphs), user demographics (high-dimensional features), user-generated content (text), temporal dynamics (data streams), heterogeneous relational data in the form of likes, shares, tags, and so on. Outliers in those applications often correspond to various types of anomalies ranging from fake celebrities, fake user accounts, (social) malware, page-like-as-a-service, and so on. As a result, it becomes critical to develop outlier models that can work with diverse datasets, provide high quality outlier detection, and also describe the reasons for outlier-ness to the human user.

Topics of Interest

The workshop covers several aspects of outlier detection and description and of related research fields. A non-exhaustive list of topics of interest is given below:

  • Interleaved detection and description of outliers
    • Description models for given outliers
    • Pattern and local information based outlier description
    • Subspace outliers, feature selection, and space transformations
    • Ensemble methods for anomaly detection and description
    • Descriptive local outlier ranking
    • Identification of outlier rules
    • Finding intensional knowledge
    • Contextual and community outliers
    • Human-in-the-loop modeling and learning
    • Visualization techniques for interactive exploration of outliers
    • Comparative studies on outlier description
  • Related research fields
    • Contrast mining
    • Change and novelty detection
    • Causality analysis
    • Frequent itemset mining
    • Compression theory
    • Subgroup mining
    • Subspace learning
  • Formal outlier mining models
    • Supervised, semi-supervised, and unsupervised models
    • Statistical models
    • Distance-based models
    • Density-based models
    • Spectral models
    • Constraint-based models
    • Ensemble models
  • Outlier mining for complex databases
    • Graph data (e.g. community outliers)
    • Spatio-temporal data
    • Time series and sequential data
    • Online processing of stream data
    • Scalability to high dimensional data
  • Applications of outlier detection and description
    • Fraud in financial data
    • Intrusions in communication networks
    • Sensor network analysis
    • Social network analysis
    • Health surveillance
    • Customer profiling
    • ... and many more ...
We encourage submissions describing innovative work in related fields that address the issue of diversity in outlier mining.

Submission Instructions

We invite submission of unpublished original research papers that are not under review elsewhere. All papers will be peer reviewed. If accepted, at least one of the authors must attend the workshop to present the work. The submitted papers must be written in English and formatted according to the ACM Proceedings Template (Tighter Alternate style).

The maximum length of papers is 10 pages in this format. We also invite vision papers and descriptions of work-in-progress or case studies on benchmark data as short paper submissions of up to 4 pages.

The papers should be in PDF format and submitted via EasyChair submission site

Accepted papers are included in the KDD 2014 Digital Proceedings, and available in the ACM Digital Library.

Important Dates

Submission deadline: June 11th, 2014
Acceptance notification: July 15th, 2014
Camera-ready deadline: July 27th, 2014
Workshop: August 24, 2014

Program Committee

  • Fabrizio Angiulli, University of Calabria
  • Ira Assent, Aarhus University
  • Albert Bifet, Yahoo! Labs Barcelona
  • Rajmonda Caceres, MIT Lincoln Laboratory
  • Varun Chandola, Oak Ridge Nat. Lab.
  • Polo Chau, Georgia Tech
  • Sanjay Chawla, University of Syndey
  • Christos Faloutsos, Carnegie Mellon University
  • Jing Gao, University of Buffalo
  • Manish Gupta, Microsoft, India
  • Bartosz Krawczyk, Wroclaw Univ of Tech, Poland
  • Arun Maiya, Institute for Defense Analyses
  • Daniel B. Neill, Carnegie Mellon University
  • Spiros Papadimitriou, Rutgers University
  • Joerg Sander, University of Alberta
  • Thomas Seidl, RWTH Aachen University
  • Koen Smets, University of Antwerp
  • Hanghang Tong, CUNY
  • Ye Wang, The Ohio State University
  • Osmar Zaiane, University of Alberta
  • Arthur Zimek, LMU Munich


If you are considering submitting to the workshop and have questions regarding the workshop scope or need further information,
please do not hesitate to contact us: odd14kdd (at)
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