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Networks are natural analytic tools in modeling adversarial activities (e.g., smuggling, illegal arms dealing, illicit drug production) in different contexts. However, such activities are often covert and embedded across multiple domains. They are generally not detectable and recognizable from the perspective of an isolated network, and only become apparent when multiple networks are analyzed in a joint manner. Thus, one of the main research topics in modeling adversarial activities is to develop effective techniques to align and fuse information from different networks into a unified representation for global analysis. Based on the combined network representation, an equally important research topic is on detecting and matching indicating patterns to recognize the underlining adversarial activities in the integrated network. Furthermore, more sophisticated activities could potentially involve attempts at covering their tracks by attacking and changing networks. Thus, a developing area of interest is focused on attacking the commonly used graph models, which has led towards then using these insights to develop robust models that are resilient to these attacks.
Three key challenge problems involved in the modeling process include:
The focus of this workshop is to gather together the researchers from all relevant fields to share their experience and opinions on addressing the three fundamental graph mining problems – “Connecting the dots”, “Finding a needle in a haystack”, and “Defending against attacks” in the context of adversarial activity analytics.
In addition, this workshop also aims to provide a forum for discussing research challenges and novel approaches in synthesizing realistic networks that those observed in the real-worlds. Numerous approaches have been proposed in the past for generating networks (e.g., exponential random graphs, stochastic Kronecker graphs). However, few research has been conducted on systematically injecting and embedding subtle signals (e.g., covert activities) to these “background” networks. In addition, new methods for generating synthetic data in other domains (e.g., Computer Vision) with deep generative models (e.g., Variational Autoencoders, Generative Adversarial Networks) have grown in prominence. Naturally, the question arises as to whether these new methods can be adapted to the graph domains and how they compare in capability to the current state-of-the-art.
Including but not limited to:
Other related topics:
This workshop (co-located with the 2019 IEEE International Conference on Big Data) aims to bring together a cross-disciplinary audience of researchers from both academia and industry to share experience techniques, resources and best practices, and to exchange perspectives and future directions. We expect the workshop to develop a community of interested researchers and facilitate their future collaborations. A best paper will be selected and announced in our workshop based on the collective feedback from our reviewers.
Submissions to the workshop will be subject to a single-blind peer review process, with each submission reviewed by at least two program committee members in addition to an organizer. Accepted papers will be given either an oral or poster presentation slot, and will be published in the IEEE Big Data workshop proceedings.
Papers must be submitted in PDF format according to IEEE Computer Society Proceedings Manuscript Formatting Guidelines to (Formatting Instructions, Templates) to fit within 8 pages (long papers), 4 pages (short papers) or 2 pages (demo papers) including any diagrams, references and appendices. Submissions must be self-contained and in English. After uploading your submission, please check the copy stored on the site.
Submissions should be made using the Online Submission System provided by IEEE BigData and submitted by 23:59, Anywhere on Earth.