CCNI Workshop

GTA³ 2.0: The 2nd workshop on Graph Techniques for Adversarial Activity Analytics

December 10th, 2018 | The Westin - Seattle, WA

For past GTA3 workshops, please visit our archive page.

Theme & Purpose

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.

Two key challenge problems involved in the modeling process include:

  • Network alignment and merging: develop accurate and scalable methods for mapping of nodes across heterogeneous networks based on various associational and causal dependencies.
  • Sub-graph detection and matching: develop robust and efficient algorithms for richly attributed networks to support detection and recognition of complex query patterns for networks.

The focus of this workshop is to gather together the researchers from all relevant fields to share their experience and opinions on addressing the two fundamental graph mining problems – “Connecting the dots” and “Finding a needle in a haystack”, 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.

Topics of Interest

Including but not limited to:

  • Novel graph generation and synthesizing techniques (e.g., static, dynamic networks)
  • Network alignment and data integration from multiple heterogeneous domains
  • Novel algorithms for sub-graph detection and matching in large networks
  • Limits of detectability and identifiability
  • Complex anomaly (e.g., group anomaly) detection and interpretation
  • Novel evaluation metrics for network generation, alignment and detection.
  • Identification of novel network datasets
  • Multilayer and multiplex network analytics
  • Clustering and ranking methods for composite networks
  • Large-scaled link prediction and recommendation algorithms
  • Community detection in big networks
  • Information diffusion and influence maximization
  • Game theoretic approach on anticipating opponent intent and actions
  • Interactive visualization for big graphs
  • New methods and frontiers in spectral graph theory
  • Analysis of network topologies (e.g., centrality and network motif analysis)
  • Semi-supervised learning, Transductive inference, Active learning, and Transfer learning in the graph context

This workshop (co-located with the 2018 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.

Submission Instructions

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 InstructionsTemplates) 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.

Important Dates

  • All deadlines end at 11:59pm PST
  • + Due date for full workshop papers submission October 10, 2018
  • + Notification of paper acceptance to authorsNovember 1, 2018
  • + Camera-ready of accepted papersNovember 15, 2018
  • + WorkshopsDecember 10-13, 2018

Program Co-chairs (Organizers)

Program Committee


Keynote Speakers

Additional keynote speakers will be announced soon