For past GTA3 workshops, please visit our archive page.
Graphs are natural analytic tools for modeling adversarial activities across a wide range of domains and applications. Examples include sensing online influence campaigns through social networks, detecting threats and vulnerabilities in cyber-physical networks, preventing disease spreading, and combating covert illicit activities, such as smuggling and arms trafficking, that span across multiple networks (e.g., financial, communication, and transportation networks). The main purpose of this workshop is to provide a forum to discuss research problems and novel approaches on graphs, with emphasis on addressing the three fundamental problems for modeling adversarial activities - “Connecting the dots”, “Finding a needle in a haystack”, and “Defending against attacks”. Besides the traditional activity-centric networks that have been the main focus of this workshop, semantic networks (e.g., knowledge graphs) recently drawn significant attention in our research community. Thus, an important question is to extend existing graph computing capabilities to handle semantically rich networks to support the emerging research direction. The workshop (co-located with the 2022 IEEE Big Data) aims to bring together a community of researchers, from both academia and industry, to share their experiences and exchange perspectives for future research directions. We also hope the workshop will serve as a medium to facilitate future collaborations among interested audiences and researchers.
Including but not limited to:
Key Research Topics:
Other related topics:
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 (Formatting Instructions) to fit within 10 pages (long papers), or 6 pages (short papers) including any diagrams, references and appendices. Submissions must be self-contained and in English. After uploading your submission, please double check the copy stored on the website.
Submissions should be made using the Online Submission System provided by IEEE BigData
We plan to invite 3 speakers who are experts in relevant research areas. Please see our past workshops for more information: https://ccni.hrl.com/workshop/archive
Uncertainty Visualization for Graph Coarsening
Fangfei Lan, Sourabh Palande, Michael Young, and Bei Wang
Dual Graphs of Polyhedral Decompositions for the Detection of Adversarial Attacks
Huma Jamil, Yajing Liu, Christina Cole, Nathaniel Blanchard, Emily King, Michael Kirby, and Christopher Peterson
Knowledge Graphs of the QAnon Twitter Network
Clay Adams, Malvina Bohzidarova, James Chen, Andrew Gao, Zhengtong Liu, Hunter Priniski, Junyuan Lin, Rishi Sonthalia, Andrea Bertozzi, and Jeffrey Brantingham
A Survey of Explainable Graph Neural Networks for Cyber Malware Analysis
Dana Warmsley, Alex Waagen, Jiejun Xu, Zhining Liu, and Hanghang Tong
|08:30am - 08:35am|||||Opening Remarks|
|08:35am - 09:20am|||||Keynote 1: Multi-View Knowledge Graph Representation Learning Prof. Wei Wang|
|09:20am - 10:05am|||||Keynote 2: Visual Analytics for Adversarial Activity Analysis - Prof: Ross Maciejewski|
|10:05am - 10:25am|||||Uncertainty Visualization for Graph Coarsening (S07201)|
|10:25am - 10:35am|||||Coffee Break|
|10:35am - 11:20am|||||Keynote 3: Sparse matrices powering three pillars of science: simulation, data, and learning - Dr. Aydin Buluç|
|11:20am - 11:40am|||||Dual Graphs of Polyhedral Decompositions for the Detection of Adversarial Attacks (S07202)|
|11:40am - 12:00pm|||||Knowledge Graphs of the QAnon Twitter Network (S07203)|
|12:00pm - 12:20pm|||||A Survey of Explainable Graph Neural Networks for Cyber Malware Analysis (S07204)|