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 2021 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 speakers who are experts in relevant research areas.
Please see our past workshops for more information: https://ccni.hrl.com/workshop/archive
A Graph Embedding Approach to User Behavior Anomaly Detection
Alexander Modell, Jonathan Larson, Melissa Turcotte, and Anna Bertiger
An Analysis of COVID-19 Knowledge Graph Construction and Applications
Dominic Flocoo, Bryce Palmer-Toy, Ruixiao Wang, Hongyu Zhu, Rishi Sonthalia, Junyuan Lin, Andrea Bertozzi, and Jeffrey Brantingham
A Constraint Propagation Approach for Identifying Biological Pathways in COVID-19 Knowledge Graphs
Influence in Transient Populations
Dana Warmsley and Samuel Johnson
Active Learning for the Subgraph Matching Problem
Yurun Ge and Andrea Bertozzi
Evaluating the subgraph matching problem in the presence of categorical constraints
MChristopher Ebsch, Joseph Cottam, and George Chin
Characterizing Disease Spreading via Visibility Graph Embedding
Kangyu Ni, Jiejun Xu, Shane Roach, Tsai-Ching Lu, and Alexei Kopylov
Transactional Knowledge Graph Generation To Model Adversarial Activities
Sumit Purohit, Patrick Mackey, William Smith, Madelyn Dunning, Miquette J Orren, Trevor M Langlie-Miletich, Rahul D Deshmukh, Ankur Bohra, Tonya J Martin, Dan Aimone, and George Chin
Higher-order Structure Based Anomaly Detection on Attributed Networks
Xu Yuan, Na Zhou, Shuo Yu, Huafei Huang, Zhikui Chen, and Feng Xia
Semantic Property Graph for Scalable Knowledge Graph Analytics
Sumit Purohit, Nhuy Van, and George Chin
|01:00pm - 01:05pm|||||Opening Remarks|
|01:05pm - 01:50pm|||||Keynote 1 - Dr. Sherry Li|
|01:50pm - 02:35pm|||||Keynote 2 - Prof. Jon Kleinberg|
|02:35pm - 02:50pm|||||Transactional Knowledge Graph Generation to Model Adversarial Activities|
|02:50pm - 03:05pm|||||Higher-order Structure Based Anomaly Detection on Attributed Networks|
|03:05pm - 03:15pm|||||Influence in Transient Populations|
|03:15pm - 03:25pm|||||Evaluating the subgraph matching problem in the presence of categorical constraints|
|03:25pm - 03:45pm|||||Coffee Break|
|03:45pm - 04:30pm|||||Keynote 3 - Prof. Marinka Zitnik|
|04:30pm - 04:45pm|||||Active Learning for the Subgraph Matching Problem|
|04:45pm - 05:00pm|||||An Analysis of COVID-19 Knowledge Graph Construction and Applications|
|05:00pm - 05:10pm|||||A Constraint Propagation Approach for Identifying Biological Pathways in COVID-19 Knowledge Graphs|
|05:10pm - 05:20pm|||||Characterizing Disease Spreading via Visibility Graph Embedding|
|05:20pm - 05:30pm|||||A Graph Embedding Approach to User Behavior Anomaly Detection|
|05:30pm - 05:40pm|||||Semantic Property Graph for Scalable Knowledge Graph Analytics|