CCNI Workshop

The 6th Workshop on Graph Techniques for Adversarial Activity Analytics (GTA³ 2022)

December 17, 2022 | Virtual Conference - Osaka, Japan

For past GTA3 workshops, please visit our archive page.

Theme & Purpose

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.

Topics of Interest

Including but not limited to:

Key Research Topics:

  • Graph alignment and data fusion from multiple heterogeneous domains
  • Sub-graph detection and matching for large-scale networks
  • Attack and defense strategies on graph models (e.g., graph neural networks)
  • Deep generative models for synthesizing realistic networks (e.g., static, dynamic, etc.)


  • Representation learning on graphs
  • Explainability of graph models
  • Clustering and ranking on graphs
  • High-performance graph computing
  • Multilayer and multiplex networks
  • Link prediction and recommendation
  • Limits of detectability and identifiability
  • Information diffusion and influence maximization
  • New methods and frontiers in spectral graph theory
  • Novel datasets and evaluation metrics for network analytics

Other related topics:

  • Knowledge graph creation, mining, and applications
  • Complex anomaly detection and interpretation
  • Summarization and visualization of large networks
  • Interactive graph search and exploration
  • Topological analysis (e.g., motif analysis) on large graphs
  • Game-theoretic approach for adversarial modeling on graphs
  • Graph-based semi-supervised learning, active learning, and transfer learning
  • Frontiers of graph machine learning for adversarial activities analytics

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 (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

Important Dates

  • + Due date for full workshop papers submissionOctober 18, 2022
  • + Notification of paper acceptance to authorsNovember 10, 2022
  • + Camera-ready of accepted papersNovember 20, 2022
  • + WorkshopsDecember 17, 2022

Keynote Speakers

We plan to invite 3 speakers who are experts in relevant research areas. Please see our past workshops for more information:

Accepted Papers

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


All times are JST (Japan Standard Time)

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)
12:20pm | Closing Remarks

Program Committee

  • Alexei Kopylov (HRL Laboratories, USA)
  • Anil Gaihre (Stevens Institute of Technology, USA)
  • Aruna Jammalamadaka (HRL Laboratories, USA)
  • Dana Warmsley (HRL Laboratories, USA)
  • Daniel L. Sussman (Boston University, USA)
  • Dawei Zhou (Virginia Tech, USA)
  • Dongqi Fu (UIUC, USA)
  • Evangelos Papalexakis (UC Riverside, USA)
  • Joseph Cottam (PNNL, USA)
  • Kang-Yu Ni (HRL Laboratories, USA)
  • Kevin Martin (HRL Laboratories, USA)
  • Michael Warren (HRL Laboratories, USA)
  • Patrick Mackey (PNNL, USA)
  • Qinghai Zhou (UIUC, USA)
  • Samuel Johnson (HRL Laboratories, USA)
  • Santosh Pandey (Stevens Institute of Technology, USA)
  • Shiyang Chen (Stevens Institute of Technology, USA)
  • Sumit Purohit (PNNL, USA)
  • Tsai-Ching Lu (HRL Laboratories, USA)
  • Yi Zhang (Vanderbilt, USA)
  • Yunchao (Lance) Liu (Vanderbilt, USA)
  • Yuying Zhao (Vanderbilt, USA)
  • Zhe Xu (UIUC, USA)

Program Chairs (Organizers)