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 strengthening the resilience and robustness of cyber networks, identifying and responding to threats and vulnerabilities in critical infrastructure, and combating covert illicit activities, such as smuggling and arms trafficking, that span across multiple domains (e.g., finance, communication, and transportation). 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 activity-centric networks that have been the main focus of this workshop, text-rich networks and semantic networks (e.g., knowledge graphs) have drawn significant attention in our research community. Thus, an important objective involves enhancing the current graph computing capabilities to effectively handle these networks. Additionally, there is a need to investigate how the latest advancements in research, such as generative AI (GenAI) and large language models (LLM), can be leveraged to analyze these networks. This workshop 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:
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.
Accepted papers will be published in the IEEE proceedings.
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
AutoKG: Efficient Automated Knowledge Graph Generation for Language Models.
Bohan Chen and Andrea Bertozzi
Hate Speech And Hate Crimes: A Data-Driven Study Of Evolving Discourse Around Marginalized Groups
Malvina Bozhidarova, Jonathn Chang, Aaishah Ale-Rasool, Yuxiang Liu, Chongyao Ma, Andrea Bertozzi, Jeffrey Brantingham, Junyuan Lin, and Sanjukta Krishnagopal
Hybrid Attack Graph Generation with Graph Convolutional Deep-Q Learning
Sam Donald, Rounak Meyur, and Sumit Purohit
Learning Explainable Multi-view Representations for Malware Authorship Attribution
Irsyad Adam, Alex Waagen, Dana Warmsley, and Jiejun Xu
|14:50 CET, 05:50 PST|||||Opening Remarks|
|15:00 CET, 06:00 PST|||||Keynote 1: Prof. Tyler Derr|
|15:40 CET, 06:40 PST|||||Keynote 2: Ultra-Low-Latency Graph Neural Networks: Applications and Implementations - Prof. Callie Hao|
|16:20 CET, 07:20 PST|||||AutoKG: Efficient Automated Knowledge Graph Generation for Language Models|
|16:35 CET, 07:35 PST|||||Hate Speech And Hate Crimes: A Data-Driven Study Of Evolving Discourse Around Marginalized Groups|
|16:50 CET, 07:50 PST|||||Coffee Break|
|17:00 CET, 08:00 PST|||||Keynote 3: Prof. Sameer Singh|
|17:40 CET, 08:40 PST|||||Keynote 4: High-Performance Intelligent Big Data Analytics - Prof. Hang Liu|
|18:20 CET, 09:20 PST|||||Hybrid Attack Graph Generation with Graph Convolutional Deep-Q Learning|
|18:35 CET, 09:35 PST|||||Learning Explainable Multi-view Representations for Malware Authorship Attribution|
|18:50 CET, 09:50 PST|||||Closing Remarks|