The 9th Workshop on Graph Techniques for Adversarial Activity Analytics (GTA³ 2025)
November 12, 2025 | Washington DC, USA
For past GTA3 workshops, please visit our archive page.
In conjunction with the 25th IEEE International Conference on Data Mining (ICDM)
Workshop Summary
Graphs are powerful analytic tools for modeling adversarial activities across a wide range of domains and applications. Examples include identifying and responding to cybersecurity systems' threats and vulnerabilities, strengthening critical infrastructure's resilience and robustness, and combating covert illicit activities that span various domains like finance, communication, and transportation. With the rapid development of generative AI, the lifecycle and throughput of adversarial activities, such as generating attacks or synthesizing deceptive signals, have accelerated significantly. For instance, a malicious actor can generate many malware variants to flood defense systems or create agents to disseminate misleading signals, obscuring their activities. Consequently, there is a pressing need for novel and effective technology to autonomously handle these adversarial activities and keep pace with the evolving threats. The purpose of this workshop is to provide a forum to discuss emerging research problems and novel approaches in graph analysis for modeling adversarial activities in the age of generative AI.
Theme and Topics
Adversarial activities are often covert and embedded across multiple domains, making them generally undetectable and unrecognizable when viewed in isolation. They only become apparent when analyzed jointly across these domains. Therefore, a main research focus on modeling adversarial activities is developing techniques to fuse information from different networks into a unified view for comprehensive analysis. Equally important is the detection and matching of indicative patterns to recognize underlying adversarial activities within activity networks or graphs. Additionally, sophisticated adversarial actions may involve attempts to cover their tracks by attacking and altering networks, which has led to interest in attacking graph machine learning models. This, in turn, drives the development of robust models resilient to such attacks. The goal of the workshop is to address three fundamental problems in graph-based adversarial activity analytics: "Connecting the Dots," "Finding a Needle in a Haystack," and "Defending Against Attacks." In particular, with the advent of generative AI, we aim to augment state-of-the-art techniques and tackle these challenges in this new era.
In addition to analyzing activity networks, knowledge graphs play an important role in adversarial activity analysis. They provide the benefits of contextualizing and correlating information based on facts, enhancing the ability to detect and understand complex adversarial patterns. For instance, in the context of cybersecurity, knowledge graphs can help attribute attacks to specific threat actors by linking patterns of behavior, tools, and techniques to known profiles of adversaries. Similarly, in financial systems, knowledge graphs can be used to detect fraudulent activities by connecting transactions, accounts, and entities to identify unusual patterns indicative of fraud.
Submission Instructions
Submissions to the workshop will be subject to a double-blind peer review process, with each submission reviewed by at least two program committee members and an organizer. Submissions will be evaluated based on their relevance to the workshop, scientific novelty, and technical quality. Accepted papers will be given a presentation slot in the workshop and will be included in the ICDM Workshop Proceedings.
We accept two types of submissions: short papers (up to 6 pages) describing work in progress and full papers (up to 10 pages) presenting mature work. All submissions must follow the two-column IEEE format. More details are available on the IEEE ICDM 2025 Submission Guidelines page. Please submit your manuscript through the GTA3 2025 online submission system.
