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 a large number of 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.

Workshop Theme

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 in 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 graph analytics techniques to tackle these challenges in this new era.

Submission Guidelines

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.

Papers must be submitted in PDF format according to the ACM CIKM template. Submissions can vary in length from 4 to 8 pages, plus additional pages for references (not counted towards the page limit). Note that there is no distinction between long and short papers; authors may decide on the appropriate length of their paper.

Topics of Interest

Graph alignment and data integration from multiple heterogeneous domains

Subgraph detection and discovery for large networks

Attack and defense strategies on graph models (e.g., GNNs)

Generative models for synthesizing realistic networks

Representation learning on graphs

Multilayer and multiplex networks

High-performance graph computing

Explainability of graph models (e.g., GNNs)Limits of detectability and identifiability

Link prediction and recommendation

Information diffusion and influence maximization

New methods for clustering and ranking on graphs

Novel datasets and evaluation metrics for network analytics

Knowledge graph creation, mining, and applications

Knowledge graph completion and reasoning

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

Large language models for adversarial activities

Weakly supervised and self-supervised anomaly detection

Fairness, explainability, and privacy in adversarial activities analytics

Benchmarks for adversarial activities analytics