Year: 2025
Type: conference-paper
Abstract: We introduce ROKSANA, an open-source Python toolkit designed to support research in graph-based keyword search under adversarial settings.ROKSANA provides a modular environment for dataset handling, graph neural network (GNN)-based retrieval, and adversarial attack modeling, enabling systematic evaluation of search robustness.The framework integrates built-in retrieval and attack methods while allowing seamless customization of search algorithms and perturbation strategies.Users can benchmark performance on a centralized leaderboard, generate reproducible evaluation reports, and explore ranking behaviors through an interactive web-based visualization interface.By centering around reproducibility, extensibility, and collaborative benchmarking, ROKSANA serves as a comprehensive platform for advancing robust and interpretable keyword search in graphs.This demonstration will showcase ROKSANA's capabilities in real-time, illustrating its impact on experimental workflows and adversarial robustness analysis in graph IR research.
Authors: Radin Hamidi Rad, Amir Khosrojerdi, Ebrahim Bagheri
Institutions: University of Toronto, Queen's University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4412377002