Link prediction is one of the fundamental problems for graphstructured data. However, a number of applications of link prediction, such as predicting commercial ties or memberships within a criminal organization, are adversarial, with another party aiming to minimize its effectiveness by manipulating observed information about the graph. In this paper, we focus on the feasibility of mounting adversarial attacks against link prediction algorithms based on graph neural networks. We first propose a greedy heuristic that exploits incremental computation to find attacks against a state-of-the-art link prediction algorithm, called SEAL. We then design an efficient variant of this algorithm that incorporates the link formation mechanism and Υ-decaying heuristic theory to design more effective adversarial attacks. We used real-world datasets and performed an extensive array of experiments to show that the performance of SEAL is negatively affected by a significant margin. More importantly, our experimental results have shown that our adversarial attacks mounted based on SEAL can be readily transferred to several existing link prediction heuristics in the literature. CCS CONCEPTS • Security and privacy → Network security.

. Significance: Open-source software packages have been extensively used in the past three decades in medical imaging and diagnostics, aiming to study the feasibility of the application ex vivo . Unfortunately, most of the existing open-source tools require some software engineering background to install the prerequisite libraries, choose a suitable computational platform, and combine several software tools to address different applications. Aim: To facilitate the use of open-source software in medical applications, enabling computational studies of treatment outcomes prior to the complex in-vivo setting. Approach: FullMonteWeb, an open-source, user-friendly web-based software with a graphical user interface for interstitial photodynamic therapy (iPDT) modeling, visualization, and optimization, is introduced. The software can perform Monte Carlo simulations of light propagation in biological tissues, along with iPDT plan optimization. FullMonteWeb installs and runs the required software and libraries on Amazon Web Services (AWS), allowing scalable computing without complex set up. Results: FullMonteWeb allows simulation of large and small problems on the most appropriate compute hardware, enabling cost improvements of versus always running on a single platform. Case studies in optical property estimation and diffuser placement optimization highlight FullMonteWeb’s versatility. Conclusion: The FullMonte open source suite enables easier and more cost-effective in-silico studies for iPDT.