Graph Anomaly Detection (GAD) aims to identify uncommon, deviated, or suspicious objects within graph-structured data. Existing methods generally focus on a single graph object type (node, edge, graph, etc.) and often overlook the inherent connections among different object types of graph anomalies. For instance, a money laundering transaction might involve an abnormal account and the broader community it interacts with. To address this, we present UniGAD, the first unified framework for detecting anomalies at node, edge, and graph levels jointly. Specifically, we develop the Maximum Rayleigh Quotient Subgraph Sampler (MRQSampler) that unifies multi-level formats by transferring objects at each level into graph-level tasks on subgraphs. We theoretically prove that MRQSampler maximizes the accumulated spectral energy of subgraphs (i.e., the Rayleigh quotient) to preserve the most significant anomaly information. To further unify multi-level training, we introduce a novel GraphStitch Network to integrate information across different levels, adjust the amount of sharing required at each level, and harmonize conflicting training goals. Comprehensive experiments show that UniGAD outperforms both existing GAD methods specialized for a single task and graph prompt-based approaches for multiple tasks, while also providing robust zero-shot task transferability.

Artificial general intelligence on graphs has shown significant advancements across various applications, yet the traditional `Pre-train & Fine-tune' paradigm faces inefficiencies and negative transfer issues, particularly in complex and few-shot settings. Graph prompt learning emerges as a promising alternative, leveraging lightweight prompts to manipulate data and fill the task gap by reformulating downstream tasks to the pretext. However, several critical challenges still remain: how to unify diverse graph prompt models, how to evaluate the quality of graph prompts, and to improve their usability for practical comparisons and selection. In response to these challenges, we introduce the first comprehensive benchmark for graph prompt learning. Our benchmark integrates SIX pre-training methods and FIVE state-of-the-art graph prompt techniques, evaluated across FIFTEEN diverse datasets to assess performance, flexibility, and efficiency. We also present 'ProG', an easy-to-use open-source library that streamlines the execution of various graph prompt models, facilitating objective evaluations. Additionally, we propose a unified framework that categorizes existing graph prompt methods into two main approaches: prompts as graphs and prompts as tokens. This framework enhances the applicability and comparison of graph prompt techniques. The code is available at: https://github.com/sheldonresearch/ProG.

The reliability and security of distributed detection systems have become increasingly important due to their growing prevalence in various applications. As advancements in human-machine systems continue, human factors, such as herding behaviors, are becoming influential in decision fusion process of these systems. The presence of malicious users further highlights the necessity to mitigate security concerns. In this paper, we propose a maximum entropy attack exploring the herding behaviors of users to amplify the hazard of attackers. Different from prior works that try to maximize the fusion error rate, the proposed attack maximizes the entropy of inferred system states from the fusion center, making the fusion results the same as a random coin toss. Moreover, we design static and dynamic attack modes to maximize the entropy of fusion results at the steady state and during the dynamic evolution stage, respectively. Simulation results show that the proposed attack strategy can cause the fusion accuracy to hover around 50% and existing fusion rules cannot resist our proposed attack, demonstrating its effectiveness.

In this paper we describe a complex optimization application arising in maintenance scheduling, developed in close collaboration with an industrial partner. We have to plan and schedule preventive and corrective maintenance activities at customer sites by a group of traveling repair technicians. A specific property of the problem considered here is a mix of customers in both urban centers and rural areas. This means that travel times between customers must be considered when balancing overall workload for each agent. We discuss a problem decomposition compatible with current management practice, describe different solvers for the individual problem steps, and show results on real-world data from the industrial partner.

This paper proposes objective quality measures adopted in speech processing for perceptual quality evaluation of audio watermarking. Different from using an auditory perception model that mimics human auditory system, objective quality measures are introduced as alternative approach to perceive the dissimilarities caused by audio watermarking. After embedding the watermark into a variety of audio signals from EBU database, we calculate the distance between the watermarked and host signals in terms of several well- developed quality measures. For correlation analysis, subjective listening tests and a commercial evaluation tool PEMO-Q are also used to grade the differences. Pearson correlation coefficients reveal that the investigated quality measures, especially weighted spectral slope (WSS) measure, correspond well with reference ratings. Moreover, quality measures run much faster than PEMO-Q. The results indicate that objective quality measures can be the perceptual quality predictors for audio watermarking.