Graph Neural Networks (GNNs) are powerful tools for graph representation learning. Despite their rapid development, GNNs also face some challenges, such as over-fitting, over-smoothing, and non-robustness. Previous works indicate that these problems can be alleviated by random dropping methods, which integrate augmented data into models by randomly masking parts of the input. However, some open problems of random dropping on GNNs remain to be solved. First, it is challenging to find a universal method that are suitable for all cases considering the divergence of different datasets and models. Second, augmented data introduced to GNNs causes the incomplete coverage of parameters and unstable training process. Third, there is no theoretical analysis on the effectiveness of random dropping methods on GNNs. In this paper, we propose a novel random dropping method called DropMessage, which performs dropping operations directly on the propagated messages during the message-passing process. More importantly, we find that DropMessage provides a unified framework for most existing random dropping methods, based on which we give theoretical analysis of their effectiveness. Furthermore, we elaborate the superiority of DropMessage: it stabilizes the training process by reducing sample variance; it keeps information diversity from the perspective of information theory, enabling it become a theoretical upper bound of other methods. To evaluate our proposed method, we conduct experiments that aims for multiple tasks on five public datasets and two industrial datasets with various backbone models. The experimental results show that DropMessage has the advantages of both effectiveness and generalization, and can significantly alleviate the problems mentioned above.

Many graph-based machine learning models are known to be vulnerable to adversarial attacks, where even limited perturbations on input data can result in dramatic performance deterioration. Most existing works focus on moderate settings in which the attacker is either aware of the model structure and parameters (white-box), or able to send queries to fetch model information. In this paper, we propose a query-free black-box adversarial attack on graphs, in which the attacker has no knowledge of the target model and no query access to the model. With the mere observation of the graph topology, the proposed attack strategy flips a limited number of links to mislead the graph models. We prove that the impact of the flipped links on the target model can be quantified by spectral changes, and thus be approximated using the eigenvalue perturbation theory. Accordingly, we model the proposed attack strategy as an optimization problem, and adopt a greedy algorithm to select the links to be flipped. Due to its simplicity and scalability, the proposed model is not only generic in various graph-based models, but can be easily extended when different knowledge levels are accessible as well. Extensive experiments demonstrate the effectiveness and efficiency of the proposed model on various downstream tasks, as well as several different graph-based learning models.

Recent works have demonstrated that deep learning on graphs is vulnerable to adversarial attacks, in that imperceptible perturbations on input data can lead to dramatic performance deterioration. In this paper, we focus on the underlying problem of learning robust representations on graphs via mutual information. In contrast to previous works measure the task-specific robustness based on the label space, we here take advantage of the representation space to study a task-free robustness measure given the joint input space w.r.t graph topology and node attributes. We formulate this problem as a constrained saddle point optimization problem and solve it efficiently in a reduced search space. Furthermore, we provably establish theoretical connections between our task-free robustness measure and the robustness of downstream classifiers. Extensive experiments demonstrate that our proposed method is able to enhance robustness against adversarial attacks on graphs, yet even increases natural accuracy.

Fairness has become a central issue for our research community as classification algorithms are adopted in societally critical domains such as recidivism prediction and loan approval. In this work, we consider the potential bias based on protected attributes (e.g., race and gender), and tackle this problem by learning latent representations of individuals that are statistically indistinguishable between protected groups while sufficiently preserving other information for classification. To do that, we develop a minimax adversarial framework with a generator to capture the data distribution and generate latent representations, and a critic to ensure that the distributions across different protected groups are similar. Our framework provides a theoretical guarantee with respect to statistical parity and individual fairness. Empirical results on four real-world datasets also show that the learned representation can effectively be used for classification tasks such as credit risk prediction while obstructing information related to protected groups, especially when removing protected attributes is not sufficient for fair classification.

Most multi-target tracking filters assume that one target and its observation follow a Hidden Markov Chain (HMC) model, but the implicit independence assumption of HMC model is invalid in many practical applications, and a Pairwise Markov Chain (PMC) model is more universally suitable than traditional HMC model. A particle probability hypothesis density filter based on PMC model (PF-PMC-PHD) is proposed for the nonlinear multi-target tracking system. Simulation results show the effectiveness of PF-PMC-PHD filter, and that the tracking performance of PF-PMC-PHD filter is superior to the particle PHD filter based on HMC model in a scenario where we kept the local physical properties of nonlinear and Gaussian HMC models while relaxing their independence assumption.