However, previous NF-based methods forcibly transform the distribution of all features into a single distribution (e.g., unit normal distribution), even when the features can have locally distinct semantic information and thus follow different

Graph neural networks (GNNs) have shown the power in graph representation learningfornumeroustasks.

However, previous NF-based methods forcibly transform the distribution of all features into a single distribution (e.g., unit normal distribution), even when the features can have locally distinct semantic information and thus follow different

Our user study shows that the anomaly explanation form of PARs is better understood and favoured by Anomaly detection, which aims to identify data instances regular anomaly detection system users compared with existing that do not conform to the expected behavior, is a classic model-agnostic anomaly explanation options. In our machine learning task with numerous applications in experiments, we demonstrate that it is significantly more various domains including fraud detection, intrusion detection, efficient to find PARs than anchors (Ribeiro, Singh, and predictive maintenance, etc. Over the past decades, numerous Guestrin 2018), another rule-based explanation, for identified methods have been proposed to tackle this challenging anomaly instances. Moreover, PARs are also far more problem. Examples include one-class classificationbased accurate than anchors for anomaly explanation, meaning (Manevitz and Yousef 2001; Ruff et al. 2018), nearest that they have considerably higher precision and recall when neighbor-based (Breunig et al. 2000), clustering-based applied as anomaly detection rules on unseen data other (Jiang and An 2008), isolation-based (Liu, Ting, and Zhou than the anomaly instance on which they were originally derived 2012; Hariri, Kind, and Brunner 2019), density-based (Liu, for explanation. Additionally, we show that PARs can Tan, and Zhou 2022; Feng and Tian 2021) and deep anomaly also achieve higher accuracy on abnormal feature identification detection models based on autoencoders (Zhou and Paffenroth compared with many state-of-the-art model-agnostic 2017; Zong et al. 2018), generative adversarial networks explanation methods including LIME (Ribeiro, Singh, and (Zenati et al. 2018; Han, Chen, and Liu 2021), to Guestrin 2016), SHAP (Lundberg and Lee 2017), COIN name a few.

Counterfactual explanations (CFEs) exemplify how to minimally modify a feature vector to achieve a different prediction for an instance. CFEs can enhance informational fairness and trustworthiness, and provide suggestions for users who receive adverse predictions. However, recent research has shown that multiple CFEs can be offered for the same instance or instances with slight differences. Multiple CFEs provide flexible choices and cover diverse desiderata for user selection. However, individual fairness and model reliability will be damaged if unstable CFEs with different costs are returned. Existing methods fail to exploit flexibility and address the concerns of non-robustness simultaneously. To address these issues, we propose a conceptually simple yet effective solution named Counterfactual Explanations with Minimal Satisfiable Perturbations (CEMSP). Specifically, CEMSP constrains changing values of abnormal features with the help of their semantically meaningful normal ranges. For efficiency, we model the problem as a Boolean satisfiability problem to modify as few features as possible. Additionally, CEMSP is a general framework and can easily accommodate more practical requirements, e.g., casualty and actionability. Compared to existing methods, we conduct comprehensive experiments on both synthetic and real-world datasets to demonstrate that our method provides more robust explanations while preserving flexibility.

As powerful tools for representation learning on graphs, graph neural networks (GNNs) have facilitated various applications from drug discovery to recommender systems. Nevertheless, the effectiveness of GNNs is immensely challenged by issues related to data quality, such as distribution shift, abnormal features and adversarial attacks. Recent efforts have been made on tackling these issues from a modeling perspective which requires additional cost of changing model architectures or re-training model parameters. In this work, we provide a data-centric view to tackle these issues and propose a graph transformation framework named GTrans which adapts and refines graph data at test time to achieve better performance. We provide theoretical analysis on the design of the framework and discuss why adapting graph data works better than adapting the model. Extensive experiments have demonstrated the effectiveness of GTrans on three distinct scenarios for eight benchmark datasets where suboptimal data is presented. Remarkably, GTrans performs the best in most cases with improvements up to 2.8%, 8.2% and 3.8% over the best baselines on three experimental settings. Code is released at https://github.com/ChandlerBang/GTrans.

Unlike other forms of spamming, it is difficult to collect a large amount of gold-standard labels for reviews Many existing methods on review spam detection by means of manual effort. Thus, most of these methods considering text content merely utilize simple text [Mukherjee et al., 2013; Li et al., 2013a; Sun et al., features such as content similarity. We explore a 2013] just rely on the ad-hoc or pseudo fake or non-fake novel idea of exploiting text generality for improving labels for model training, such as the labels annotated by spam detection. Besides, apart from the task the Amazon anonymous online workers [Ott et al., 2011; of review spam detection, although there have also Li et al., 2014]. On the other hand, some unsupervised been some works on identifying the review spammers methods have been proposed to detect the individual review (users) and the manipulated offerings (items), spammer [Mukherjee et al., 2013; Lim et al., 2010; no previous works have attempted to solve these Wang et al., 2011] and review spammer groups [Mukherjee et three tasks in a unified model. We have proposed al., 2012]. In addition, time series pattern [Xie et al., 2012], a unified probabilistic graphical model to detect rating distribution [Feng et al., 2012], reviewer graph [Wang et the suspicious review spams, the review spammers al., 2011], and reviewing burstiness [Fei et al., 2013] have also and the manipulated offerings in an unsupervised been applied to identify the review spams in an unsupervised manner.