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Recently, graph anomaly detection has attracted increasing attention in data mining and machine learning communities. Apart from existing attribute anomalies, graph anomaly detection also captures suspicious topological-abnormal nodes that differ from the major counterparts. Although massive graph-based detection approaches have been proposed, most of them focus on node-level comparison while pay insufficient attention on the surrounding topology structures. Nodes with more dissimilar neighborhood substructures have more suspicious to be abnormal. To enhance the local substructure detection ability, we propose a novel Graph Anomaly Detection framework via Multi-scale Substructure Learning (GADMSL for abbreviation). Unlike previous algorithms, we manage to capture anomalous substructures where the inner similarities are relatively low in dense-connected regions. Specifically, we adopt a region proposal module to find high-density substructures in the network as suspicious regions. Their inner-node embedding similarities indicate the anomaly degree of the detected substructures. Generally, a lower degree of embedding similarities means a higher probability that the substructure contains topology anomalies. To distill better embeddings of node attributes, we further introduce a graph contrastive learning scheme, which observes attribute anomalies in the meantime. In this way, GADMSL can detect both topology and attribute anomalies. Ultimately, extensive experiments on benchmark datasets show that GADMSL greatly improves detection performance (up to 7.30% AUC and 17.46% AUPRC gains) compared to state-of-the-art attributed networks anomaly detection algorithms.

Visual anomaly detection plays a crucial role in not only manufacturing inspection to find defects of products during manufacturing processes, but also maintenance inspection to keep equipment in optimum working condition particularly outdoors. Due to the scarcity of the defective samples, unsupervised anomaly detection has attracted great attention in recent years. However, existing datasets for unsupervised anomaly detection are biased towards manufacturing inspection, not considering maintenance inspection which is usually conducted under outdoor uncontrolled environment such as varying camera viewpoints, messy background and degradation of object surface after long-term working. We focus on outdoor maintenance inspection and contribute a comprehensive Maintenance Inspection Anomaly Detection (MIAD) dataset which contains more than 100K high-resolution color images in various outdoor industrial scenarios. This dataset is generated by a 3D graphics software and covers both surface and logical anomalies with pixel-precise ground truth. Extensive evaluations of representative algorithms for unsupervised anomaly detection are conducted, and we expect MIAD and corresponding experimental results can inspire research community in outdoor unsupervised anomaly detection tasks. Worthwhile and related future work can be spawned from our new dataset.

Recently, there has been a significant amount of interest in satellite telemetry anomaly detection (AD) using neural networks (NN). For AD purposes, the current approaches focus on either forecasting or reconstruction of the time series, and they cannot measure the level of reliability or the probability of correct detection. Although the Bayesian neural network (BNN)-based approaches are well known for time series uncertainty estimation, they are computationally intractable. In this paper, we present a tractable approximation for BNN based on the Monte Carlo (MC) dropout method for capturing the uncertainty in the satellite telemetry time series, without sacrificing accuracy. For time series forecasting, we employ an NN, which consists of several Long Short-Term Memory (LSTM) layers followed by various dense layers. We employ the MC dropout inside each LSTM layer and before the dense layers for uncertainty estimation. With the proposed uncertainty region and by utilizing a post-processing filter, we can effectively capture the anomaly points. Numerical results show that our proposed time series AD approach outperforms the existing methods from both prediction accuracy and AD perspectives.

The goal of Anomaly-Detection (AD) is to identify outliers, or outlying regions, from some unknown distribution given only a set of positive (good) examples. Few-Shot AD (FSAD) aims to solve the same task with a minimal amount of normal examples. Recent embedding-based methods, that compare the embedding vectors of queries to a set of reference embeddings, have demonstrated impressive results for FSAD, where as little as one good example is provided. A different approach, image-reconstruction-based, has been historically used for AD. The idea is to train a model to recover normal images from corrupted observations, assuming that the model will fail to recover regions when encountered with an out-of-distribution image. However, image-reconstruction-based methods were not yet used in the low-shot regime as they need to be trained on a diverse set of normal images in order to properly perform. We suggest using Masked Auto-Encoder (MAE), a self-supervised transformer model trained for recovering missing image regions based on their surroundings for FSAD. We show that MAE performs well by pre-training on an arbitrary set of natural images (ImageNet) and only fine-tuning on a small set of normal images. We name this method MAEDAY. We further find that MAEDAY provides an orthogonal signal to the embedding-based methods and the ensemble of the two approaches achieves very strong SOTA results. We also present a novel task of Zero-Shot AD (ZSAD) where no normal samples are available at training time. We show that MAEDAY performs surprisingly well at this task. Finally, we provide a new dataset for detecting foreign objects on the ground and demonstrate superior results for this task as well. Code is available at //github.com/EliSchwartz/MAEDAY .

In this article, we propose using deep learning and transformer architectures combined with classical machine learning algorithms to detect and identify text anomalies in texts. Deep learning model provides a very crucial context information about the textual data which all textual context are converted to a numerical representation. We used multiple machine learning methods such as Sentence Transformers, Auto Encoders, Logistic Regression and Distance calculation methods to predict anomalies. The method are tested on the texts data and we used syntactic data from different source injected into the original text as anomalies or use them as target. Different methods and algorithm are explained in the field of outlier detection and the results of the best technique is presented. These results suggest that our algorithm could potentially reduce false positive rates compared with other anomaly detection methods that we are testing.

In the research area of anomaly detection, novel and promising methods are frequently developed. However, most existing studies, especially those leveraging deep neural networks, exclusively focus on the detection task only and ignore the interpretability of the underlying models as well as their detection results. However, anomaly interpretation, which aims to provide explanation of why specific data instances are identified as anomalies, is an equally (if not more) important task in many real-world applications. In this work, we pursue highly interpretable anomaly detection via invariant rule mining. Specifically, we leverage decision tree learning and association rule mining to automatically generate invariant rules that are consistently satisfied by the underlying data generation process. The generated invariant rules can provide explicit explanation of anomaly detection results and thus are extremely useful for subsequent decision-making. Furthermore, our empirical evaluation shows that the proposed method can also achieve comparable performance in terms of AUC and partial AUC with popular anomaly detection models in various benchmark datasets.

Recently, contrastive learning (CL) has emerged as a successful method for unsupervised graph representation learning. Most graph CL methods first perform stochastic augmentation on the input graph to obtain two graph views and maximize the agreement of representations in the two views. Despite the prosperous development of graph CL methods, the design of graph augmentation schemes -- a crucial component in CL -- remains rarely explored. We argue that the data augmentation schemes should preserve intrinsic structures and attributes of graphs, which will force the model to learn representations that are insensitive to perturbation on unimportant nodes and edges. However, most existing methods adopt uniform data augmentation schemes, like uniformly dropping edges and uniformly shuffling features, leading to suboptimal performance. In this paper, we propose a novel graph contrastive representation learning method with adaptive augmentation that incorporates various priors for topological and semantic aspects of the graph. Specifically, on the topology level, we design augmentation schemes based on node centrality measures to highlight important connective structures. On the node attribute level, we corrupt node features by adding more noise to unimportant node features, to enforce the model to recognize underlying semantic information. We perform extensive experiments of node classification on a variety of real-world datasets. Experimental results demonstrate that our proposed method consistently outperforms existing state-of-the-art baselines and even surpasses some supervised counterparts, which validates the effectiveness of the proposed contrastive framework with adaptive augmentation.

Properly handling missing data is a fundamental challenge in recommendation. Most present works perform negative sampling from unobserved data to supply the training of recommender models with negative signals. Nevertheless, existing negative sampling strategies, either static or adaptive ones, are insufficient to yield high-quality negative samples --- both informative to model training and reflective of user real needs. In this work, we hypothesize that item knowledge graph (KG), which provides rich relations among items and KG entities, could be useful to infer informative and factual negative samples. Towards this end, we develop a new negative sampling model, Knowledge Graph Policy Network (KGPolicy), which works as a reinforcement learning agent to explore high-quality negatives. Specifically, by conducting our designed exploration operations, it navigates from the target positive interaction, adaptively receives knowledge-aware negative signals, and ultimately yields a potential negative item to train the recommender. We tested on a matrix factorization (MF) model equipped with KGPolicy, and it achieves significant improvements over both state-of-the-art sampling methods like DNS and IRGAN, and KG-enhanced recommender models like KGAT. Further analyses from different angles provide insights of knowledge-aware sampling. We release the codes and datasets at //github.com/xiangwang1223/kgpolicy.

To provide more accurate, diverse, and explainable recommendation, it is compulsory to go beyond modeling user-item interactions and take side information into account. Traditional methods like factorization machine (FM) cast it as a supervised learning problem, which assumes each interaction as an independent instance with side information encoded. Due to the overlook of the relations among instances or items (e.g., the director of a movie is also an actor of another movie), these methods are insufficient to distill the collaborative signal from the collective behaviors of users. In this work, we investigate the utility of knowledge graph (KG), which breaks down the independent interaction assumption by linking items with their attributes. We argue that in such a hybrid structure of KG and user-item graph, high-order relations --- which connect two items with one or multiple linked attributes --- are an essential factor for successful recommendation. We propose a new method named Knowledge Graph Attention Network (KGAT) which explicitly models the high-order connectivities in KG in an end-to-end fashion. It recursively propagates the embeddings from a node's neighbors (which can be users, items, or attributes) to refine the node's embedding, and employs an attention mechanism to discriminate the importance of the neighbors. Our KGAT is conceptually advantageous to existing KG-based recommendation methods, which either exploit high-order relations by extracting paths or implicitly modeling them with regularization. Empirical results on three public benchmarks show that KGAT significantly outperforms state-of-the-art methods like Neural FM and RippleNet. Further studies verify the efficacy of embedding propagation for high-order relation modeling and the interpretability benefits brought by the attention mechanism.

The world we see is ever-changing and it always changes with people, things, and the environment. Domain is referred to as the state of the world at a certain moment. A research problem is characterized as domain transfer adaptation when it needs knowledge correspondence between different moments. Conventional machine learning aims to find a model with the minimum expected risk on test data by minimizing the regularized empirical risk on the training data, which, however, supposes that the training and test data share similar joint probability distribution. Transfer adaptation learning aims to build models that can perform tasks of target domain by learning knowledge from a semantic related but distribution different source domain. It is an energetic research filed of increasing influence and importance. This paper surveys the recent advances in transfer adaptation learning methodology and potential benchmarks. Broader challenges being faced by transfer adaptation learning researchers are identified, i.e., instance re-weighting adaptation, feature adaptation, classifier adaptation, deep network adaptation, and adversarial adaptation, which are beyond the early semi-supervised and unsupervised split. The survey provides researchers a framework for better understanding and identifying the research status, challenges and future directions of the field.