Data Mining Practical Machine Learning Tools and Techniques

Anomaly detection, a.k.a. outlier detection or novelty detection, has been a lasting yet active research area in various research communities for several decades. There are still some unique problem complexities and challenges that require advanced approaches. In recent years, deep learning enabled anomaly detection, i.e., deep anomaly detection, has emerged as a critical direction. This article surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in 3 high-level categories and 11 fine-grained categories of the methods. We review their key intuitions, objective functions, underlying assumptions, advantages, and disadvantages and discuss how they address the aforementioned challenges. We further discuss a set of possible future opportunities and new perspectives on addressing the challenges.

Deep Learning for Anomaly Detection: A Review

Anomaly detection, a.k.a. outlier detection, has been a lasting yet active research area in various research communities for several decades. There are still some unique problem complexities and challenges that require advanced approaches. In recent years, deep learning enabled anomaly detection, i.e., deep anomaly detection, has emerged as a critical direction. This paper reviews the research of deep anomaly detection with a comprehensive taxonomy of detection methods, covering advancements in three high-level categories and 11 fine-grained categories of the methods. We review their key intuitions, objective functions, underlying assumptions, advantages and disadvantages, and discuss how they address the aforementioned challenges. We further discuss a set of possible future opportunities and new perspectives on addressing the challenges.

Attributed networks are ubiquitous and form a critical component of modern information infrastructure, where additional node attributes complement the raw network structure in knowledge discovery. Recently, detecting anomalous nodes on attributed networks has attracted an increasing amount of research attention, with broad applications in various high-impact domains, such as cybersecurity, finance, and healthcare. Most of the existing attempts, however, tackle the problem with shallow learning mechanisms by ego-network or community analysis, or through subspace selection. Undoubtedly, these models cannot fully address the computational challenges on attributed networks. For example, they often suffer from the network sparsity and data nonlinearity issues, and fail to capture the complex interactions between different information modalities, thus negatively impact the performance of anomaly detection. To tackle the aforementioned problems, in this paper, we study the anomaly detection problem on attributed networks by developing a novel deep model. In particular, our proposed deep model: (1) explicitly models the topological structure and nodal attributes seamlessly for node embedding learning with the prevalent graph convolutional network (GCN); and (2) is customized to address the anomaly detection problem by virtue of deep autoencoder that leverages the learned embeddings to reconstruct the original data. The synergy between GCN and autoencoder enables us to spot anomalies by measuring the reconstruction errors of nodes from both the structure and the attribute perspectives. Extensive experiments on real-world attributed network datasets demonstrate the efficacy of our proposed algorithm.

Deep Anomaly Detection on Attributed Networks.

This work considers the question of how convenient access to copious data impacts our ability to learn causal effects and relations. In what ways is learning causality in the era of big data different from—or the same as—the traditional one? To answer this question, this survey provides a comprehensive and structured review of both traditional and frontier methods in learning causality and relations along with the connections between causality and machine learning. This work points out on a case-by-case basis how big data facilitates, complicates, or motivates each approach.

https://dl.acm.org/doi/pdf/10.1145/3397269

A Survey of Learning Causality with Data: Problems and Methods

There is an increasing attention on next-item recommendation systems to infer the dynamic user preferences with sequential user interactions. While the semantics of an item can change over time and across users, the item correlations defined by user interactions in the short term can be distilled to capture such change, and help in uncovering the dynamic user preferences. Thus, we are motivated to develop a novel next-item recommendation framework empowered by sequential hypergraphs. Specifically, the framework: (i) adopts hypergraph to represent the short-term item correlations and applies multiple convolutional layers to capture multi-order connections in the hypergraph; (ii) models the connections between different time periods with a residual gating layer; and (iii) is equipped with a fusion layer to incorporate both the dynamic item embedding and short-term user intent to the representation of each interaction before feeding it into the self-attention layer for dynamic user modeling. Through experiments on datasets from the ecommerce sites Amazon and Etsy and the information sharing platform Goodreads, the proposed model can significantly outperform the state-of-the-art in predicting the next interesting item for each user.

https://dl.acm.org/doi/pdf/10.1145/3397271.3401133

Next-item Recommendation with Sequential Hypergraphs

Performing anomaly detection on attributed networks concerns with finding nodes whose patterns or behaviors deviate significantly from the majority of reference nodes. Its success can be easily found in many real-world applications such as network intrusion detection, opinion spam detection and system fault diagnosis, to name a few. Despite their empirical success, a vast majority of existing efforts are overwhelmingly performed in an unsupervised scenario due to the expensive labeling costs of ground truth anomalies. In fact, in many scenarios, a small amount of prior human knowledge of the data is often effortless to obtain, and getting it involved in the learning process has shown to be effective in advancing many important learning tasks. Additionally, since new types of anomalies may constantly arise over time especially in an adversarial environment, the interests of human expert could also change accordingly regarding to the detected anomaly types. It brings further challenges to conventional anomaly detection algorithms as they are often applied in a batch setting and are incapable to interact with the environment. To tackle the above issues, in this paper, we investigate the problem of anomaly detection on attributed networks in an interactive setting by allowing the system to proactively communicate with the human expert in making a limited number of queries about ground truth anomalies. Our objective is to maximize the true anomalies presented to the human expert after a given budget is used up. Along with this line, we formulate the problem through the principled multi-armed bandit framework and develop a novel collaborative contextual bandit algorithm, named GraphUCB. In particular, our developed algorithm: (1) explicitly models the nodal attributes and node dependencies seamlessly in a joint framework; and (2) handles the exploration-exploitation dilemma when querying anomalies of different types. Extensive experiments on real-world datasets show the improvement of the proposed algorithm over the state-of-the-art algorithms.

https://dl.acm.org/doi/pdf/10.1145/3289600.3290964

Interactive Anomaly Detection on Attributed Networks

Attributed networks nowadays are ubiquitous in a myriad of high-impact applications, such as social network analysis, financial fraud detection, and drug discovery. As a central analytical task on attributed networks, node classification has received much attention in the research community. In real-world attributed networks, a large portion of node classes only contains limited labeled instances, rendering a long-tail node class distribution. Existing node classification algorithms are unequipped to handle the few-shot node classes. As a remedy, few-shot learning has attracted a surge of attention in the research community. Yet, few-shot node classification remains a challenging problem as we need to address the following questions: (i) How to extract meta-knowledge from an attributed network for few-shot node classification? (ii) How to identify the informativeness of each labeled instance for building a robust and effective model? To answer these questions, in this paper, we propose a graph meta-learning framework -- Graph Prototypical Networks (GPN). By constructing a pool of semi-supervised node classification tasks to mimic the real test environment, GPN is able to perform meta-learning on an attributed network and derive a highly generalizable model for handling the target classification task. Extensive experiments demonstrate the superior capability of GPN in few-shot node classification.

https://dl.acm.org/doi/pdf/10.1145/3340531.3411922

Graph Prototypical Networks for Few-shot Learning on Attributed Networks

Text classification is a critical research topic with broad applications in natural language processing. Recently, graph neural networks (GNNs) have received increasing attention in the research community and demonstrated their promising results on this canonical task. Despite the success, their performance could be largely jeopardized in practice since they are: (1) unable to capture high-order interaction between words; (2) inefficient to handle large datasets and new documents. To address those issues, in this paper, we propose a principled model -- hypergraph attention networks (HyperGAT), which can obtain more expressive power with less computational consumption for text representation learning. Extensive experiments on various benchmark datasets demonstrate the efficacy of the proposed approach on the text classification task.

Kaize Ding

Papers

Deep Anomaly Detection on Attributed Networks.

Next-item Recommendation with Sequential Hypergraphs

Interactive Anomaly Detection on Attributed Networks

Graph Prototypical Networks for Few-shot Learning on Attributed Networks

Be More with Less: Hypergraph Attention Networks for Inductive Text Classification.