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Due to the intricate dynamics of multivariate time series in cyber-physical system, unsupervised anomaly detection has always been a research hotspot. Common methods are mainly based on reducing reconstruction error or maximizing estimated ...

Anomaly detection on attributed networks is a vital task in graph data mining and has been widely applied in many real-world scenarios. Despite the promising performance, existing contrastive learning-based anomaly detection models still suffer ...

Video Anomaly Detection (VAD) is a significant task, which refers to taking a video clip as input and outputting class labels, e.g., normal or abnormal, at the frame level. Wang et al. proposed a method called DSTJiP, which trains the model by ...

In rapidly evolving industrial IT systems, the integration of sensor networks has become the cornerstone of operational workflows. These networks diligently collect data in the form of time series, where the format intertwines closely with ...

We present TOPOMA, a time-series orthogonal projection operator with moving average that can identify anomalous points for multivariate time-series, without requiring any labels nor training. Despite intensive research the problem has received, it ...

Unsupervised learning-based anomaly detection using autoencoders has gained importance since anomalies behave differently than normal data when reconstructed from a well-regularized latent space. Existing research shows that retaining valuable ...

There have been multiple attempts to tackle the problem of identifying abnormal instances that have inconsistent behaviors in multi-view data (i.e., multi-view anomalies) but the problem still remains a challenge. In this paper, we propose an ...

In this paper, we propose QWalkVec, a quantum walk-based node embedding method. A quantum walk is a quantum version of a random walk that demonstrates a faster propagation than a random walk on a graph. We focus on the fact that the effect of the ...

Graph representation learning methods have significantly transformed applications in various domains. However, their success often comes at the cost of interpretability, hindering them from being adopted in critical decision-making scenarios. In ...

Deep Neural Networks have shown vulnerability to sparse adversarial attack, which involves perturbing only a limited number of pixels. Identifying the coordinates requiring perturbation in sparse attacks poses a significant computational ...${}_{\mathrm{}}$${}_{\mathrm{}}$${}_{\mathrm{}}$

Malevolent Dialogue Response Detection has gained much attention from the NLP community recently. Existing methods have difficulties in effectively utilizing the conversational context and the malevolent information. In this work, we propose a ...

Recently, multi-head Graph Attention Networks (GATs) have incorporated attention mechanisms to generate more enriched feature embeddings, demonstrating significant potential in Knowledge Graph Completion (KGC) tasks. However, existing GATs based ...

Multi-label recognition with limited annotations has been gaining attention recently due to the costs of thorough dataset annotation. Despite significant progress, current methods for simulating partial labels utilize a strategy that uniformly ...

Node classification is a pivotal task in spam detection, community identification, and social network analysis. Compared with traditional graph learning methods, Graph Neural Networks (GNN) show superior performance in prediction tasks, but ...

Fairness is increasingly becoming an important issue in machine learning. Representation learning is a popular approach recently that aims at mitigating discrimination by generating representation on the historical data so that further predictive ...

Record linkage is the process of identifying and matching records from different datasets that refer to the same entity. This process can be framed as a pairwise binary classification problem, where a classification model predicts if a pair of ...${}_{\mathrm{}}$

Recently, representation learning with contrastive learning algorithms has been successfully applied to challenging unlabeled datasets. However, these methods are unable to distinguish important features from unimportant ones under simply ...

Clustering as one of the main research methods in data mining, with the generation of multi-view data, multi-view clustering has become the research hotspot at present. Many excellent multi-view clustering algorithms have been proposed to solve ...

This paper introduces the multivariate beta mixture model (MBMM), a new probabilistic model for soft clustering. MBMM adapts to diverse cluster shapes because of the flexible probability density function of the multivariate beta distribution. We ...

AutoML has witnessed effective applications in the field of supervised learning – mainly in classification tasks – where the goal is to find the best machine-learning pipeline when a ground truth is available. This is not the case for unsupervised ...

Analyzing the properties of subsequences within time series can reveal hidden patterns and improve the quality of time series clustering. However, most existing methods for subsequence analysis require point-to-point alignment, which is sensitive ...${\mathbb{}}^{}$

In this paper, we introduce a novel Distributed Markov Chain Monte Carlo (MCMC) inference method for the Bayesian Non-Parametric Latent Block Model (DisNPLBM), employing the Master/Worker architecture. Our non-parametric co-clustering algorithm ...

Conventional fair graph clustering methods face two primary challenges: i) They prioritize balanced clusters at the expense of cluster cohesion by imposing rigid constraints, ii) Existing methods of both individual and group-level fairness in ...$\mathit{}$

Given a large graph with few node labels, how can we (a) identify whether there is generalized network-effects  (GNE) or not, (b) estimate GNE to explain the interrelations among node classes, and (c) exploit GNE efficiently to improve the ...$$