ACM Transactions on Knowledge Discovery from Data

Within a large database 𝒢 containing graphs with labeled nodes and directed, multi-edges; how can we detect the anomalous graphs? Most existing work are designed for plain (unlabeled) and/or simple (unweighted) graphs. We introduce CODEtect, the first ...

The anchor graph structure has been widely used to speed up large-scale multi-view clustering and exhibited promising performance. How to effectively integrate the anchor graphs on multiple views to achieve enhanced clustering performance still remains a ...

In real-world applications, a single instance could have more than one label. To solve this task, multi-label learning methods emerged in recent years. It is a more challenging problem for many reasons, such as complex label correlation, long-tail label ...

Graph-based multi-view learning has attracted much attention due to the efficacy of fusing the information from different views. However, most of them exhibit high computational complexity. We propose an anchor-based bipartite graph embedding approach to ...

To meet the growing bike-sharing demands and make people’s travel convenient, the companies need to add new stations at locations where demands exceed supply. Before making reliable decisions on adding new stations, it is required to understand the ...

In most domains, anomaly detection is typically cast as an unsupervised learning problem because of the infeasibility of labeling large datasets. In this setup, the evaluation and comparison of different anomaly detection algorithms is difficult. Although ...

Traditional embedding methodologies, also known as dimensionality reduction techniques, assume the availability of exact pairwise distances between the high-dimensional objects that will be embedded in a lower dimensionality. In this article, we propose ...

Structured text with plentiful hierarchical structure information is an important part in real-world complex texts. Structured text classification is attracting more attention in natural language processing due to the increasing complexity of application ...

The COVID-19 pandemic has caused the society lockdowns and a large number of deaths in many countries. Potential transmission cluster discovery is to find all suspected users with infections, which is greatly needed to fast discover virus transmission ...

Background: Looking pattern differences are shown to separate individuals with Autism Spectrum Disorder (ASD) and Typically Developing (TD) controls. Recent studies have shown that, in children with ASD, these patterns change with intellectual and social ...

Effective time-series forecasting methods are of significant importance to solve a broad spectrum of research problems. Deep probabilistic forecasting techniques have recently been proposed for modeling large collections of time-series. However, these ...

Recent advancements in deep learning techniques have transformed the area of semantic text matching (STM). However, most state-of-the-art models are designed to operate with short documents such as tweets, user reviews, comments, and so on. These models ...

Given a million escort advertisements, how can we spot near-duplicates? Such micro-clusters of ads are usually signals of human trafficking (HT). How can we summarize them to convince law enforcement to act? Spotting micro-clusters of near-duplicate ...

This article¹ studies how to schedule hyperparameters to improve generalization of both centralized single-machine stochastic gradient descent (SGD) and distributed asynchronous SGD (ASGD). SGD augmented with momentum variants (e.g., heavy ball momentum (...

Crowdsourcing techniques have been extensively explored in the past decade, including task allocation, quality assessment, and so on. Most of professional crowdsourcing platforms adopt the fixed pricing scheme to offer a fixed price for crowd tasks. It is ...