ACM Transactions on Knowledge Discovery from Data

Image retrieval keeps attracting a lot of attention from both academic and industry over past years due to its variety of useful applications. Due to the rapid growth of deep learning approaches, more better feature vectors of images could be discovered ...

Consensus clustering provides an elegant framework to aggregate multiple weak clustering results to learn a consensus one that is more robust and stable than a single result. However, most of the existing methods usually use all data for consensus ...

Graph neural networks (GNNs) have been widely used for graph structure learning and achieved excellent performance in tasks such as node classification and link prediction. Real-world graph networks imply complex and various semantic information and are ...

Traffic flow prediction has always been the focus of research in the field of Intelligent Transportation Systems, which is conducive to the more reasonable allocation of basic transportation resources and formulation of transportation policies. The spread ...

Crowdsourcing truth inference aims to assign a correct answer to each task from candidate answers that are provided by crowdsourced workers. A common approach is to generate workers’ reliabilities to represent the quality of answers. Although crowdsourced ...

Given a dense tensor, how can we efficiently discover hidden relations and patterns in static and online streaming settings? Tucker decomposition is a fundamental tool to analyze multidimensional arrays in the form of tensors. However, existing Tucker ...

Hidden community is a useful concept proposed recently for social network analysis. Hidden communities indicate some weak communities whose most members also belong to other stronger dominant communities. Dominant communities could form a layer that ...

Urban vibrancy describes the prosperity, diversity, and accessibility of urban areas, which is vital to a city’s socio-economic development and sustainability. While many efforts have been made for statically measuring and evaluating urban vibrancy, there ...

Self-supervised graph-level representation learning has recently received considerable attention. Given varied input distributions, jointly learning graphs’ unique and common features is vital to downstream tasks. Inspired by graph contrastive learning (...

Graph-based Multi-View Clustering (GMVC) has received extensive attention due to its ability to capture the neighborhood relationship among data points from diverse views. However, most existing approaches construct similarity graphs from the original ...

Anomaly detection on multivariate time series (MTS) is an important research topic in data mining, which has a wide range of applications in information technology, financial management, manufacturing system, and so on. However, the state-of-the-art ...

With the rapid development of data stream, multi-label algorithms for mining dynamic data become more and more important. At the same time, when data distribution changes, concept drift will occur, which will make the existing classification models lose ...

In recent years, many spatial-temporal graph convolutional network (STGCN) models are proposed to deal with the spatial-temporal network data forecasting problem. These STGCN models have their own advantages, i.e., each of them puts forward many effective ...

In this study, sentiment classification and emotion distribution learning across domains are both formulated as a semi-supervised domain adaptation problem, which utilizes a small amount of labeled documents in the target domain for model training. By ...

As the scope of receptive field and the depth of Graph Neural Networks (GNNs) are two completely orthogonal aspects for graph learning, existing GNNs often have shallow layers with truncated-receptive field and far from achieving satisfactory performance. ...