ACM Transactions on Knowledge Discovery from Data

Truth inference can help solve some difficult problems of data integration in crowdsourcing. Crowdsourced workers are not experts and their labeling ability varies greatly; therefore, in practical applications, it is difficult to determine whether the ...

With an increasing outreach of digital platforms in our lives, researchers have taken a keen interest in studying different facets of social interactions. Analyzing the spread of information (aka diffusion) has brought forth multiple research areas such ...

While urban rail transit systems are playing an increasingly important role in meeting the transportation demands of people, precise awareness of how the human crowd is distributed within such a system is highly necessary, which serves a range of ...

Personalized federated learning (PFL) has emerged as a paradigm to provide a personalized model that can fit the local data distribution of each client. One natural choice for PFL is to leverage the fast adaptation capability of meta-learning, where it ...

What is the best way to match the nodes of two graphs? This graph alignment problem generalizes graph isomorphism and arises in applications from social network analysis to bioinformatics. Some solutions assume that auxiliary information on known matches ...

Analysis of social networks with limited data access is challenging for third parties. To address this challenge, a number of studies have developed algorithms that estimate properties of social networks via a simple random walk. However, most existing ...

In this article, we formulate lifelong learning as an online transfer learning procedure over consecutive tasks, where learning a given task depends on the accumulated knowledge. We propose a novel theoretical principled framework, lifelong online ...

Accurate citywide traffic inference is critical for improving intelligent transportation systems with smart city applications. However, this task is very challenging given the limited training data, due to the high cost of sensor installment and ...

Transportation demand forecasting is a critical precondition of optimal online transportation dispatch, which will greatly reduce drivers’ wasted mileage and customers’ waiting time, contributing to economic and environmental sustainability. Though ...

Data explosion in the information society drives people to develop more effective ways to extract meaningful information. Extracting semantic information and relational information has emerged as a key mining primitive in a wide variety of practical ...

Predicting traffic accidents can help traffic management departments respond to sudden traffic situations promptly, improve drivers’ vigilance, and reduce losses caused by traffic accidents. However, the causality of traffic accidents is complex and ...

Tourism is an important industry and a popular leisure activity involving billions of tourists per annum. One challenging problem tourists face is identifying attractive Places-of-Interest (POIs) and planning the personalized trip with time constraints. ...

Graph Convolutional Networks (GCNs) have been widely used for collaborative filtering, due to their effectiveness in exploiting high-order collaborative signals. However, two issues have not been well addressed by existing studies. First, usually only one ...

For a new city that is committed to promoting Electric Vehicles (EVs), it is significant to plan the public charging infrastructure where charging demands are high. However, it is difficult to predict charging demands before the actual deployment of EV ...