Triangle counting (TC) plays a vital role in graph mining systems. However, existing methods often rely on single intersection kernel function, limiting their performance across diverse datasets. To address this challenge, we proposed StepTC (a ...$$$$

Few-shot learning (FSL) aims to adapt quickly to new categories with limited samples. Despite significant progress in utilizing meta-learning for solving FSL tasks, challenges such as overfitting and poor generalization still exist. Building upon ...

A networked time series (NETS) is a family of time series on a given graph, one for each node. It has a wide range of applications from intelligent transportation to environment monitoring to smart grid management. An important task in such applications ...

Graph clustering has been a hot research topic and is widely used in many fields, such as community detection in social networks. Lots of works combining auto-encoder and graph neural networks have been applied to clustering tasks by utilizing node ...

This article studies the problem of semi-supervised learning on graphs, which aims to incorporate ubiquitous unlabeled knowledge (e.g., graph topology, node attributes) with few-available labeled knowledge (e.g., node class) to alleviate the scarcity ...

Recent years have witnessed the explosive growth of interaction behaviors in multimedia information systems, where multi-behavior recommender systems have received increasing attention by leveraging data from various auxiliary behaviors such as tip and ...

The drastic increase of metro passengers in recent years inevitably causes the overcrowdedness in the metro systems. Accurately predicting passenger flows at metro stations is critical for efficient metro system management, which helps alleviate such ...

With the rise of big data, graph computing has become prevalent in many fields. To effectively address such problems, large-scale graph computing systems have emerged. Most existing systems adopt memory-based computing frameworks. However, the rapid ...

Data scientists and researchers utilize enormous spatio-temporal data and build machine learning models to solve practical problems in diverse domains including intelligent transportation, urban planning, epidemic prediction, and many more. Extracting ...

Urban traffic light control is an important and challenging real-world problem. By regarding intersections as agents, most of the reinforcement learning-based methods generate agents’ actions independently. They can cause action conflict and result ...

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 ...

Nearest neighbor search aims at obtaining the samples in the database with the smallest distances from them to the queries, which is a basic task in a range of fields, including computer vision and data mining. Hashing is one of the most widely used ...

• A novel baseline for person re-identification is proposed to learn comprehensive global embedding, ensuring that more local regions (the number of local ...

Person re-identification (Re-ID) aims to retrieve person images from a large gallery given a query image of a person of interest. Global information and fine-grained local features are both essential for the representation. However, ...

In this paper, we propose an embarrassingly simple yet highly effective adversarial domain adaptation (ADA) method. We view ADA problem primarily from an optimization perspective and point out a fundamental dilemma, in that the real-world data ...

Since Intersection-over-Union (IoU) based optimization maintains the consistency of the final IoU prediction metric and losses, it has been widely used in both regression and classification branches of single-stage 2D object detectors. Recently, ...

Face clustering is a promising way to scale up face recognition systems using large-scale unlabeled face images. It remains challenging to identify small or sparse face image clusters that we call hard clusters, which is caused by the ...

In this paper, we explore the details in video recognition with the aim to improve the accuracy. It is observed that most failure cases in recent works fall on the mis-classifications among very similar actions (such as high kick vs. side kick) ...

In online advertising, click-through rate (CTR) prediction typically utilizes click data to train models for estimating the probability of a user clicking on an item. However, the different presentations of an item, including its position and contextual ...

With the increasing need for emotion analysis, facial action unit (AU) recognition has attracted much more attention as a fundamental task for affective computing. Although deep learning has boosted the performance of AU recognition to a new level in ...

Unsupervised Person Re-identification (U-ReID) with pseudo labeling recently reaches a competitive performance compared to fully-supervised ReID methods based on modern clustering algorithms. However, such clustering-based scheme becomes computationally ...