This paper delves into the problem of correlated time-series forecasting in practical
applications, an area of growing interest in a multitude of fields such as stock price …

This paper studies the problem of traffic flow forecasting, which aims to predict future traffic
conditions on the basis of road networks and traffic conditions in the past. The problem is …

Clustering algorithms are widely used in many societal resource allocation applications,
such as loan approvals and candidate recruitment, among others, and hence, biased or …

Abstract The integration of Spiking Neural Networks (SNNs) and Graph Neural Networks
(GNNs) is gradually attracting attention due to the low power consumption and high …

Micro-video classification plays a central role in online content recommendation platforms,
such as Kwai and Tik-Tok. Existing works on video classification largely exploit the …

Relations among multiple entities are prevalent in many fields, and hypergraphs are widely
used to represent such group relations. Hence, machine learning on hypergraphs has …

Multimodal emotion recognition in conversation (MERC) refers to identifying and classifying
human emotional states by combining data from multiple different modalities (eg, audio …

Graph neural network (GNN)-based models have been extensively studied for
recommendations, as they can extract high-order collaborative signals accurately which is …

Recommender systems are vulnerable to injective attacks, which inject limited fake users
into the platforms to manipulate the exposure of target items to all users. In this work, we …

Hyperedges, as extensions of pairwise edges, can characterize higher-order relations
among multiple individuals. Due to the necessity of hypergraph detection in practical …