Influence maximization (IM) has shown wide applicability in immense fields over the past
decades. Previous researches on IM mainly focused on the dyadic relationship but lacked …

The goal of this study is to comprehensively analyze the dynamics and structure of TikTok
research since its initial development. The scholarly composition of articles dealing with …

Under the background of the rapid development of live streaming shopping industry,
diversified live streaming platforms have emerged one after another. This study aims to …

Predicting the popularity of online content on social network can bring considerable
economic benefits to companies and marketers, and it has wide application in viral …

This paper addresses influence pathway discovery, a key emerging problem in today's
online media. We propose a discovery algorithm that leverages recently published work on …

When it comes to machine learning on graphs, Graph Neural Networks (GNNs) is a potent
tool. By iteratively propagating neural messages along the edges of the input graph, GNNs …

Graph neural networks (GNNs) typically exhibit superior performance in shallow
architectures. However, as the network depth increases, issues such as overfitting and …

Heterogeneous Graph Neural Networks (HGNNs) leverage diverse semantic relationships
in Heterogeneous Graphs (HetGs) and have demonstrated remarkable learning …

Short video hot spot classification is a fundamental method to grasp the focus of consumers
and improve the effectiveness of video marketing. The limitations of traditional short text …

This work introduces the ClimateSent-GAT Model, an innovative method that integrates
Graph Attention Networks (GATs) with techniques from natural language processing to …