2 citations · 3 across the 4 of their papers we have counts for
6 papers
HIP: Model-Agnostic Hypergraph Influence Prediction via Distance-Centrality Fusion and Neural ODEs
Su-Su Zhang, JinFeng Xie, Yang Chen +4
Predicting user influence in social networks is a critical problem, and hypergraphs, as a prevalent higher-order modeling approach, provide new perspectives for this task. However,…
Structural-Aware Key Node Identification in Hypergraphs via Representation Learning and Fine-Tuning
Xiaonan Ni, Guangyuan Mei, Su-Su Zhang +4
Evaluating node importance is a critical aspect of analyzing complex systems, with broad applications in digital marketing, rumor suppression, and disease control. However, existin…
Modeling Coupled Epidemic-Information Dynamics via Reaction-Diffusion Processes on Multiplex Networks with Media and Mobility Effects
Guangyuan Mei, Yao Cai, Su-Su Zhang +3
While most existing epidemic models focus on the influence of isolated factors, infectious disease transmission is inherently shaped by the complex interplay of multiple interactin…
BCIM: Budget and capacity constrained influence maximization in multilayer networks
Su-Su Zhang, Chuang Liu, Huijuan Wang +2
Influence maximization (IM) seeks to identify a seed set that maximizes influence within a network, with applications in areas such as viral marketing, disease control, and politic…
Locating influential nodes in hypergraphs via fuzzy collective influence
Su-Su Zhang, Xiaoyan Yu, Gui-Quan Sun +2
Complex contagion phenomena, such as the spread of information or contagious diseases, often occur among the population due to higher-order interactions between individuals. Indivi…
Influence maximization in multilayer networks based on adaptive coupling degree
Su-Su Zhang, Ming Xie, Chuang Liu +1
Influence Maximization(IM) aims to identify highly influential nodes to maximize influence spread in a network. Previous research on the IM problem has mainly concentrated on singl…