6 papers
Entropy-Driven Sensor Deployment and Source Detection in Hypergraphs
Qiao Ke, Chengjun Zhang, Chuang Liu +3
Identifying the diffusion source in complex networks is critical for understanding and controlling epidemic spread. In realistic settings, full observation of node states is rarely…
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…
Source Detection in Hypergraph Epidemic Dynamics using a Higher-Order Dynamic Message Passing Algorithm
Qiao Ke, Naoki Masuda, Zhen Jin +2
Source detection is crucial for capturing the dynamics of real-world infectious diseases and informing effective containment strategies. Most existing approaches to source detectio…
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…