97 citations · 205 across the 23 of their papers we have counts for
7 papers · 1 filter
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…
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…
Influence Maximization in Hypergraphs
Ming Xie, Xiu-Xiu Zhan, Chuang Liu +1
Influence maximization in complex networks, i.e., maximizing the size of influenced nodes via selecting K seed nodes for a given spreading process, has attracted great attention in…
Quantification of network structural dissimilarities based on graph embedding
Zhipeng Wang, Xiu-Xiu Zhan, Chuang Liu +1
Identifying and quantifying structural dissimilarities between complex networks is a fundamental and challenging problem in network science. Previous network comparison methods are…
Measuring and utilizing temporal network dissimilarity
Xiu-Xiu Zhan, Chuang Liu, Zhipeng Wang +3
Quantifying the structural and functional differences of temporal networks is a fundamental and challenging problem in the era of big data. This work proposes a temporal dissimilar…
SI-spreading-based network embedding in static and temporal networks
Xiu-Xiu Zhan, Ziyu Li, Naoki Masuda +2
Link prediction can be used to extract missing information, identify spurious interactions as well as forecast network evolution. Network embedding is a methodology to assign coord…