97 citations · 205 across the 23 of their papers we have counts for
27 papers
Multiscale Reconstruction of Multiplex Networks with Higher-Order Interactions
Guangyuan Mei, Yao Cai, Suoyi Tan +4
Inferring network structure from dynamical observations is a fundamental inverse problem in complex systems. Existing approaches have largely focused on single-layer or pairwise in…
EMMR: Emotion-Mediated Multimodal Reasoning for Personality Assessment in Asynchronous Video Interviews
Dongsheng Hu, Tianyi Zhang, Chuang Liu +3
Asynchronous Video Interviews (AVIs) have become increasingly popular for personality assessment. Recent large language models (LLMs) have shown potential for personality assessmen…
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…
ESND: An Embedding-based Framework for Signed Network Dismantling
Chenwei Xie, Chuang Liu, Cong Li +2
Network dismantling aims to maximize the disintegration of a network by removing a specific set of nodes or edges and is applied to various tasks in diverse domains, such as cracki…
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…
Directed network comparison using motifs
Chenwei Xie, Qiao Ke, Haoyu Chen +2
Analyzing and characterizing the differences between networks is a fundamental and challenging problem in network science. Previously, most network comparison methods that rely on…
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…
A universal meta-heuristic framework for influence maximization in hypergraphs
Ming Xie, Xiu-Xiu Zhan, Chuang Liu +1
Influence maximization (IM) aims to select a small number of nodes that are able to maximize their influence in a network and covers a wide range of applications. Despite numerous…
A hyper-distance-based method for hypernetwork comparison
Tao Xu, Xiaowen Xie, Zi-Ke Zhang +2
Hypernetwork is a useful way to depict multiple connections between nodes, making it an ideal tool for representing complex relationships in network science. In recent years, there…
Vital node identification in hypergraphs via gravity model
Xiao-Wen Xie, Xiu-Xiu Zhan, Zi-Ke Zhang +1
Hypergraphs that can depict interactions beyond pairwise edges have emerged as an appropriate representation for modeling polyadic relations in complex systems. With the recent sur…
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…
The Aging Effect in Evolving Scientific Citation Networks
Feng Hu, Lin Ma, Xiu-Xiu Zhan +4
The study of citation networks is of interest to the scientific community. However, the underlying mechanism driving individual citation behavior remains imperfectly understood, de…
Exploring the effect of social media and spatial characteristics during the COVID-19 pandemic in China
Xiu-Xiu Zhan, Kaiyue Zhang, Lun Ge +6
The declaration of COVID-19 as a pandemic has largely amplified the spread of related information on social media, such as Twitter, Facebook, and WeChat.Unlike the previous studies…
Temporal Gravity Model for Important Nodes Identification in Temporal Networks
Jialin Bi, Ji Jin, Cunquan Qu +2
Identifying important nodes is one of the central tasks in network science, which is crucial for analyzing the structure of a network and understanding the dynamical processes on a…
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…
Information Spreading Dynamics on Adaptive Social Networks
Chuang Liu, Nan Zhou, Xiu-Xiu Zhan +2
There is currently growing interest in modeling the information diffusion on social networks across multi-disciplines. The majority of the corresponding research has focused on inf…
Mutual Feedback Between Epidemic Spreading and Information Diffusion
Xiu-Xiu Zhan, Chuang Liu, Ge Zhou +3
The impact that information diffusion has on epidemic spreading has recently attracted much attention. As a disease begins to spread in the population, information about the diseas…