Xiao-Ke Xu
Dados Biográficos
| ID | 4156490 |
|---|---|
| NOME | Xiao-Ke Xu |
| PRENOMES | Xiao-Ke |
| SOBRENOME | Xu |
| ASSINATURA | XU X |
| AFILIAÇÕES | Beijing Normal University |
| ORCID | 0000-0002-9148-3145 |
| VERIFICADO | Sim |
| TOTAL DE OBRAS | 19 |
| TOTAL DE CITAÇÕES | 1 |
| TOTAL COMO AUTOR | 19 |
| TOTAL COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2020 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2026 |
| ÍNDICE H | 1 |
EntroBot
Hypergraph Community Detection Based on Higher-Order Topology and Information Flow
Hypergraph community detection has gained significant attention due to reflecting higher-order mesoscale structures and facilitating functional analysis. However, previous methods mainly rely on a single and biased hypergraph property, i.e., static hyperedge structures or dynamic information flow, but largely overlook their intrinsic cooccurrence and potential conflicts in real-world situations. To address this issue, we first model hypergraph co…
Detecting Social Bots via Multi-Motif Attention Fusion Network
The rapid growth of social networks has enabled the widespread deployment of social bots that manipulate public opinion and disseminate misinformation, thereby posing significant cybersecurity risks. Most existing detection methods for social bots primarily focus on individual features and low-order neighbor information, while neglecting the higher-order topological semantics embedded in frequent substructures, or motifs. This oversight limits th…
Propagation Motifs as Codewords for Fake News Detection
MSTD
Online social media has become a primary channel for the dissemination of fake news, whose rapid diffusion can generate substantial cross-sectoral impacts. However, most structure-based fake news detection methods are essentially static and overlook temporal dynamics, thereby limiting performance and obscuring underlying mechanisms. To bridge this gap, we propose a framework based on temporal propagation motifs (TPMs) that jointly models structur…
Public awareness, knowledge and attitudes toward thalassemia and its screening in five high-prevalence countries
While public awareness of thalassemia is relatively high, knowledge levels remain poor, highlighting the need for targeted educational interventions and awareness campaigns to address knowledge gaps and improve attitudes toward thalassemia screening in diverse populations
Cwiiif
The identification of influential nodes in multilayer networks is a rapidly growing area in network science. However, insufficient consideration of both inter- and intra-layer weights in existing research has limited the effectiveness of node identification methods. To address this gap, we propose a novel algorithm, coupling weighted intra-layer and inter-layer influence factors (CWIIIF), which accurately identifies nodes that exert significant i…
Privacy-Preserving Multilayer Community Detection via Federated Learning
Existing frameworks of privacy-preserving multilayer community detection have room for improving detection performance and reducing communication overhead. To address these issues, we propose a novel privacy-preserving multilayer community detection framework based on federated learning which is called federated multilayer community detection (FMCD). First, we propose a novel aggregation strategy by utilizing the network average degree of local n…
Higher Order Local Search Assisted Community Detection in Signed Networks
Community detection in signed networks reveals mesoscale structures and sign characteristics, facilitating accurate and realistic understanding of real-life signed networks. Given the complexity of signed community structures and the NP-hard challenge of achieving optimal partitions, modularity optimization using memetic algorithms has emerged as a promising approach. However, the identified signed partitions still face challenges in modularity q…
Link Prediction by Combining Local Structure Similarity With Node Behavior Synchronization
Link prediction plays a crucial role in discovering missing information and understanding evolutionary mechanisms in complex networks, so several algorithms have been proposed. However, existing link prediction algorithms usually rely only on structural information, limiting the potential for further accuracy improvement. Recently, the significance of node behaviour synchronization in network reconstruction has emerged. Both link prediction and n…
Predicting Higher Order Links in Social Interaction Networks
Link prediction is a significant research problem in network science and has widespread applications. To date, much efforts have focused on predicting the links generated by pairwise interactions, but little is known about the predictability of links created by higher order interaction patterns. In this study, we investigated a new framework for predicting the links of different orders in social interaction networks based on edge orbit degrees (E…
Predicting Scientist Collaboration by Multiple Motif Features
Scientist collaboration is of great significance for knowledge production and scientific development, and the prediction of connection and the intensity in collaboration networks are essential to understand collaboration relationships between scientists. In previous studies, most scholars only use local structure similarity to infer scientist collaboration modes, which leads to failure to accurately predict collaboration relationships between sci…
Beyond the Targeted Customer
Following an innovative large-scale field experiment, this study measures the extent of the spillover effect through social influence in a social advertising campaign. Results show that, even when the campaign does not have a significant direct effect on targeted customers, it still creates a significant spillover effect on targets’ peers because of social influence. The spillover effect is achieved through weak ties (acquaintances) rather than s…
Impact of Covid-19 pneumonia on population mobility in ethnic minority areas
The main channel for the spread of COVID-19 is macro and micro population flow, and population and material flow are important means to promote economic development and ensure people’s living standards, especially for ethnic areas with relatively backward economic development. Based on Baidu migration data, this paper confirms that population flow data has a strong correlation with urban GDP indicators, and quantitatively analyzes the population …
Mitigating Covid-19 Transmission in Schools With Digital Contact Tracing
Precision mitigation of COVID-19 is in pressing need for postpandemic time with the absence of pharmaceutical interventions. In this study, the effectiveness and cost of digital contact tracing (DCT) technology-based on-campus mitigation strategy are studied through epidemic simulations using high-resolution empirical contact networks of teachers and students. Compared with traditional class, grade, and school closure strategies, the DCT-based st…
Multiple bursts of highly retweeted articles on social media
The Family of Assortativity Coefficients in Signed Social Networks
Signed networks are a special type of networks with both positive and negative edges, and the signs of links play a significant role in functional analysis and structural evolution. Because of the particularity of signed networks, the existing methods to measure their degree assortativity only rely on dividing the original network by link signs ignoring the signs of node degrees, which cannot measure the complicated degree mixing patterns. In thi…
Low dispersion in the infectiousness of Covid-19 cases implies difficulty in control
The individual infectiousness of coronavirus disease 2019 (COVID-19), quantified by the number of secondary cases of a typical index case, is conventionally modelled by a negative-binomial (NB) distribution. Based on patient data of 9120 confirmed cases in China, we calculated the variation of the individual infectiousness, i.e., the dispersion parameter k of the NB distribution, at 0.70 (95% confidence interval: 0.59, 0.98). This suggests that t…
Realistic modelling of information spread using peer-to-peer diffusion patterns
The Family of Assortativity Coefficients in Signed Social Networks
Signed networks are a special type of networks with both positive and negative edges, and the signs of links play a significant role in functional analysis and structural evolution. Because of the particularity of signed networks, the existing methods to measure their degree assortativity only rely on dividing the original network by link signs ignoring the signs of node degrees, which cannot measure the complicated degree mixing patterns. In thi…
Low dispersion in the infectiousness of Covid-19 cases implies difficulty in control
The individual infectiousness of coronavirus disease 2019 (COVID-19), quantified by the number of secondary cases of a typical index case, is conventionally modelled by a negative-binomial (NB) distribution. Based on patient data of 9120 confirmed cases in China, we calculated the variation of the individual infectiousness, i.e., the dispersion parameter k of the NB distribution, at 0.70 (95% confidence interval: 0.59, 0.98). This suggests that t…
Realistic modelling of information spread using peer-to-peer diffusion patterns
Mitigating Covid-19 Transmission in Schools With Digital Contact Tracing
Precision mitigation of COVID-19 is in pressing need for postpandemic time with the absence of pharmaceutical interventions. In this study, the effectiveness and cost of digital contact tracing (DCT) technology-based on-campus mitigation strategy are studied through epidemic simulations using high-resolution empirical contact networks of teachers and students. Compared with traditional class, grade, and school closure strategies, the DCT-based st…
Multiple bursts of highly retweeted articles on social media
Impact of Covid-19 pneumonia on population mobility in ethnic minority areas
The main channel for the spread of COVID-19 is macro and micro population flow, and population and material flow are important means to promote economic development and ensure people’s living standards, especially for ethnic areas with relatively backward economic development. Based on Baidu migration data, this paper confirms that population flow data has a strong correlation with urban GDP indicators, and quantitatively analyzes the population …
Predicting Scientist Collaboration by Multiple Motif Features
Scientist collaboration is of great significance for knowledge production and scientific development, and the prediction of connection and the intensity in collaboration networks are essential to understand collaboration relationships between scientists. In previous studies, most scholars only use local structure similarity to infer scientist collaboration modes, which leads to failure to accurately predict collaboration relationships between sci…
Beyond the Targeted Customer
Following an innovative large-scale field experiment, this study measures the extent of the spillover effect through social influence in a social advertising campaign. Results show that, even when the campaign does not have a significant direct effect on targeted customers, it still creates a significant spillover effect on targets’ peers because of social influence. The spillover effect is achieved through weak ties (acquaintances) rather than s…
Link Prediction by Combining Local Structure Similarity With Node Behavior Synchronization
Link prediction plays a crucial role in discovering missing information and understanding evolutionary mechanisms in complex networks, so several algorithms have been proposed. However, existing link prediction algorithms usually rely only on structural information, limiting the potential for further accuracy improvement. Recently, the significance of node behaviour synchronization in network reconstruction has emerged. Both link prediction and n…
Predicting Higher Order Links in Social Interaction Networks
Link prediction is a significant research problem in network science and has widespread applications. To date, much efforts have focused on predicting the links generated by pairwise interactions, but little is known about the predictability of links created by higher order interaction patterns. In this study, we investigated a new framework for predicting the links of different orders in social interaction networks based on edge orbit degrees (E…
Cwiiif
The identification of influential nodes in multilayer networks is a rapidly growing area in network science. However, insufficient consideration of both inter- and intra-layer weights in existing research has limited the effectiveness of node identification methods. To address this gap, we propose a novel algorithm, coupling weighted intra-layer and inter-layer influence factors (CWIIIF), which accurately identifies nodes that exert significant i…
Privacy-Preserving Multilayer Community Detection via Federated Learning
Existing frameworks of privacy-preserving multilayer community detection have room for improving detection performance and reducing communication overhead. To address these issues, we propose a novel privacy-preserving multilayer community detection framework based on federated learning which is called federated multilayer community detection (FMCD). First, we propose a novel aggregation strategy by utilizing the network average degree of local n…
Higher Order Local Search Assisted Community Detection in Signed Networks
Community detection in signed networks reveals mesoscale structures and sign characteristics, facilitating accurate and realistic understanding of real-life signed networks. Given the complexity of signed community structures and the NP-hard challenge of achieving optimal partitions, modularity optimization using memetic algorithms has emerged as a promising approach. However, the identified signed partitions still face challenges in modularity q…
EntroBot
Hypergraph Community Detection Based on Higher-Order Topology and Information Flow
Hypergraph community detection has gained significant attention due to reflecting higher-order mesoscale structures and facilitating functional analysis. However, previous methods mainly rely on a single and biased hypergraph property, i.e., static hyperedge structures or dynamic information flow, but largely overlook their intrinsic cooccurrence and potential conflicts in real-world situations. To address this issue, we first model hypergraph co…
Detecting Social Bots via Multi-Motif Attention Fusion Network
The rapid growth of social networks has enabled the widespread deployment of social bots that manipulate public opinion and disseminate misinformation, thereby posing significant cybersecurity risks. Most existing detection methods for social bots primarily focus on individual features and low-order neighbor information, while neglecting the higher-order topological semantics embedded in frequent substructures, or motifs. This oversight limits th…
Propagation Motifs as Codewords for Fake News Detection
MSTD
Online social media has become a primary channel for the dissemination of fake news, whose rapid diffusion can generate substantial cross-sectoral impacts. However, most structure-based fake news detection methods are essentially static and overlook temporal dynamics, thereby limiting performance and obscuring underlying mechanisms. To bridge this gap, we propose a framework based on temporal propagation motifs (TPMs) that jointly models structur…
Public awareness, knowledge and attitudes toward thalassemia and its screening in five high-prevalence countries
While public awareness of thalassemia is relatively high, knowledge levels remain poor, highlighting the need for targeted educational interventions and awareness campaigns to address knowledge gaps and improve attitudes toward thalassemia screening in diverse populations
Computer Science (10 obras) · Complex Network Analysis Techniques (9 obras) · Artificial Intelligence (6 obras) · Mathematics (6 obras) · Opinion Dynamics and Social Influence (6 obras) · Bioinformatics and Genomic Networks (4 obras) · Complex network (4 obras) · Data mining (4 obras) · Spam and Phishing Detection (4 obras) · Advanced Graph Neural Networks (3 obras)