Understanding the relational and network dynamics among newcomer networks is important to devising appropriate strategies that will maximize the productivity of the incoming workforce. Nevertheless, there are limited ...Understanding the relational and network dynamics among newcomer networks is important to devising appropriate strategies that will maximize the productivity of the incoming workforce. Nevertheless, there are limited empirical contributions on newcomer networks with few studies examining newcomer networks in international environments. This study focuses on national homophily and examines whether ethnic identity salience, self-efficacy, individualism and ethnocentrism are associated with the occurrence of national homophily in newcomers networks. Using a multicultural student sample drawn from newly formed networks, the study found that ethnic identity salience and academic self-efficacy are associated with national homophily positively and negatively, respectively. Individualism is not found to be related to homophily while, contrary to our hypothesis, ethnocentrism is found to be negatively related to homophily. Through its examination of the effect of attitudinal variables on homophily, this study contributes to the broader literature on homophily and provides implications for managers and researchers.展开更多
With the penetration of the Internet, virtual groups have become more and more popular. The reliability and accuracy of interpersonal perception in the virtual environment is an intriguing issue. Using the Social rela...With the penetration of the Internet, virtual groups have become more and more popular. The reliability and accuracy of interpersonal perception in the virtual environment is an intriguing issue. Using the Social relations model (SRM) [1], this paper investigates interpersonal perception in virtual groups from a multilevel perspective. In particular, it examines the following three areas: homophily, identification, and individual attraction, and explores how much of these directional and dyadic relational evaluations can be attributed to the effect of the actor, the partner, and the relationship.展开更多
The proliferation of robot accounts on social media platforms has posed a significant negative impact,necessitating robust measures to counter network anomalies and safeguard content integrity.Social robot detection h...The proliferation of robot accounts on social media platforms has posed a significant negative impact,necessitating robust measures to counter network anomalies and safeguard content integrity.Social robot detection has emerged as a pivotal yet intricate task,aimed at mitigating the dissemination of misleading information.While graphbased approaches have attained remarkable performance in this realm,they grapple with a fundamental limitation:the homogeneity assumption in graph convolution allows social robots to stealthily evade detection by mingling with genuine human profiles.To unravel this challenge and thwart the camouflage tactics,this work proposed an innovative social robot detection framework based on enhanced HOmogeneity and Random Forest(HORFBot).At the core of HORFBot lies a homogeneous graph enhancement strategy,intricately woven with edge-removal techniques,tometiculously dissect the graph intomultiple revealing subgraphs.Subsequently,leveraging the power of contrastive learning,the proposed methodology meticulously trains multiple graph convolutional networks,each honed to discern nuances within these tailored subgraphs.The culminating stage involves the fusion of these feature-rich base classifiers,harmoniously aggregating their insights to produce a comprehensive detection outcome.Extensive experiments on three social robot detection datasets have shown that this method effectively improves the accuracy of social robot detection and outperforms comparative methods.展开更多
Graph Neural Networks(GNNs)play a significant role in tasks related to homophilic graphs.Traditional GNNs,based on the assumption of homophily,employ low-pass filters for neighboring nodes to achieve information aggre...Graph Neural Networks(GNNs)play a significant role in tasks related to homophilic graphs.Traditional GNNs,based on the assumption of homophily,employ low-pass filters for neighboring nodes to achieve information aggregation and embedding.However,in heterophilic graphs,nodes from different categories often establish connections,while nodes of the same category are located further apart in the graph topology.This characteristic poses challenges to traditional GNNs,leading to issues of“distant node modeling deficiency”and“failure of the homophily assumption”.In response,this paper introduces the Spatial-Frequency domain Adaptive Heterophilic Graph Neural Networks(SFA-HGNN),which integrates adaptive embedding mechanisms for both spatial and frequency domains to address the aforementioned issues.Specifically,for the first problem,we propose the“Distant Spatial Embedding Module”,aiming to select and aggregate distant nodes through high-order randomwalk transition probabilities to enhance modeling capabilities.For the second issue,we design the“Proximal Frequency Domain Embedding Module”,constructing adaptive filters to separate high and low-frequency signals of nodes,and introduce frequency-domain guided attention mechanisms to fuse the relevant information,thereby reducing the noise introduced by the failure of the homophily assumption.We deploy the SFA-HGNN on six publicly available heterophilic networks,achieving state-of-the-art results in four of them.Furthermore,we elaborate on the hyperparameter selection mechanism and validate the performance of each module through experimentation,demonstrating a positive correlation between“node structural similarity”,“node attribute vector similarity”,and“node homophily”in heterophilic networks.展开更多
This study empirically tested if the personality trait of optimism and the interpersonal capability to generate optimism in one’s network nodes (i.e., alter-optimism) influences the social relationship patterns. The ...This study empirically tested if the personality trait of optimism and the interpersonal capability to generate optimism in one’s network nodes (i.e., alter-optimism) influences the social relationship patterns. The results provide evidence that optimism trait is independent from the way social networks of personal-issue sharing, advice-seeking, problem-solving, and innovation, are structured. In contrary, the alter-optimism capability does provide a good explanation of one’s social network position. Implications of these findings are discussed at the end.展开更多
In the European Union,mass vaccination against COVID-19 staved off the strict restrictions that had characterized early epidemic response.Now,vaccination campaigns are focusing on booster doses,and primary vaccination...In the European Union,mass vaccination against COVID-19 staved off the strict restrictions that had characterized early epidemic response.Now,vaccination campaigns are focusing on booster doses,and primary vaccinations have all but halted.Still,52 million European adults are unvaccinated.We investigated if reaching the still unvaccinated population in future vaccination campaigns would substantially decrease the current burden of COVID-19,which is substantial.We focused on vaccination homophily,whereby those who are unvaccinated are mostly in contact with other unvaccinated,making COVID-19 circulation easier.We quantified vaccination homophily and estimated its impact on COVID-19 circulation.We used an online survey of 1,055,286 people from 22 European countries during early 2022.We computed vaccination homophily as the association between reported vaccination status and perceived vaccination uptake among one's own social contacts,using a case-referent design and a hierarchical logistic model.We used this information in an analysis of the COVID-19 reproduction ratio to determine the impact of vaccine homophily in transmission.Vaccination homophily was present and strong everywhere:the average odds ratio of being vaccinated for a 10-percentage-point increase in coverage among contacts was 1.66(95%CI=(1.60,1.72)).Homophily was positively associated with the strictness of COVID-19-related restrictions in 2020(Pearson=0.49,P=.03).In the countries studied,12%-to-18%of the reproduction ratio would be attributable to vaccine homophily.Reducing vaccination homophily may curb the reproduction ratio substantially even to the point of preventing recurrent epidemic waves.In addition to boosting those already vaccinated,increasing primary vaccination should remain a high priority in future vaccination campaigns,to reduce vaccination homophily:this combined strategy may decrease COVID-19 burden.展开更多
User-specified trust relations are often very sparse and dynamic, making them difficult to accurately predict from online social media. In addition, trust relations are usually unavailable for most social media platfo...User-specified trust relations are often very sparse and dynamic, making them difficult to accurately predict from online social media. In addition, trust relations are usually unavailable for most social media platforms.These issues pose a great challenge for predicting trust relations and further building trust networks. In this study,we investigate whether we can predict trust relations via a sparse learning model, and propose to build a trust network without trust relations using only pervasively available interaction data and homophily effect in an online world. In particular, we analyze the reliability of predicting trust relations by interaction behaviors, and provide a principled way to mathematically incorporate interaction behaviors and homophily effect in a novel framework,b Trust. Results of experiments on real-world datasets from Epinions and Ciao demonstrated the effectiveness of the proposed framework. Further experiments were conducted to understand the importance of interaction behaviors and homophily effect in building trust networks.展开更多
Trust, as a major part of human interactions, plays an important role in helping users collect reliable infor-mation and make decisions. However, in reality, user-specified trust relations are often very sparse and fo...Trust, as a major part of human interactions, plays an important role in helping users collect reliable infor-mation and make decisions. However, in reality, user-specified trust relations are often very sparse and follow a power law distribution; hence inferring unknown trust relations attracts increasing attention in recent years. Social theories are frameworks of empirical evidence used to study and interpret social phenomena from a sociological perspective, while social networks reflect the correlations of users in real world; hence, making the principle, rules, ideas and methods of social theories into the analysis of social networks brings new opportunities for trust prediction. In this paper, we investigate how to exploit homophily and social status in trust prediction by modeling social theories. We first give several methods to compute homophily coe？cient and status coe？cient, then provide a principled way to model trust prediction mathe-matically, and propose a novel framework, hsTrust, which incorporates homophily theory and status theory. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework. Further experiments are conducted to understand the importance of homophily theory and status theory in trust prediction.展开更多
文摘Understanding the relational and network dynamics among newcomer networks is important to devising appropriate strategies that will maximize the productivity of the incoming workforce. Nevertheless, there are limited empirical contributions on newcomer networks with few studies examining newcomer networks in international environments. This study focuses on national homophily and examines whether ethnic identity salience, self-efficacy, individualism and ethnocentrism are associated with the occurrence of national homophily in newcomers networks. Using a multicultural student sample drawn from newly formed networks, the study found that ethnic identity salience and academic self-efficacy are associated with national homophily positively and negatively, respectively. Individualism is not found to be related to homophily while, contrary to our hypothesis, ethnocentrism is found to be negatively related to homophily. Through its examination of the effect of attitudinal variables on homophily, this study contributes to the broader literature on homophily and provides implications for managers and researchers.
文摘With the penetration of the Internet, virtual groups have become more and more popular. The reliability and accuracy of interpersonal perception in the virtual environment is an intriguing issue. Using the Social relations model (SRM) [1], this paper investigates interpersonal perception in virtual groups from a multilevel perspective. In particular, it examines the following three areas: homophily, identification, and individual attraction, and explores how much of these directional and dyadic relational evaluations can be attributed to the effect of the actor, the partner, and the relationship.
基金Funds for the Central Universities(grant number CUC24SG018).
文摘The proliferation of robot accounts on social media platforms has posed a significant negative impact,necessitating robust measures to counter network anomalies and safeguard content integrity.Social robot detection has emerged as a pivotal yet intricate task,aimed at mitigating the dissemination of misleading information.While graphbased approaches have attained remarkable performance in this realm,they grapple with a fundamental limitation:the homogeneity assumption in graph convolution allows social robots to stealthily evade detection by mingling with genuine human profiles.To unravel this challenge and thwart the camouflage tactics,this work proposed an innovative social robot detection framework based on enhanced HOmogeneity and Random Forest(HORFBot).At the core of HORFBot lies a homogeneous graph enhancement strategy,intricately woven with edge-removal techniques,tometiculously dissect the graph intomultiple revealing subgraphs.Subsequently,leveraging the power of contrastive learning,the proposed methodology meticulously trains multiple graph convolutional networks,each honed to discern nuances within these tailored subgraphs.The culminating stage involves the fusion of these feature-rich base classifiers,harmoniously aggregating their insights to produce a comprehensive detection outcome.Extensive experiments on three social robot detection datasets have shown that this method effectively improves the accuracy of social robot detection and outperforms comparative methods.
基金supported by the Fundamental Research Funds for the Central Universities(Grant No.2022JKF02039).
文摘Graph Neural Networks(GNNs)play a significant role in tasks related to homophilic graphs.Traditional GNNs,based on the assumption of homophily,employ low-pass filters for neighboring nodes to achieve information aggregation and embedding.However,in heterophilic graphs,nodes from different categories often establish connections,while nodes of the same category are located further apart in the graph topology.This characteristic poses challenges to traditional GNNs,leading to issues of“distant node modeling deficiency”and“failure of the homophily assumption”.In response,this paper introduces the Spatial-Frequency domain Adaptive Heterophilic Graph Neural Networks(SFA-HGNN),which integrates adaptive embedding mechanisms for both spatial and frequency domains to address the aforementioned issues.Specifically,for the first problem,we propose the“Distant Spatial Embedding Module”,aiming to select and aggregate distant nodes through high-order randomwalk transition probabilities to enhance modeling capabilities.For the second issue,we design the“Proximal Frequency Domain Embedding Module”,constructing adaptive filters to separate high and low-frequency signals of nodes,and introduce frequency-domain guided attention mechanisms to fuse the relevant information,thereby reducing the noise introduced by the failure of the homophily assumption.We deploy the SFA-HGNN on six publicly available heterophilic networks,achieving state-of-the-art results in four of them.Furthermore,we elaborate on the hyperparameter selection mechanism and validate the performance of each module through experimentation,demonstrating a positive correlation between“node structural similarity”,“node attribute vector similarity”,and“node homophily”in heterophilic networks.
文摘This study empirically tested if the personality trait of optimism and the interpersonal capability to generate optimism in one’s network nodes (i.e., alter-optimism) influences the social relationship patterns. The results provide evidence that optimism trait is independent from the way social networks of personal-issue sharing, advice-seeking, problem-solving, and innovation, are structured. In contrary, the alter-optimism capability does provide a good explanation of one’s social network position. Implications of these findings are discussed at the end.
基金based on survey results from University of Maryland.Partial support from EU grant MOOD(H2020-874850)ANR and Fondation de France-NoCOV(00105995)ANRS EMERGEN(ANRS0151)and Sorbonne Universite ISCD.
文摘In the European Union,mass vaccination against COVID-19 staved off the strict restrictions that had characterized early epidemic response.Now,vaccination campaigns are focusing on booster doses,and primary vaccinations have all but halted.Still,52 million European adults are unvaccinated.We investigated if reaching the still unvaccinated population in future vaccination campaigns would substantially decrease the current burden of COVID-19,which is substantial.We focused on vaccination homophily,whereby those who are unvaccinated are mostly in contact with other unvaccinated,making COVID-19 circulation easier.We quantified vaccination homophily and estimated its impact on COVID-19 circulation.We used an online survey of 1,055,286 people from 22 European countries during early 2022.We computed vaccination homophily as the association between reported vaccination status and perceived vaccination uptake among one's own social contacts,using a case-referent design and a hierarchical logistic model.We used this information in an analysis of the COVID-19 reproduction ratio to determine the impact of vaccine homophily in transmission.Vaccination homophily was present and strong everywhere:the average odds ratio of being vaccinated for a 10-percentage-point increase in coverage among contacts was 1.66(95%CI=(1.60,1.72)).Homophily was positively associated with the strictness of COVID-19-related restrictions in 2020(Pearson=0.49,P=.03).In the countries studied,12%-to-18%of the reproduction ratio would be attributable to vaccine homophily.Reducing vaccination homophily may curb the reproduction ratio substantially even to the point of preventing recurrent epidemic waves.In addition to boosting those already vaccinated,increasing primary vaccination should remain a high priority in future vaccination campaigns,to reduce vaccination homophily:this combined strategy may decrease COVID-19 burden.
基金supported by the National Natural Science Foundation of China(Nos.61602057 and 11690012)the China Postdoctoral Science Foundation(No.2017M611301)+3 种基金the Science and Technology Department of Jilin Province,China(No.20170520059JH)the Education Department of Jilin Province,China(No.2016311)the Key Laboratory of Symbolic Computation and Knowledge Engineering(No.93K172016K13)the Guangxi Key Laboratory of Trusted Software(No.kx201533)
文摘User-specified trust relations are often very sparse and dynamic, making them difficult to accurately predict from online social media. In addition, trust relations are usually unavailable for most social media platforms.These issues pose a great challenge for predicting trust relations and further building trust networks. In this study,we investigate whether we can predict trust relations via a sparse learning model, and propose to build a trust network without trust relations using only pervasively available interaction data and homophily effect in an online world. In particular, we analyze the reliability of predicting trust relations by interaction behaviors, and provide a principled way to mathematically incorporate interaction behaviors and homophily effect in a novel framework,b Trust. Results of experiments on real-world datasets from Epinions and Ciao demonstrated the effectiveness of the proposed framework. Further experiments were conducted to understand the importance of interaction behaviors and homophily effect in building trust networks.
基金This work is supported by the National Natural Science Foundation of China under Grant No. 61300148, the Scientific and Technological Break-Through Program of Jilin Province of China under Grant No. 20130206051GX, and the Science and Technology Development Program of Jilin Province of China under Grant No. 20130522112JH.
文摘Trust, as a major part of human interactions, plays an important role in helping users collect reliable infor-mation and make decisions. However, in reality, user-specified trust relations are often very sparse and follow a power law distribution; hence inferring unknown trust relations attracts increasing attention in recent years. Social theories are frameworks of empirical evidence used to study and interpret social phenomena from a sociological perspective, while social networks reflect the correlations of users in real world; hence, making the principle, rules, ideas and methods of social theories into the analysis of social networks brings new opportunities for trust prediction. In this paper, we investigate how to exploit homophily and social status in trust prediction by modeling social theories. We first give several methods to compute homophily coe？cient and status coe？cient, then provide a principled way to model trust prediction mathe-matically, and propose a novel framework, hsTrust, which incorporates homophily theory and status theory. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework. Further experiments are conducted to understand the importance of homophily theory and status theory in trust prediction.
