Information diffusion in online social networks is induced by the event of forwarding information for users, and latency exists widely in user spreading behaviors. Little work has been done to reveal the effect of lat...Information diffusion in online social networks is induced by the event of forwarding information for users, and latency exists widely in user spreading behaviors. Little work has been done to reveal the effect of latency on the diffusion process. In this paper, we propose a propagation model in which nodes may suspend their spreading actions for a waiting period of stochastic length. These latent nodes may recover their activity again. Meanwhile, the mechanism of forwarding information is also introduced into the diffusion model. Mean-field analysis and numerical simulations indicate that our model has three nontrivial results. First, the spreading threshold does not correlate with latency in neither homogeneous nor heterogeneous networks, but depends on the spreading and refractory parameter. Furthermore, latency affects the diffusion process and changes the infection scale. A large or small latency parameter leads to a larger final diffusion extent, but the intrinsic dynamics is different. Large latency implies forwarding information rapidly, while small latency prevents nodes from dropping out of interactions. In addition, the betweenness is a better descriptor to identify influential nodes in the model with latency, compared with the coreness and degree. These results are helpful in understanding some collective phenomena of the diffusion process and taking measures to restrain a rumor in social networks.展开更多
Based on the social network analysis methods and human network, this paper randomly selected 44 students (31 males and 13 females) as the research objects, and it used the UCINET software to analyze the friendship bet...Based on the social network analysis methods and human network, this paper randomly selected 44 students (31 males and 13 females) as the research objects, and it used the UCINET software to analyze the friendship between them of which 43 used WeChat and 44 used QQ, and it also used the tool Netdraw to visualize the network sociogram. By mining the four aspects of density, accessibility, centrality, block model, the results demonstrated that QQ social network and WeChat social network existed the phenomenon of small world, leaders and subgroups, and the key nodes of QQ human network were more than WeChat network. Through using the key nodes, it can push the precise and efficient information and improve the accuracy of information transmission and impact among network members.展开更多
Due to the presence of a large amount of personal sensitive information in social networks,privacy preservation issues in social networks have attracted the attention of many scholars.Inspired by the self-nonself disc...Due to the presence of a large amount of personal sensitive information in social networks,privacy preservation issues in social networks have attracted the attention of many scholars.Inspired by the self-nonself discrimination paradigmin the biological immune system,the negative representation of information indicates features such as simplicity and efficiency,which is very suitable for preserving social network privacy.Therefore,we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks,called AttNetNRI.Specifically,a negative survey-based method is developed to disturb the relationship between nodes in the social network so that the topology structure can be kept private.Moreover,a negative database-based method is proposed to hide node attributes,so that the privacy of node attributes can be preserved while supporting the similarity estimation between different node attributes,which is crucial to the analysis of social networks.To evaluate the performance of the AttNetNRI,empirical studies have been conducted on various attribute social networks and compared with several state-of-the-art methods tailored to preserve the privacy of social networks.The experimental results show the superiority of the developed method in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topology disturbing and attribute hiding parts.The experimental results show the superiority of the developed methods in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topological interference and attribute-hiding components.展开更多
Deep stochastic configuration networks(DSCNs)produce redundant hidden nodes and connections during training,which complicates their model structures.Aiming at the above problems,this paper proposes a double pruning st...Deep stochastic configuration networks(DSCNs)produce redundant hidden nodes and connections during training,which complicates their model structures.Aiming at the above problems,this paper proposes a double pruning structure design algorithm for DSCNs based on mutual information and relevance.During the training process,the mutual information algorithm is used to calculate and sort the importance scores of the nodes in each hidden layer in a layer-by-layer manner,the node pruning rate of each layer is set according to the depth of the DSCN at the current time,the nodes that contribute little to the model are deleted,and the network-related parameters are updated.When the model completes the configuration procedure,the correlation evaluation strategy is used to sort the global connection weights and delete insignificance connections;then,the network parameters are updated after pruning is completed.The experimental results show that the proposed structure design method can effectively compress the scale of a DSCN model and improve its modeling speed;the model accuracy loss is small,and fine-tuning for accuracy restoration is not needed.The obtained DSCN model has certain application value in the field of regression analysis.展开更多
A heterogeneous information network,which is composed of various types of nodes and edges,has a complex structure and rich information content,and is widely used in social networks,academic networks,e-commerce,and oth...A heterogeneous information network,which is composed of various types of nodes and edges,has a complex structure and rich information content,and is widely used in social networks,academic networks,e-commerce,and other fields.Link prediction,as a key task to reveal the unobserved relationships in the network,is of great significance in heterogeneous information networks.This paper reviews the application of presentation-based learning methods in link prediction of heterogeneous information networks.This paper introduces the basic concepts of heterogeneous information networks,and the theoretical basis of representation learning,and discusses the specific application of the deep learning model in node embedding learning and link prediction in detail.The effectiveness and superiority of these methods on multiple real data sets are demonstrated by experimental verification.展开更多
Identifying critical nodes is a pivotal research topic in network science,yet the efficient and accurate detection of highly influential nodes remains a challenge.Existing centrality measures predominantly rely on loc...Identifying critical nodes is a pivotal research topic in network science,yet the efficient and accurate detection of highly influential nodes remains a challenge.Existing centrality measures predominantly rely on local or global topological structures,often overlooking indirect connections and their interaction strengths.This leads to imprecise assessments of node importance,limiting practical applications.To address this,we propose a novel node centrality measure,termed six-degree gravity centrality(SDGC),grounded in the six degrees of separation theory,for the precise identification of influential nodes in networks.Specifically,we introduce a set of node influence parameters—node mass,dynamic interaction distance,and attraction coefficient—to enhance the gravity model.Node mass is calculated by integrating K-shell and closeness centrality measures.The dynamic interaction distance,informed by the six-degrees of separation theory,is determined through path searches within six hops between node pairs.The attraction coefficient is derived from the difference in K-shell values between nodes.By integrating these parameters,we develop an improved gravity model to quantify node influence.Experiments conducted on nine real-world networks demonstrate that SDGC significantly outperforms nine existing classical and state-of-the-art methods in identifying the influential nodes.展开更多
In view of the forwarding microblogging,secondhand smoke,happiness,and many other phenomena in real life,the spread characteristic of the secondary neighbor nodes in this kind of phenomenon and network scheduling is e...In view of the forwarding microblogging,secondhand smoke,happiness,and many other phenomena in real life,the spread characteristic of the secondary neighbor nodes in this kind of phenomenon and network scheduling is extracted,and sequence influence maximization problem based on the influence of neighbor nodes is proposed in this paper.That is,in the time sequential social network,the propagation characteristics of the second-level neighbor nodes are considered emphatically,and k nodes are found to maximize the information propagation.Firstly,the propagation probability between nodes is calculated by the improved degree estimation algorithm.Secondly,the weighted cascade model(WCM) based on static social network is not suitable for temporal social network.Therefore,an improved weighted cascade model(IWCM) is proposed,and a second-level neighbors time sequential maximizing influence algorithm(STIM) is put forward based on node degree.It combines the consideration of neighbor nodes and the problem of overlap of influence scope between nodes,and makes it chronological.Finally,the experiment verifies that STIM algorithm has stronger practicability,superiority in influence range and running time compared with similar algorithms,and is able to solve the problem of maximizing the timing influence based on the influence of neighbor nodes.展开更多
El Eulma has become an important node of transnational informal trade. It maintains strong relationships with international market places since the period where the ＂Trabendo＂ have seen a wide development in Algeria...El Eulma has become an important node of transnational informal trade. It maintains strong relationships with international market places since the period where the ＂Trabendo＂ have seen a wide development in Algeria. On the national and regional level, the city of El Eulma is the main element of the network of informal merchant areas known by ＂Douba＇l＂ markets. The most important areas are located in the central and eastern of Algeria. This dual membership was conferred to it because of the strong presence of import companies and the existence of discrete and dynamic networks well-organized, formed of false importers, This article focuses on commercial trajectories, the forms of networks and their developments and tries to know the logic. From these trajectories, how the merchant places could be formed in Algeria, as was the case for El Eulma.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.61401015 and 61271308)the Fundamental Research Funds for the Central Universities,China(Grant No.2014JBM018)the Talent Fund of Beijing Jiaotong University,China(Grant No.2015RC013)
文摘Information diffusion in online social networks is induced by the event of forwarding information for users, and latency exists widely in user spreading behaviors. Little work has been done to reveal the effect of latency on the diffusion process. In this paper, we propose a propagation model in which nodes may suspend their spreading actions for a waiting period of stochastic length. These latent nodes may recover their activity again. Meanwhile, the mechanism of forwarding information is also introduced into the diffusion model. Mean-field analysis and numerical simulations indicate that our model has three nontrivial results. First, the spreading threshold does not correlate with latency in neither homogeneous nor heterogeneous networks, but depends on the spreading and refractory parameter. Furthermore, latency affects the diffusion process and changes the infection scale. A large or small latency parameter leads to a larger final diffusion extent, but the intrinsic dynamics is different. Large latency implies forwarding information rapidly, while small latency prevents nodes from dropping out of interactions. In addition, the betweenness is a better descriptor to identify influential nodes in the model with latency, compared with the coreness and degree. These results are helpful in understanding some collective phenomena of the diffusion process and taking measures to restrain a rumor in social networks.
文摘Based on the social network analysis methods and human network, this paper randomly selected 44 students (31 males and 13 females) as the research objects, and it used the UCINET software to analyze the friendship between them of which 43 used WeChat and 44 used QQ, and it also used the tool Netdraw to visualize the network sociogram. By mining the four aspects of density, accessibility, centrality, block model, the results demonstrated that QQ social network and WeChat social network existed the phenomenon of small world, leaders and subgroups, and the key nodes of QQ human network were more than WeChat network. Through using the key nodes, it can push the precise and efficient information and improve the accuracy of information transmission and impact among network members.
基金supported by the National Natural Science Foundation of China(Nos.62006001,62372001)the Natural Science Foundation of Chongqing City(Grant No.CSTC2021JCYJ-MSXMX0002).
文摘Due to the presence of a large amount of personal sensitive information in social networks,privacy preservation issues in social networks have attracted the attention of many scholars.Inspired by the self-nonself discrimination paradigmin the biological immune system,the negative representation of information indicates features such as simplicity and efficiency,which is very suitable for preserving social network privacy.Therefore,we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks,called AttNetNRI.Specifically,a negative survey-based method is developed to disturb the relationship between nodes in the social network so that the topology structure can be kept private.Moreover,a negative database-based method is proposed to hide node attributes,so that the privacy of node attributes can be preserved while supporting the similarity estimation between different node attributes,which is crucial to the analysis of social networks.To evaluate the performance of the AttNetNRI,empirical studies have been conducted on various attribute social networks and compared with several state-of-the-art methods tailored to preserve the privacy of social networks.The experimental results show the superiority of the developed method in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topology disturbing and attribute hiding parts.The experimental results show the superiority of the developed methods in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topological interference and attribute-hiding components.
基金supported by the National Natural Science Foundation of China(62073006)the Beijing Natural Science Foundation of China(4212032)
文摘Deep stochastic configuration networks(DSCNs)produce redundant hidden nodes and connections during training,which complicates their model structures.Aiming at the above problems,this paper proposes a double pruning structure design algorithm for DSCNs based on mutual information and relevance.During the training process,the mutual information algorithm is used to calculate and sort the importance scores of the nodes in each hidden layer in a layer-by-layer manner,the node pruning rate of each layer is set according to the depth of the DSCN at the current time,the nodes that contribute little to the model are deleted,and the network-related parameters are updated.When the model completes the configuration procedure,the correlation evaluation strategy is used to sort the global connection weights and delete insignificance connections;then,the network parameters are updated after pruning is completed.The experimental results show that the proposed structure design method can effectively compress the scale of a DSCN model and improve its modeling speed;the model accuracy loss is small,and fine-tuning for accuracy restoration is not needed.The obtained DSCN model has certain application value in the field of regression analysis.
基金Science and Technology Research Project of Jiangxi Provincial Department of Education(Project No.GJJ211348,GJJ211347 and GJJ2201056)。
文摘A heterogeneous information network,which is composed of various types of nodes and edges,has a complex structure and rich information content,and is widely used in social networks,academic networks,e-commerce,and other fields.Link prediction,as a key task to reveal the unobserved relationships in the network,is of great significance in heterogeneous information networks.This paper reviews the application of presentation-based learning methods in link prediction of heterogeneous information networks.This paper introduces the basic concepts of heterogeneous information networks,and the theoretical basis of representation learning,and discusses the specific application of the deep learning model in node embedding learning and link prediction in detail.The effectiveness and superiority of these methods on multiple real data sets are demonstrated by experimental verification.
基金supported by the National Natural Science Foundation of China(Grant No.62173065)the Natural Science Foundation of Beijing(Grant No.4242040)+1 种基金the Intelligent Policing and National Security Risk Management Laboratory Open Topics for the year 2025(Grant No.ZHKFYB2503)the Intelligent Policing and National Security Risk Management Laboratory Open Topics for the year 2024(Grant No.ZHKFZD2401).
文摘Identifying critical nodes is a pivotal research topic in network science,yet the efficient and accurate detection of highly influential nodes remains a challenge.Existing centrality measures predominantly rely on local or global topological structures,often overlooking indirect connections and their interaction strengths.This leads to imprecise assessments of node importance,limiting practical applications.To address this,we propose a novel node centrality measure,termed six-degree gravity centrality(SDGC),grounded in the six degrees of separation theory,for the precise identification of influential nodes in networks.Specifically,we introduce a set of node influence parameters—node mass,dynamic interaction distance,and attraction coefficient—to enhance the gravity model.Node mass is calculated by integrating K-shell and closeness centrality measures.The dynamic interaction distance,informed by the six-degrees of separation theory,is determined through path searches within six hops between node pairs.The attraction coefficient is derived from the difference in K-shell values between nodes.By integrating these parameters,we develop an improved gravity model to quantify node influence.Experiments conducted on nine real-world networks demonstrate that SDGC significantly outperforms nine existing classical and state-of-the-art methods in identifying the influential nodes.
基金Supported by the National Natural Science Foundation of China(No.62172352,61871465,42002138)the Natural Science Foundation of Hebei Province(No.F2019203157)the Science and Technology Research Project of Hebei(No.ZD2019004)。
文摘In view of the forwarding microblogging,secondhand smoke,happiness,and many other phenomena in real life,the spread characteristic of the secondary neighbor nodes in this kind of phenomenon and network scheduling is extracted,and sequence influence maximization problem based on the influence of neighbor nodes is proposed in this paper.That is,in the time sequential social network,the propagation characteristics of the second-level neighbor nodes are considered emphatically,and k nodes are found to maximize the information propagation.Firstly,the propagation probability between nodes is calculated by the improved degree estimation algorithm.Secondly,the weighted cascade model(WCM) based on static social network is not suitable for temporal social network.Therefore,an improved weighted cascade model(IWCM) is proposed,and a second-level neighbors time sequential maximizing influence algorithm(STIM) is put forward based on node degree.It combines the consideration of neighbor nodes and the problem of overlap of influence scope between nodes,and makes it chronological.Finally,the experiment verifies that STIM algorithm has stronger practicability,superiority in influence range and running time compared with similar algorithms,and is able to solve the problem of maximizing the timing influence based on the influence of neighbor nodes.
文摘El Eulma has become an important node of transnational informal trade. It maintains strong relationships with international market places since the period where the ＂Trabendo＂ have seen a wide development in Algeria. On the national and regional level, the city of El Eulma is the main element of the network of informal merchant areas known by ＂Douba＇l＂ markets. The most important areas are located in the central and eastern of Algeria. This dual membership was conferred to it because of the strong presence of import companies and the existence of discrete and dynamic networks well-organized, formed of false importers, This article focuses on commercial trajectories, the forms of networks and their developments and tries to know the logic. From these trajectories, how the merchant places could be formed in Algeria, as was the case for El Eulma.
