Purpose: This paper tries to understand the dynamics of scientific communication systems during crises by investigating as a case study the blogging activities that took place during the period of the 2011 earthquake ...Purpose: This paper tries to understand the dynamics of scientific communication systems during crises by investigating as a case study the blogging activities that took place during the period of the 2011 earthquake and related events in Japan. Interactions between bloggers and registered users are studied quantitatively and qualitatively at Sciencenet.cn, an influential science-related blogosphere in China.Design/methodology/approach: The editors of Sciencenet.cn compiled a special issue of science blog articles under the title Analysis of the Japanese Earthquake. We developed a spider program and downloaded from this special issue the metadata about title, content,publishing time, total read count, reply count and recommendation count, and further collected information about bloggers and recommenders. We then sent a short message to the bloggers who wrote articles on these emergencies, asking for their educational and professional background.Findings: We found that knowledge reflected in the blog articles is strongly related to the educational and professional background of bloggers. Knowledge diffusion is facilitated by interactions, such as recommendations, comments and answers. Interactions via comments and recommendations are of an assortative nature: A blog article is more likelyto be commented on and recommended by those bloggers who write on the same or similar topics than by those writing on a different one. Registered users tend to give comments on articles dealing with the topic that they recommend, and vice versa.Interaction in the intersection of two or three topics is more intense than that within one topic. The impact of blog articles is also influenced by other factors, such as the reputation of the blogger and the type of information they contain.Implications and limitations: It is confirmed that studying blogs is a valid approach within informetric studies. Yet, we only studied one triple(earthquake, tsunami, nuclear disaster) event based on data originating from one Chinese blog website. More展开更多
模糊的社团结构能有效提升网络传输性能。基于社团结构,利用节点之间的同异配程度和 k core 结构来定义链路重要性,提出了一种新的在社团内部删除链路,社团之间添加链路来减弱社团结构,提高网络容量的链路重连策略,即社团组合信息链路...模糊的社团结构能有效提升网络传输性能。基于社团结构,利用节点之间的同异配程度和 k core 结构来定义链路重要性,提出了一种新的在社团内部删除链路,社团之间添加链路来减弱社团结构,提高网络容量的链路重连策略,即社团组合信息链路重连策略(CCLS策略)。为了验证方法的有效性,我们分别在伪随机网络、具有社团结构的CWS小世界网络、无标度社团网络以及真实网络进行了仿真实验,仿真结果表明,CCLS策略能有效减弱网络社团特性,提高网络传输容量。展开更多
In real-world networks,there usually exist a small set of nodes that play an important role in the structure and function of networks.Those vital nodes can influence most of other nodes in the network via a spreading ...In real-world networks,there usually exist a small set of nodes that play an important role in the structure and function of networks.Those vital nodes can influence most of other nodes in the network via a spreading process.While most of the existing works focused on vital nodes that can maximize the spreading size in the final stage,which we call final influencers,recent work proposed the idea of fast influencers,which emphasizes nodes’spreading capacity at the early stage.Despite the recent surge of efforts in identifying these two types of influencers in networks,there remained limited research on untangling the differences between the fast influencers and final influencers.In this paper,we firstly distinguish the two types of influencers:fast-only influencers and final-only influencers.The former is defined as individuals who can achieve a high spreading effect at the early stage but lose their superiority in the final stage,and the latter are those individuals that fail to exhibit a prominent spreading performance at the early stage but influence a large fraction of nodes at the final stage.Further experiments are based on eight empirical datasets,and we reveal the key differences between the two types of influencers concerning their spreading capacity and the local structures.We also analyze how network degree assortativity influences the fraction of the proposed two types of influencers.The results demonstrate that with the increase of degree assortativity,the fraction of the fast-only influencers decreases,which indicates that more fast influencers tend to keep their superiority at the final stage.Our study provides insights into the differences and evolution of different types of influencers and has important implications for various empirical applications,such as advertisement marketing and epidemic suppressing.展开更多
For most networks, the weight of connection is changing with their attachment and inner affinity. By introducing a mixed mechanism of weighted-driven and inner selection, the model exhibits wide range power-law distri...For most networks, the weight of connection is changing with their attachment and inner affinity. By introducing a mixed mechanism of weighted-driven and inner selection, the model exhibits wide range power-law distributions of node strength and edge weight, and the exponent can be adjusted by not only the parameter δ but also the probability q. Furthermore, we investigate the weighted average shortest distance, clustering coefficient, and the correlation of our network. In addition, the weighted assortativity coefficient which characterizes important information of weighted topological networks has been discussed, but the variation of coefficients is much smaller than the former researches.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.:71173154)the National Social Science Foundation of China(Grant No.:08BZX076)the Social Science Foundation of Tongji University(Grant No.:3850219007)
文摘Purpose: This paper tries to understand the dynamics of scientific communication systems during crises by investigating as a case study the blogging activities that took place during the period of the 2011 earthquake and related events in Japan. Interactions between bloggers and registered users are studied quantitatively and qualitatively at Sciencenet.cn, an influential science-related blogosphere in China.Design/methodology/approach: The editors of Sciencenet.cn compiled a special issue of science blog articles under the title Analysis of the Japanese Earthquake. We developed a spider program and downloaded from this special issue the metadata about title, content,publishing time, total read count, reply count and recommendation count, and further collected information about bloggers and recommenders. We then sent a short message to the bloggers who wrote articles on these emergencies, asking for their educational and professional background.Findings: We found that knowledge reflected in the blog articles is strongly related to the educational and professional background of bloggers. Knowledge diffusion is facilitated by interactions, such as recommendations, comments and answers. Interactions via comments and recommendations are of an assortative nature: A blog article is more likelyto be commented on and recommended by those bloggers who write on the same or similar topics than by those writing on a different one. Registered users tend to give comments on articles dealing with the topic that they recommend, and vice versa.Interaction in the intersection of two or three topics is more intense than that within one topic. The impact of blog articles is also influenced by other factors, such as the reputation of the blogger and the type of information they contain.Implications and limitations: It is confirmed that studying blogs is a valid approach within informetric studies. Yet, we only studied one triple(earthquake, tsunami, nuclear disaster) event based on data originating from one Chinese blog website. More
文摘模糊的社团结构能有效提升网络传输性能。基于社团结构,利用节点之间的同异配程度和 k core 结构来定义链路重要性,提出了一种新的在社团内部删除链路,社团之间添加链路来减弱社团结构,提高网络容量的链路重连策略,即社团组合信息链路重连策略(CCLS策略)。为了验证方法的有效性,我们分别在伪随机网络、具有社团结构的CWS小世界网络、无标度社团网络以及真实网络进行了仿真实验,仿真结果表明,CCLS策略能有效减弱网络社团特性,提高网络传输容量。
基金supported by the National Natural Science Foundation of China(Grant Nos.61673150 and 11622538)Special Project for the Central Guidance on Local Science and Technology Development of Sichuan Province,China(Project No.2021ZYD0029)。
文摘In real-world networks,there usually exist a small set of nodes that play an important role in the structure and function of networks.Those vital nodes can influence most of other nodes in the network via a spreading process.While most of the existing works focused on vital nodes that can maximize the spreading size in the final stage,which we call final influencers,recent work proposed the idea of fast influencers,which emphasizes nodes’spreading capacity at the early stage.Despite the recent surge of efforts in identifying these two types of influencers in networks,there remained limited research on untangling the differences between the fast influencers and final influencers.In this paper,we firstly distinguish the two types of influencers:fast-only influencers and final-only influencers.The former is defined as individuals who can achieve a high spreading effect at the early stage but lose their superiority in the final stage,and the latter are those individuals that fail to exhibit a prominent spreading performance at the early stage but influence a large fraction of nodes at the final stage.Further experiments are based on eight empirical datasets,and we reveal the key differences between the two types of influencers concerning their spreading capacity and the local structures.We also analyze how network degree assortativity influences the fraction of the proposed two types of influencers.The results demonstrate that with the increase of degree assortativity,the fraction of the fast-only influencers decreases,which indicates that more fast influencers tend to keep their superiority at the final stage.Our study provides insights into the differences and evolution of different types of influencers and has important implications for various empirical applications,such as advertisement marketing and epidemic suppressing.
基金supported by the National Natural Science Foundation of China under Grant No.10675060
文摘For most networks, the weight of connection is changing with their attachment and inner affinity. By introducing a mixed mechanism of weighted-driven and inner selection, the model exhibits wide range power-law distributions of node strength and edge weight, and the exponent can be adjusted by not only the parameter δ but also the probability q. Furthermore, we investigate the weighted average shortest distance, clustering coefficient, and the correlation of our network. In addition, the weighted assortativity coefficient which characterizes important information of weighted topological networks has been discussed, but the variation of coefficients is much smaller than the former researches.
