Betweenness centrality (BC) is a measure of the importance of a vertex in a graph, which is defined using the number of the shortest paths passing through the vertex. Brandes proposed an efficient algorithm for computing the BC scores of all vertices in a graph, which accumulates pair dependencies while traversing single-source shortest paths. Although this algorithm works well on static graphs, its direct application to dynamic graphs takes a huge amount of computation time because the BC scores must be computed from scratch every time the structure of graph changes. Therefore, various algorithms for updating the BC scores of all vertices have been developed so far. In this article, we propose a novel algorithm for updating the BC scores of all vertices in a graph upon deletion of a single edge. We also show the validity and efficiency of the proposed algorithm through theoretical analysis and experiments using various graphs obtained from synthetic and real networks.
{"title":"An algorithm for updating betweenness centrality scores of all vertices in a graph upon deletion of a single edge","authors":"Yoshiki Satotani;Tsuyoshi Migita;Norikazu Takahashi;Ernesto Estrada","doi":"10.1093/comnet/cnac033","DOIUrl":"https://doi.org/10.1093/comnet/cnac033","url":null,"abstract":"Betweenness centrality (BC) is a measure of the importance of a vertex in a graph, which is defined using the number of the shortest paths passing through the vertex. Brandes proposed an efficient algorithm for computing the BC scores of all vertices in a graph, which accumulates pair dependencies while traversing single-source shortest paths. Although this algorithm works well on static graphs, its direct application to dynamic graphs takes a huge amount of computation time because the BC scores must be computed from scratch every time the structure of graph changes. Therefore, various algorithms for updating the BC scores of all vertices have been developed so far. In this article, we propose a novel algorithm for updating the BC scores of all vertices in a graph upon deletion of a single edge. We also show the validity and efficiency of the proposed algorithm through theoretical analysis and experiments using various graphs obtained from synthetic and real networks.","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":"10 4","pages":"1-24"},"PeriodicalIF":2.1,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8016804/10070447/10070457.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49943396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Trust and reputation are a person's belief over another person and are essential factors while opinion values are shared among online social platforms. Both the values are calculated using past shared opinions and the structure of the network. Further, a credibility score is calculated using the trust and reputation of the nodes, which is helpful to share the opinion values more accurately. In this work, an opinion dynamics temporal network is modelled using the credibility score of the nodes in the network. The addition and deletion of the edges and the opinion evolution occur on the basis of the credibility score of the nodes. Results are analysed over scale-free networks generated using Bollabas et al. model. Such scale-free networks are evolved over time termed as temporal network using the proposed model. It is analysed how the different threshold values on the credibility score of the nodes affect the opinion values convergence on the proposed model.
{"title":"Trust- and reputation-based opinion dynamics modelling over temporal networks","authors":"Eeti Jain;Anurag Singh;Ernesto Estrada","doi":"10.1093/comnet/cnac019","DOIUrl":"https://doi.org/10.1093/comnet/cnac019","url":null,"abstract":"Trust and reputation are a person's belief over another person and are essential factors while opinion values are shared among online social platforms. Both the values are calculated using past shared opinions and the structure of the network. Further, a credibility score is calculated using the trust and reputation of the nodes, which is helpful to share the opinion values more accurately. In this work, an opinion dynamics temporal network is modelled using the credibility score of the nodes in the network. The addition and deletion of the edges and the opinion evolution occur on the basis of the credibility score of the nodes. Results are analysed over scale-free networks generated using Bollabas et al. model. Such scale-free networks are evolved over time termed as temporal network using the proposed model. It is analysed how the different threshold values on the credibility score of the nodes affect the opinion values convergence on the proposed model.","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":"10 4","pages":"cnab033-121"},"PeriodicalIF":2.1,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49943936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-01DOI: 10.1016/j.jco.2022.101680
Jia Chen
{"title":"EC-(t1, t2)-tractability of approximation in weighted Korobov spaces in the worst case setting","authors":"Jia Chen","doi":"10.1016/j.jco.2022.101680","DOIUrl":"https://doi.org/10.1016/j.jco.2022.101680","url":null,"abstract":"","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":"3 1","pages":"101680"},"PeriodicalIF":2.1,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78608191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-01DOI: 10.1016/j.jco.2022.101679
S. Dong, Wenchang Sun
{"title":"Learning rate of distribution regression with dependent samples","authors":"S. Dong, Wenchang Sun","doi":"10.1016/j.jco.2022.101679","DOIUrl":"https://doi.org/10.1016/j.jco.2022.101679","url":null,"abstract":"","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":"8 1","pages":"101679"},"PeriodicalIF":2.1,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81030457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-01DOI: 10.1016/j.jco.2022.101678
I. Argyros, S. George, Christoper Argyros
{"title":"On the complexity of convergence for high order iterative methods","authors":"I. Argyros, S. George, Christoper Argyros","doi":"10.1016/j.jco.2022.101678","DOIUrl":"https://doi.org/10.1016/j.jco.2022.101678","url":null,"abstract":"","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":"18 1","pages":"101678"},"PeriodicalIF":2.1,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82115245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-01DOI: 10.1016/j.jco.2022.101669
Eric Novak
{"title":"Announcement: IBC award 2022 and the nomination deadline 2023","authors":"Eric Novak","doi":"10.1016/j.jco.2022.101669","DOIUrl":"https://doi.org/10.1016/j.jco.2022.101669","url":null,"abstract":"","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":"69 1","pages":"101669"},"PeriodicalIF":2.1,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79558504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Math anxiety is a clinical pathology impairing cognitive processing in math-related contexts. Originally thought to affect only inexperienced, low-achieving students, recent investigations show how math anxiety is vastly diffused even among high-performing learners. This review of data-informed studies outlines math anxiety as a complex system that: (i) cripples well-being, self-confidence and information processing on both conscious and subconscious levels, (ii) can be transmitted by social interactions, like a pathogen, and worsened by distorted perceptions, (iii) affects roughly 20$%$ of students in 63 out of 64 worldwide educational systems but correlates weakly with academic performance and (iv) poses a concrete threat to students’ well-being, computational literacy and career prospects in science. These patterns underline the crucial need to go beyond performance for estimating math anxiety. Recent advances in network psychometrics and cognitive network science provide ideal frameworks for detecting, interpreting and intervening upon such clinical condition. Merging education research, psychology and data science, the approaches reviewed here reconstruct psychological constructs as complex systems, represented either as multivariate correlation models (e.g. graph exploratory analysis) or as cognitive networks of semantic/emotional associations (e.g. free association networks or forma mentis networks). Not only can these interconnected networks detect otherwise hidden levels of math anxiety but—more crucially—they can unveil the specific layout of interacting factors, for example, key sources and targets, behind math anxiety in a given cohort. As discussed here, these network approaches open concrete ways for unveiling students’ perceptions, emotions and mental well-being, and can enable future powerful data-informed interventions untangling math anxiety.
{"title":"A fast parameter estimator for large complex networks","authors":"Grover E. C. Guzman, D. Takahashi, André Fujita","doi":"10.1093/comnet/cnac022","DOIUrl":"https://doi.org/10.1093/comnet/cnac022","url":null,"abstract":"\u0000 Math anxiety is a clinical pathology impairing cognitive processing in math-related contexts. Originally thought to affect only inexperienced, low-achieving students, recent investigations show how math anxiety is vastly diffused even among high-performing learners. This review of data-informed studies outlines math anxiety as a complex system that: (i) cripples well-being, self-confidence and information processing on both conscious and subconscious levels, (ii) can be transmitted by social interactions, like a pathogen, and worsened by distorted perceptions, (iii) affects roughly 20$%$ of students in 63 out of 64 worldwide educational systems but correlates weakly with academic performance and (iv) poses a concrete threat to students’ well-being, computational literacy and career prospects in science. These patterns underline the crucial need to go beyond performance for estimating math anxiety. Recent advances in network psychometrics and cognitive network science provide ideal frameworks for detecting, interpreting and intervening upon such clinical condition. Merging education research, psychology and data science, the approaches reviewed here reconstruct psychological constructs as complex systems, represented either as multivariate correlation models (e.g. graph exploratory analysis) or as cognitive networks of semantic/emotional associations (e.g. free association networks or forma mentis networks). Not only can these interconnected networks detect otherwise hidden levels of math anxiety but—more crucially—they can unveil the specific layout of interacting factors, for example, key sources and targets, behind math anxiety in a given cohort. As discussed here, these network approaches open concrete ways for unveiling students’ perceptions, emotions and mental well-being, and can enable future powerful data-informed interventions untangling math anxiety.","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":"45 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84774369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We consider undirected graphs that grow through the successive combination of component sub-graphs. For any well-behaved functions defined for such graphs, taking values in a Banach space, we show that there must exist a scaling law applicable when successive copies of the same component graph are combined. Crucially, we extend the approach introduced in previous work to the successive combination of component random sub-graphs. We illustrate this by generalizing the preferential attachment operation for the combination of stochastic block models. We discuss a further wide range of random graph combination operators to which this theory now applies, indicating the ubiquity of growth scaling laws (and asymptotic decay scaling laws) within applications, where the modules are quite distinct, yet may be considered as instances drawn from the same random graph. This is a type of statistically self-similar growth process, as opposed to a deterministic growth process incorporating exact copies of the same motif, and it represents a natural, partially random, growth processes for graphs observed in the analysis of social and technology contexts.
{"title":"Scaling laws for properties of random graphs that grow via successive combination","authors":"P. Grindrod","doi":"10.1093/comnet/cnac024","DOIUrl":"https://doi.org/10.1093/comnet/cnac024","url":null,"abstract":"\u0000 We consider undirected graphs that grow through the successive combination of component sub-graphs. For any well-behaved functions defined for such graphs, taking values in a Banach space, we show that there must exist a scaling law applicable when successive copies of the same component graph are combined. Crucially, we extend the approach introduced in previous work to the successive combination of component random sub-graphs. We illustrate this by generalizing the preferential attachment operation for the combination of stochastic block models. We discuss a further wide range of random graph combination operators to which this theory now applies, indicating the ubiquity of growth scaling laws (and asymptotic decay scaling laws) within applications, where the modules are quite distinct, yet may be considered as instances drawn from the same random graph. This is a type of statistically self-similar growth process, as opposed to a deterministic growth process incorporating exact copies of the same motif, and it represents a natural, partially random, growth processes for graphs observed in the analysis of social and technology contexts.","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":"6 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74335522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-01DOI: 10.1016/j.jco.2022.101667
Michael Allouche, S. Girard, E. Gobet
{"title":"A generative model for fBm with deep ReLU neural networks","authors":"Michael Allouche, S. Girard, E. Gobet","doi":"10.1016/j.jco.2022.101667","DOIUrl":"https://doi.org/10.1016/j.jco.2022.101667","url":null,"abstract":"","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":"1 1","pages":"101667"},"PeriodicalIF":2.1,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90586697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-01DOI: 10.1016/j.jco.2022.101665
T. Kühn, M. Petersen
{"title":"Approximation in periodic Gevrey spaces","authors":"T. Kühn, M. Petersen","doi":"10.1016/j.jco.2022.101665","DOIUrl":"https://doi.org/10.1016/j.jco.2022.101665","url":null,"abstract":"","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":"7 1","pages":"101665"},"PeriodicalIF":2.1,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78198529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：