Journal of complex networks最新文献_第8页

Motif importance measurement based on multi-attribute decision 基于多属性决策的主题重要性度量

IF 2.1 4区数学

Journal of complex networks Pub Date : 2022-07-01 DOI: 10.1093/comnet/cnac023

Biao Feng;Yunyun Yang;Liao Zhang;Shuhong Xue;Xinlin Xie;Jiianrong Wang;Gang Xie

引用次数: 0

Reconstruction of cascading failures in dynamical models of power grids 电网动力学模型中级联故障的重构

IF 2.1 4区数学

Journal of complex networks Pub Date : 2022-07-01 DOI: 10.1093/comnet/cnac035

Alessandra Corso;Lucia Valentina Gambuzza;Federico Malizia;Giovanni Russo;Vito Latora;Mattia Frasca

引用次数: 1

Evaluating node embeddings of complex networks 评估复杂网络的节点嵌入

IF 2.1 4区数学

Journal of complex networks Pub Date : 2022-07-01 DOI: 10.1093/comnet/cnac030

Arash Dehghan-Kooshkghazi;Bogumił Kamiński;Łukasz Kraiński;Paweł Prałat;François Théberge;Ali Pinar

{"title":"Evaluating node embeddings of complex networks","authors":"Arash Dehghan-Kooshkghazi;Bogumił Kamiński;Łukasz Kraiński;Paweł Prałat;François Théberge;Ali Pinar","doi":"10.1093/comnet/cnac030","DOIUrl":"https://doi.org/10.1093/comnet/cnac030","url":null,"abstract":"Graph embedding is a transformation of nodes of a graph into a set of vectors. A good embedding should capture the graph topology, node-to-node relationship and other relevant information about the graph, its subgraphs and nodes. If these objectives are achieved, an embedding is a meaningful, understandable, compressed representations of a network that can be used for other machine learning tools such as node classification, community detection or link prediction. In this article, we do a series of extensive experiments with selected graph embedding algorithms, both on real-world networks as well as artificially generated ones. Based on those experiments, we formulate the following general conclusions. First, we confirm the main problem of node embeddings that is rather well-known to practitioners but less documented in the literature. There exist many algorithms available to choose from which use different techniques and have various parameters that may be tuned, the dimension being one of them. One needs to ensure that embeddings describe the properties of the underlying graphs well but, as our experiments confirm, it highly depends on properties of the network at hand and the given application in mind. As a result, selecting the best embedding is a challenging task and very often requires domain experts. Since investigating embeddings in a supervised manner is computationally expensive, there is a need for an unsupervised tool that is able to select a handful of promising embeddings for future (supervised) investigation. A general framework, introduced recently in the literature and easily available on GitHub repository, provides one of the very first tools for an unsupervised graph embedding comparison by assigning the ‘divergence score’ to embeddings with a goal of distinguishing good from bad ones. We show that the divergence score strongly correlates with the quality of embeddings by investigating three main applications of node embeddings: node classification, community detection and link prediction.","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49943946","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}

引用次数: 11

An algorithm for updating betweenness centrality scores of all vertices in a graph upon deletion of a single edge 一种在删除一条边时更新图中所有顶点间性中心性分数的算法

IF 2.1 4区数学

Journal of complex networks Pub Date : 2022-07-01 DOI: 10.1093/comnet/cnac033

Yoshiki Satotani;Tsuyoshi Migita;Norikazu Takahashi;Ernesto Estrada

引用次数: 1

Trust- and reputation-based opinion dynamics modelling over temporal networks 基于信任和声誉的意见动态建模的时间网络

IF 2.1 4区数学

Journal of complex networks Pub Date : 2022-07-01 DOI: 10.1093/comnet/cnac019

Eeti Jain;Anurag Singh;Ernesto Estrada

引用次数: 2

EC-(t1, t2)-tractability of approximation in weighted Korobov spaces in the worst case setting EC-(t1, t2)-最坏情况下加权Korobov空间逼近的可跟踪性

IF 2.1 4区数学

Journal of complex networks Pub Date : 2022-06-01 DOI: 10.1016/j.jco.2022.101680

Jia Chen

引用次数: 1

Learning rate of distribution regression with dependent samples 相关样本分布回归的学习率

IF 2.1 4区数学

Journal of complex networks Pub Date : 2022-05-01 DOI: 10.1016/j.jco.2022.101679

S. Dong, Wenchang Sun

引用次数: 2

On the complexity of convergence for high order iterative methods 高阶迭代方法的收敛复杂度

IF 2.1 4区数学

Journal of complex networks Pub Date : 2022-05-01 DOI: 10.1016/j.jco.2022.101678

I. Argyros, S. George, Christoper Argyros

引用次数: 1

Announcement: IBC award 2022 and the nomination deadline 2023 公告:IBC奖2022和提名截止日期2023

IF 2.1 4区数学

Journal of complex networks Pub Date : 2022-05-01 DOI: 10.1016/j.jco.2022.101669

Eric Novak

引用次数: 0

A fast parameter estimator for large complex networks 大型复杂网络的快速参数估计器

IF 2.1 4区数学

Journal of complex networks Pub Date : 2022-04-25 DOI: 10.1093/comnet/cnac022

Grover E. C. Guzman, D. Takahashi, André Fujita

{"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":null,"pages":null},"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}

引用次数: 3