Computational Social Networks最新文献_第3页

Structural hole centrality: evaluating social capital through strategic network formation 结构洞中心性:通过战略网络形成评价社会资本

Computational Social Networks Pub Date : 2020-09-17 DOI: 10.1186/s40649-020-00079-4

Faisal Ghaffar, Neil Hurley

{"title":"Structural hole centrality: evaluating social capital through strategic network formation","authors":"Faisal Ghaffar, Neil Hurley","doi":"10.1186/s40649-020-00079-4","DOIUrl":"https://doi.org/10.1186/s40649-020-00079-4","url":null,"abstract":"Strategic network formation is a branch of network science that takes an economic perspective to the creation of social networks, considering that actors in a network form links in order to maximise some utility that they attain through their connections to other actors in the network. In particular, Jackson’s Connections model, writes an actor’s utility as a sum over all other actors that can be reached along a path in the network of a benefit value that diminishes with the path length. In this paper, we are interested in the “social capital” that an actor retains due to their position in the network. Social capital can be understood as an ability to bond with actors, as well as an ability to form a bridge that connects otherwise disconnected actors. This bridging benefit has previously been modelled in another “structural hole” network formation game, proposed by Kleinberg. In this paper, we develop an approach that generalises the utility of Kleinberg’s game and combines it with that of the Connections model, to create a utility that models both the bonding and bridging capabilities of an actor with social capital. From this utility and its associated formation game, we derive a new centrality measure, which we dub “structural hole centrality”, to identify actors with high social capital. We analyse this measure by applying it to networks of different types, and assessing its correlation to other centrality metrics, using a benchmark dataset of 299 networks, drawn from different domains. Finally, using one social network from the dataset, we illustrate how an actor’s “structural hole centrality profile” can be used to identify their bridging and bonding value to the network.","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"43 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138513558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Solving the k-dominating set problem on very large-scale networks 求解非常大规模网络上的k支配集问题

Computational Social Networks Pub Date : 2020-07-20 DOI: 10.1186/s40649-020-00078-5

M. Nguyen, Minh Hoàng Hà, Diep N. Nguyen, The-Trung Tran

引用次数: 11

A hybrid metaheuristic for solving asymmetric distance-constrained vehicle routing problem 求解非对称距离约束车辆路径问题的混合元启发式算法

Computational Social Networks Pub Date : 2020-07-09 DOI: 10.21203/rs.3.rs-37923/v1

H. Ban, P. Nguyen

引用次数: 3

Understanding social media beyond text: a reliable practice on Twitter 理解超越文本的社交媒体:Twitter的可靠实践

Computational Social Networks Pub Date : 2020-04-30 DOI: 10.21203/rs.3.rs-24618/v1

Qixuan Hou, Meng Han, Feiyang Qu, J. He

{"title":"Understanding social media beyond text: a reliable practice on Twitter","authors":"Qixuan Hou, Meng Han, Feiyang Qu, J. He","doi":"10.21203/rs.3.rs-24618/v1","DOIUrl":"https://doi.org/10.21203/rs.3.rs-24618/v1","url":null,"abstract":"Social media provides high-volume and real-time data, which has been broadly used in diverse applications in sales, marketing, disaster management, health surveillance, etc. However, distinguishing between noises and reliable information can be challenging, since social media, a user-generated content system, has a great number of users who update massive information every second. The rich information is not only included in the short textual content but also embedded in the images and videos. In this paper, we introduce an effective and efficient framework for event detection with social media data. The framework integrates both textual and imagery content in the hope to fully utilize the information. The approach has been demonstrated to be more accurate than the text-only approach by removing 58 (66.7%) false-positive events. The precision of event detection is improved by 6.5%. Besides, based on our analysis, we also look into the content of these images to further explore the space of social media studies. Finally, the closely related text and image from social media offer us a valuable text-image mapping, which can enable knowledge transfer between two media types.","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"8 1","pages":"1-20"},"PeriodicalIF":0.0,"publicationDate":"2020-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42370924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Gumbel-softmax-based optimization: a simple general framework for optimization problems on graphs 基于gumbel -softmax的优化:图上优化问题的简单通用框架

Computational Social Networks Pub Date : 2020-04-14 DOI: 10.1186/s40649-021-00086-z

Yaoxin Li, Jing Liu, Guozheng Lin, Y. Hou, Muyun Mou, Jiang Zhang

引用次数: 4

Modelling community structure and temporal spreading on complex networks 复杂网络上群落结构和时间扩展的建模

Computational Social Networks Pub Date : 2020-03-09 DOI: 10.21203/rs.3.rs-16363/v1

Vesa Kuikka

{"title":"Modelling community structure and temporal spreading on complex networks","authors":"Vesa Kuikka","doi":"10.21203/rs.3.rs-16363/v1","DOIUrl":"https://doi.org/10.21203/rs.3.rs-16363/v1","url":null,"abstract":"We present methods for analysing hierarchical and overlapping community structure and spreading phenomena on complex networks. Different models can be developed for describing static connectivity or dynamical processes on a network topology. In this study, classical network connectivity and influence spreading models are used as examples for network models. Analysis of results is based on a probability matrix describing interactions between all pairs of nodes in the network. One popular research area has been detecting communities and their structure in complex networks. The community detection method of this study is based on optimising a quality function calculated from the probability matrix. The same method is proposed for detecting underlying groups of nodes that are building blocks of different sub-communities in the network structure. We present different quantitative measures for comparing and ranking solutions of the community detection algorithm. These measures describe properties of sub-communities: strength of a community, probability of formation and robustness of composition. The main contribution of this study is proposing a common methodology for analysing network structure and dynamics on complex networks. We illustrate the community detection methods with two small network topologies. In the case of network spreading models, time development of spreading in the network can be studied. Two different temporal spreading distributions demonstrate the methods with three real-world social networks of different sizes. The Poisson distribution describes a random response time and the e-mail forwarding distribution describes a process of receiving and forwarding messages.","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"8 1","pages":"1-38"},"PeriodicalIF":0.0,"publicationDate":"2020-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48328634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

A new model for calculating the maximum trust in Online Social Networks and solving by Artificial Bee Colony algorithm 基于人工蜂群算法的在线社交网络最大信任计算模型

Computational Social Networks Pub Date : 2020-02-13 DOI: 10.1186/s40649-020-00077-6

Shahram Saeidi

{"title":"A new model for calculating the maximum trust in Online Social Networks and solving by Artificial Bee Colony algorithm","authors":"Shahram Saeidi","doi":"10.1186/s40649-020-00077-6","DOIUrl":"https://doi.org/10.1186/s40649-020-00077-6","url":null,"abstract":"The social networks are widely used by millions of people worldwide. The trust concept is one of the most important issues in Social Network Analysis (SNA) which highly affects the quantity and quality of the inter-connections, decisions, and interactions among the users in e-commerce or recommendation systems. Many normative algorithms are developed to calculate the trust which most of them are complicated, depend on the network structure, and need lots of critical information that makes them hard to use. The aim of this paper is proposing a descriptive, simple and effective method for calculating the maximal trust and the trust route between any two users of an Online Social Network (OSN). For this purpose, four new models for estimating the trust mechanism of the users are proposed and analyzed using Kolmogorov–Smirnov and Anderson–Darling statistical hypothesis tests to identify and validate the best-fitted model based on 20,613 empirical results gathered from 4552 social network volunteers. Due to the time–complexity of the problem, a meta-heuristic algorithm based on the Artificial Bee Colony (ABC) optimization method is also developed for solving the best-fitted model. The proposed algorithm is simulated in Matlab® over six larger test cases adopted from the Facebook dataset. In order to evaluate the performance of the developed algorithm, the Ant Colony Optimization (ACO) and Genetic Algorithm (GA) based meta-heuristics are also simulated on the same test cases. The comparison of the computational results shows that the ABC approach performs better than the ACO and GA as the size of the network increases.","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"46 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138513550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

A model of opinion and propagation structure polarization in social media 社交媒体中观点与传播结构极化模型

Computational Social Networks Pub Date : 2020-01-09 DOI: 10.1186/s40649-019-0076-z

Hafizh A. Prasetya, Tsuyoshi Murata

{"title":"A model of opinion and propagation structure polarization in social media","authors":"Hafizh A. Prasetya, Tsuyoshi Murata","doi":"10.1186/s40649-019-0076-z","DOIUrl":"https://doi.org/10.1186/s40649-019-0076-z","url":null,"abstract":"The issue of polarization in online social media has been gaining attention in recent years amid the changing political landscapes of many parts of the world. Several studies empirically observed the existence of echo chambers in online social media, stimulating a slew of works that tries to model the phenomenon via opinion modeling. Here, we propose a model of opinion dynamics centered around the notion that opinion changes are invoked by news exposure. Our model comes with parameters for opinions and connection strength which are updated through news propagation. We simulate the propagation of multiple news under the model in synthetic networks and observe the evolution of the model’s parameters and the propagation structure induced. Unlike previous models, our model successfully exhibited not only polarization of opinion, but also segregated propagation structure. By analyzing the results of our simulations, we found that the formation probability of echo chambers is primarily connected to the news polarization. However, it is also affected by intolerance to dissimilar opinions and how quickly individuals update their opinions. Through simulations on Twitter networks, we found that the behavior of the model is reproducible across different network structure and sizes.","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"42 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138513562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 33

Network-based indices of individual and collective advising impacts in mathematics 数学中个人和集体建议影响的网络指标

Computational Social Networks Pub Date : 2020-01-06 DOI: 10.1186/s40649-019-0075-0

Alexander Semenov, Alexander Veremyev, Alexander Nikolaev, E. Pasiliao, V. Boginski

引用次数: 5

Full command of a network by a new node: some results and examples 新节点对网络的完全命令:一些结果和示例

Computational Social Networks Pub Date : 2019-12-26 DOI: 10.1186/s40649-019-0074-1

Clara Grácio, Sara Fernandes, Luís Mário Lopes

引用次数: 0