2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)最新文献_第4页

Predicting Swedish elections with Twitter: A case for stochastic link structure analysis 用Twitter预测瑞典大选:一个随机链接结构分析的案例

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2808915

Nima Dokoohaki, Filippia Zikou, D. Gillblad, M. Matskin

{"title":"Predicting Swedish elections with Twitter: A case for stochastic link structure analysis","authors":"Nima Dokoohaki, Filippia Zikou, D. Gillblad, M. Matskin","doi":"10.1145/2808797.2808915","DOIUrl":"https://doi.org/10.1145/2808797.2808915","url":null,"abstract":"The question that whether Twitter data can be leveraged to forecast outcome of the elections has always been of great anticipation in the research community. Existing research focuses on leveraging content analysis for positivity or negativity analysis of the sentiments of opinions expressed. This is while, analysis of link structure features of social networks underlying the conversation involving politicians has been less looked. The intuition behind such study comes from the fact that density of conversations about parties along with their respective members, whether explicit or implicit, should reflect on their popularity. On the other hand, dynamism of interactions, can capture the inherent shift in popularity of accounts of politicians. Within this manuscript we present evidence of how a well-known link prediction algorithm, can reveal an authoritative structural link formation within which the popularity of the political accounts along with their neighbourhoods, shows strong correlation with the standing of electoral outcomes. As an evidence, the public time-lines of two electoral events from 2014 elections of Sweden on Twitter have been studied. By distinguishing between member and official party accounts, we report that even using a focus-crawled public dataset, structural link popularities bear strong statistical similarities with vote outcomes. In addition we report strong ranked dependence between standings of selected politicians and general election outcome, as well as for official party accounts and European election outcome.","PeriodicalId":371988,"journal":{"name":"2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128810712","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: 32

An approach from statistical mechanics for collaborative business social network reconstruction 基于统计力学的协同商业社会网络重构方法

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2809377

A. Corallo, C. Bisconti, Laura Fortunato, A. A. Gentile, Piergiuseppe Pellè

引用次数: 2

On mining lifestyles from user trip data 从用户出行数据中挖掘生活方式

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2808906

Meng-Fen Chiang, Ee-Peng Lim, Jia-Wei Low

引用次数: 2

Social influence computation and maximization in signed networks with competing cascades 具有竞争级联的签名网络中的社会影响计算与最大化

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2809304

Ajitesh Srivastava, C. Chelmis, V. Prasanna

{"title":"Social influence computation and maximization in signed networks with competing cascades","authors":"Ajitesh Srivastava, C. Chelmis, V. Prasanna","doi":"10.1145/2808797.2809304","DOIUrl":"https://doi.org/10.1145/2808797.2809304","url":null,"abstract":"Often in marketing, political campaigns and social media, two competing products or opinions propagate over a social network. Studying social influence in such competing cascades scenarios enables building effective strategies for maximizing the propagation of one process by targeting the most \"influential\" nodes in the network. The majority of prior work however, focuses on unsigned networks where individuals adopt the opinion of their neighbors with certain probability. In real life, relationships between individuals can be positive (e.g., friend-of relationship) or negative (e.g. connection between \"foes\"). According to social theory, people tend to have similar opinions to their friends but opposite of their foes. In this work, we study the problem of competing cascades on signed networks, which has been relatively unexplored. Particularly, we study the progressive propagation of two competing cascades in a signed network under the Independent Cascade Model, and provide an approximate analytical solution to compute the probability of infection of a node at any given time. We leverage our analytical solution to the problem of competing cascades in signed networks to develop a heuristic for the influence maximization problem. Unlike prior work, we allow the seed-set to be initialized with populations of both cascades with the end goal of maximizing the spread of one cascade. We validate our approach on several large-scale real-world and synthetic networks. Our experiments demonstrate that our influence maximization heuristic significantly outperforms state-of-the-art methods, particularly when the network is dominated by distrust relationships.","PeriodicalId":371988,"journal":{"name":"2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126649278","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: 23

Trend detection in social networks using Hawkes processes 使用Hawkes过程的社交网络趋势检测

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2814178

Julio Cesar Louzada Pinto, T. Chahed, E. Altman

引用次数: 29

Hackers topology matter geography: Mapping the dynamics of repeated system trespassing events networks 黑客拓扑物质地理:映射重复系统入侵事件网络的动态

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2808873

Amit Rechavi, Tamar Berenblum, David Maimon, Ido Sivan Sevilla

引用次数: 0

Email conversation network analysis: Work groups and teams in organizations 电子邮件会话网络分析:组织中的工作组和团队

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2808862

S. Zehnalova, Z. Horak, M. Kudelka

引用次数: 1

Is normalized mutual information a fair measure for comparing community detection methods? 标准化的互信息是比较社区检测方法的公平衡量标准吗?

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2809344

Alessia Amelio, C. Pizzuti

引用次数: 90

Bipartite network model for inferring hidden ties in crime data 犯罪数据中隐含联系的二部网络模型

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2808842

Haruna Isah, D. Neagu, P. Trundle

{"title":"Bipartite network model for inferring hidden ties in crime data","authors":"Haruna Isah, D. Neagu, P. Trundle","doi":"10.1145/2808797.2808842","DOIUrl":"https://doi.org/10.1145/2808797.2808842","url":null,"abstract":"Certain crimes are difficult to be committed by individuals but carefully organised by group of associates and affiliates loosely connected to each other with a single or small group of individuals coordinating the overall actions. A common starting point in understanding the structural organisation of criminal groups is to identify the criminals and their associates. Situations arise in many criminal datasets where there is no direct connection among the criminals. In this paper, we investigate ties and community structure in crime data in order to understand the operations of both traditional and cyber criminals, as well as to predict the existence of organised criminal networks. Our contributions are twofold: we propose a bipartite network model for inferring hidden ties between actors who initiated an illegal interaction and objects affected by the interaction, we then validate the method in two case studies on pharmaceutical crime and underground forum data using standard network algorithms for structural and community analysis. The vertex level metrics and community analysis results obtained indicate the significance of our work in understanding the operations and structure of organised criminal networks which were not immediately obvious in the data. Identifying these groups and mapping their relationship to one another is essential in making more effective disruption strategies in the future.","PeriodicalId":371988,"journal":{"name":"2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124978091","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: 20

Topological resilience analysis of supply networks under random disruptions and targeted attacks 随机中断和目标攻击下供应网络拓扑弹性分析

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2809325

Wenjun Wang, W. Street, Renato E. deMatta

{"title":"Topological resilience analysis of supply networks under random disruptions and targeted attacks","authors":"Wenjun Wang, W. Street, Renato E. deMatta","doi":"10.1145/2808797.2809325","DOIUrl":"https://doi.org/10.1145/2808797.2809325","url":null,"abstract":"Along with the rapid advancement of information technology, the traditional hierarchical supply chain has been quickly evolving into a variety of supply networks, which usually incorporate a large number of entities into complex graph topologies. The study of the resilience of supply networks is an important challenge. In this paper, we exploit the resilience embedded in the network topology by investigating in depth the multiple-path reachability of each demand node to other nodes, and propose a novel network resilience metric. We also develop new supply-network growth mechanisms that reflect the heterogeneous roles of different types of nodes in the supply network. We incorporate them into two fundamental network topologies (i.e. random-graph network and scale-free network), and evaluate their resilience against both random disruptions and targeted attacks using the new resilience metric. The experimental results verify the validity of our resilience metric and the effectiveness of our growth model. This research provides a generic framework and important insights into the construction and resilience analysis of complex supply networks.","PeriodicalId":371988,"journal":{"name":"2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127728301","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: 11