2011 International Conference on Advances in Social Networks Analysis and Mining最新文献_第2页

A Novel Approach for Developing Automatic Knowledge Construction and Diagnostic System for Tag-Based Learning Environment 基于标签的学习环境下知识自动构建与诊断系统的新方法

2011 International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2011-07-25 DOI: 10.1109/ASONAM.2011.31

Jun-Ming Chen, Ying-Ying Chen, Yeali S. Sun, Meng Chang Chen

引用次数: 0

Evaluating Cooperation in Communities with the k-Core Structure 基于k-Core结构的社区合作评价

2011 International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2011-07-25 DOI: 10.1109/ASONAM.2011.65

C. Giatsidis, D. Thilikos, M. Vazirgiannis

引用次数: 119

Extracting Social Networks to Understand Interaction 提取社交网络来理解互动

2011 International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2011-07-25 DOI: 10.1109/ASONAM.2011.64

M. Forestier, Julien Velcin, D. Zighed

引用次数: 20

A Recommendation Method for Online Dating Networks Based on Social Relations and Demographic Information 基于社会关系和人口统计信息的在线约会网络推荐方法

2011 International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2011-07-25 DOI: 10.1109/ASONAM.2011.66

Lin Chen, R. Nayak, Yue Xu

引用次数: 18

Predicting Customer Demographics in a Mobile Social Network 在移动社交网络中预测客户人口统计

2011 International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2011-07-25 DOI: 10.1109/ASONAM.2011.13

S. Aarthi, S. Bharanidharan, M. Saravanan, V. Anand

引用次数: 10

Probabilistic Subgraph Matching on Huge Social Networks 大型社交网络的概率子图匹配

2011 International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2011-07-25 DOI: 10.1109/ASONAM.2011.78

Matthias Broecheler, Andrea Pugliese, V. S. Subrahmanian

引用次数: 15

Geo-Friends Recommendation in GPS-based Cyber-physical Social Network 基于gps的网络物理社交网络中的地理朋友推荐

2011 International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2011-07-25 DOI: 10.1109/ASONAM.2011.118

Xiao Yu, Ang Pan, L. Tang, Z. Li, Jiawei Han

{"title":"Geo-Friends Recommendation in GPS-based Cyber-physical Social Network","authors":"Xiao Yu, Ang Pan, L. Tang, Z. Li, Jiawei Han","doi":"10.1109/ASONAM.2011.118","DOIUrl":"https://doi.org/10.1109/ASONAM.2011.118","url":null,"abstract":"The popularization of GPS-enabled mobile devices provides social network researchers a taste of cyber-physical social network in advance. Traditional link prediction methods are designed to find friends solely relying on social network information. With location and trajectory data available, we can generate more accurate and geographically related results, and help web-based social service users find more friends in the real world. Aiming to recommend geographically related friends in social network, a three-step statistical recommendation approach is proposed for GPS-enabled cyber-physical social network. By combining GPS information and social network structures, we build a pattern-based heterogeneous information network. Links inside this network reflect both people's geographical information, and their social relationships. Our approach estimates link relevance and finds promising geo-friends by employing a random walk process on the heterogeneous information network. Empirical studies from both synthetic datasets and real-life dataset demonstrate the power of merging GPS data and social graph structure, and suggest our method outperforms other methods for friends recommendation in GPS-based cyber-physical social network.","PeriodicalId":416479,"journal":{"name":"2011 International Conference on Advances in Social Networks Analysis and Mining","volume":"190 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115782157","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: 120

Different Approaches to Groups and Key Person Identification in Blogosphere 部落格圈中团体与关键人物辨识的不同方法

2011 International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2011-07-25 DOI: 10.1109/ASONAM.2011.71

Anna Zygmunt, Piotr Bródka, Przemyslaw Kazienko, J. Kozlak

引用次数: 8

Towards Social Recommendation System Based on the Data from Microblogs 基于微博数据的社会推荐系统研究

2011 International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2011-07-25 DOI: 10.1109/ASONAM.2011.101

Pei-Shan Chang, I. Ting, Shyue-Liang Wang

引用次数: 12

SNAP: Towards a Validation of the Social Network Assembly Pipeline SNAP:迈向社会网络组装管道的验证

2011 International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2011-07-25 DOI: 10.1109/ASONAM.2011.88

Michael Farrugia, N. Hurley, A. Quigley

{"title":"SNAP: Towards a Validation of the Social Network Assembly Pipeline","authors":"Michael Farrugia, N. Hurley, A. Quigley","doi":"10.1109/ASONAM.2011.88","DOIUrl":"https://doi.org/10.1109/ASONAM.2011.88","url":null,"abstract":"A key problem for social network analysis is the lack of ground-truth data upon which to validate an analysis. Consider for example community-finding algorithms. The ``communities'' identified by such algorithms are typically justified on the basis of their structural properties, rather than on their ability to recover communities which can be independently verified. A ground truth of actual community data isn't always available and at best only partial ground-truth community information is. However, this problem isn't unique to community-finding algorithms. In previous publications, we introduced an automated Social Network Assembly Pipeline we refer to as SNAP. This is intended for the large scale actor identification, tie interference and strength measurement of social networks from non-relational data sets. In this paper we describe a validation study of SNAP through an intensive user-study of a portion of the individuals in the network. Individuals are asked to validate the network relationships uncovered by SNAP and where misclassified relationships are found, the individuals are interviewed in order to determine the underlying cause of the misclassification. The findings provide feedback on the rules through which relationships are inferred. For instance, it becomes clear that an error in actor identification can result in a propagation of this error though the network relations leading to follow-on relationship misclassifications. Also, we observe how outliers lead to a propagation of error in the inferred network. The results help us validate and invalidate different hypotheses we have about SNAP and suggests domain specific rule-sets for SNAP.","PeriodicalId":416479,"journal":{"name":"2011 International Conference on Advances in Social Networks Analysis and Mining","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124756923","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: 1