2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)最新文献_第2页

The fragility of Twitter social networks against suspended users Twitter社交网络面对被暂停用户的脆弱性

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2809316

Wei Wei, K. Joseph, Huan Liu, Kathleen M. Carley

{"title":"The fragility of Twitter social networks against suspended users","authors":"Wei Wei, K. Joseph, Huan Liu, Kathleen M. Carley","doi":"10.1145/2808797.2809316","DOIUrl":"https://doi.org/10.1145/2808797.2809316","url":null,"abstract":"Social media is rapidly becoming one of the mediums of choice for understanding the cultural pulse of a region; e.g., for identifying what the population is concerned with and what kind of help is needed in a crisis. To assess this cultural pulse it is critical to have an accurate assessment of who is saying what in social media. However, social media is also the home of malicious users engaged in disruptive, disingenuous, and potentially illegal activity. A range of users, both human and non-human, carry out such social cyber-attacks. We ask, to what extent does the presence or absence of such users influence our ability to assess the cultural pulse of a region? We conduct a series of experiments to analyze the fragility of social network assessments based on Twitter data by comparing changes in both the structural and content results when suspended users are left in and taken out. Because a Twitter account can be suspended for various reasons including spamming or spreading ideas that can lead to extremism or terrorism, we separately assess the impacts of removing apparent spam bots and apparent extremists. Experimental results demonstrate that Twitter-based network structures and content are unstable, and can be highly impacted by the removal of suspended users. Further, the results exhibit regional and temporal variation that may be related to the political situation or civil unrest. We also provides guidance on the differential impact of different types of potentially suspend-able users.","PeriodicalId":371988,"journal":{"name":"2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"44 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":"123827797","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: 34

An evaluation of self-training styles for domain adaptation on the task of splice site prediction 剪接位置预测任务领域自适应的自我训练风格评价

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2808809

Nic Herndon, Doina Caragea

{"title":"An evaluation of self-training styles for domain adaptation on the task of splice site prediction","authors":"Nic Herndon, Doina Caragea","doi":"10.1145/2808797.2808809","DOIUrl":"https://doi.org/10.1145/2808797.2808809","url":null,"abstract":"We consider the problem of adding a large unlabeled sample from the target domain to boost the performance of a domain adaptation algorithm when only a small set of labeled examples are available from the target domain. In particular, we consider the problem setting motivated by the task of splice site prediction. For this task, annotating a genome using machine learning requires a lot of labeled data, whereas for non-model organisms, there is only some labeled data and lots of unlabeled data. With domain adaptation one can leverage the large amount of data from a related model organism, along with the labeled and unlabeled data from the organism of interest to train a classifier for the latter. Our goal is to analyze the three ways of incorporating the unlabeled data - with soft labels only (i.e., Expectation-Maximization), with hard labels only (i.e., self-training), or with both soft and hard labels - for the splice site prediction in particular, and more broadly for a general iterative domain adaptation setting. We provide empirical results on splice site prediction indicating that using soft labels only can lead to better classifier compared to the other two ways.","PeriodicalId":371988,"journal":{"name":"2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"35 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":"114352930","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: 2

GraphExploiter: Creation, visualization and algorithms on graphs graphexplorer:图形的创建、可视化和算法

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2808803

Victor Lequay, A. Ringot, Mohammed Haddad, Brice Effantin, H. Kheddouci

引用次数: 3

Prominent users detection during specific events by learning On- and Off-topic features of user activities 通过学习用户活动的On- and - Off-topic特征，在特定事件中突出用户检测

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2809411

Imen Bizid, Nibal Nayef, P. Boursier, Sami Faïz, Jacques Morcos

{"title":"Prominent users detection during specific events by learning On- and Off-topic features of user activities","authors":"Imen Bizid, Nibal Nayef, P. Boursier, Sami Faïz, Jacques Morcos","doi":"10.1145/2808797.2809411","DOIUrl":"https://doi.org/10.1145/2808797.2809411","url":null,"abstract":"Microblogs such as Twitter are characterized by the richness and recency of information shared by their users during major events. However, it is very challenging to automatically mine for information or for users sharing certain information due to the huge variety of unstructured stream of data shared in such microblogs. This work proposes a ranking and classification model for identifying users sharing useful information during a specified event. The model is based on a novel set of features that can be computed in real time. These features are designed such that they take into account both the on and off-topic activities of a user. Once users are characterized by a feature vector, supervised machine learning tool is trained to classify users as either prominent or not. Our model has been tested on data shared during a flooding disaster event and performed very well. The achieved results show the effectiveness of the proposed model for both the classification and ranking of prominent users in such events, and also the importance of the adjustment of the on-topic features by the off-topic ones when describing users' activities.","PeriodicalId":371988,"journal":{"name":"2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"42 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":"124001813","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: 18

Real-time monitoring of Twitter traffic by using semantic networks 使用语义网络实时监控Twitter流量

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2809371

F. Bisio, Claudia Meda, R. Zunino, Roberto Surlinelli, Eugenio Scillia, A. Ottaviano

引用次数: 6

An authentication model for IoT clouds 物联网云的身份验证模型

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2809361

Luciano Barreto, A. Celesti, M. Villari, M. Fazio, A. Puliafito

引用次数: 43

I/O efficient algorithms for exact distance queries on disk-resident dynamic graphs 对驻留磁盘的动态图进行精确距离查询的I/O高效算法

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2808876

Yishi Lin, Xiaowei Chen, John C.S. Lui

{"title":"I/O efficient algorithms for exact distance queries on disk-resident dynamic graphs","authors":"Yishi Lin, Xiaowei Chen, John C.S. Lui","doi":"10.1145/2808797.2808876","DOIUrl":"https://doi.org/10.1145/2808797.2808876","url":null,"abstract":"Point-to-point shortest distance queries are fundamental to large graph analytics. Motivated by the need for low-latency distance queries in large-scale \"dynamic\" graphs, we consider the problem of answering exact shortest distance queries on disk-resident scale-free dynamic graphs. Our query processing uses the canonical labeling method, which is a special 2-hop distance labeling for fast distance queries. In this paper, we propose two I/O efficient algorithms to update the canonical labeling. To the best of our knowledge, our proposed methods are the first practical disk-based methods to \"incrementally update\" the canonical labeling on dynamic graphs. We also show how to answer distance queries on the latest network based on outdated labels and new edges. Extensive experiments demonstrate the efficiency of our methods. Our update methods are an order of magnitude faster than reconstructing the canonical labeling. When the number of new edges is small, say less than 1% of the previous number of edges, our query algorithm based on outdated labels provides exact shortest distance and the query time is comparable to other query algorithms using latest labels.","PeriodicalId":371988,"journal":{"name":"2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"130 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":"132414301","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

Social circle discovery in ego-networks by mining the latent structure of user connections and profile attributes 挖掘用户连接和配置文件属性的潜在结构在自我网络中的社交圈发现

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2809303

Georgios Petkos, S. Papadopoulos, Y. Kompatsiaris

{"title":"Social circle discovery in ego-networks by mining the latent structure of user connections and profile attributes","authors":"Georgios Petkos, S. Papadopoulos, Y. Kompatsiaris","doi":"10.1145/2808797.2809303","DOIUrl":"https://doi.org/10.1145/2808797.2809303","url":null,"abstract":"Online Social Networks (OSN) allow their users to organize their friends into groups, also known as social circles. These social circles can be used to better manage who has access to users' posted content and also to control the content posted from other users that they view. Unfortunately, these social circles are generated manually and this can be a laborious process for users with more than a few friends. In this paper, we propose an approach for automatically generating social circles that takes into account both the profile information of the friends to be grouped and the social network connectivity between them, while it allows multiple membership of friends in social circles. The approach is based on an adaptation of the widely used Latent Dirichlet Allocation model and, despite the fact that it does not explicitly model social network connectivity, as other state of the art methods do, it manages to achieve results that are competitive and even better than those obtained from such methods, at a considerably lower computational cost.","PeriodicalId":371988,"journal":{"name":"2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"40 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":"131831841","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: 8

Analyzing event opinion transition through summarized emotion visualization 总结情绪可视化分析事件意见转变

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2808801

Fernando H. Calderon, Chun-Hao Chang, C. Argueta, Elvis Saravia, Yi-Shin Chen

引用次数: 5

A test-bed for generating social graphs and recommending named groups from email 用于生成社交图表和从电子邮件中推荐命名组的测试平台

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2808800

Andrew Ghobrial, Jacob W. Bartel, Andrew Vitkus, P. Dewan

引用次数: 0