2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)最新文献_第3页

CS-ComDet: A compressive sensing approach for inter-community detection in social networks CS-ComDet:一种用于社交网络中社区间检测的压缩感知方法

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2808856

H. Mahyar, H. Rabiee, A. Movaghar, E. Ghalebi, Ali Nazemian

引用次数: 14

Epitope mapping and antigenic evaluation of Helicobacter pylori Urease subunit beta fragment 幽门螺杆菌脲酶亚基片段表位定位及抗原评价

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2809365

E. Raoufi, H. Akrami, B. Khansarinejad, H. Abtahi

{"title":"Epitope mapping and antigenic evaluation of Helicobacter pylori Urease subunit beta fragment","authors":"E. Raoufi, H. Akrami, B. Khansarinejad, H. Abtahi","doi":"10.1145/2808797.2809365","DOIUrl":"https://doi.org/10.1145/2808797.2809365","url":null,"abstract":"Background: Helicobacter pylori (H.pylori) is one of the most common chronic infections worldwide. The H. pylori Urease is a high molecular mass (530 kDa) multimeric enzyme collected of two separate subunits, UreA (26.5 kDa) and UreB (61.7 kDa). Recently, many researchers are working in the expansion of a vaccine to prevent H. pylori infection, and of the various candidate antigens, the majority promising is the B subunit of the Urease protein (Urease B), for the reason that immunization of mice with purified Urease B has resulted in better immunogenicity and protection as contrast to the use of Urease A. The aim of this study was to high level expression and evaluation of antigenic property of recombinant UreB protein with infected human and mice sera as a vaccine candidate. Materials and Methods: In this experimental study, the highly antigenic region of UreB gene (609 base pair) was detected by best immunobioinformatics methods of epitope mapping, amplified by PCR method and was cloned into the cloning vector pBSK and then inserted into the expression vector pET-32a. The target protein was expressed and purified. Finally antigenicity was studied by western blotting using human sera infected with H. pylori UreB recombinant protein. Results: PCR and sequencing results showed the successful cloning of the target gene into the recombinant vector. The expression of protein was induced by IPTG and the expressed protein was purified with Ni-NTA kit and dialysis. The recombinant protein with molecular weight about 42 KDa was recognized by antibodies in western blotting. Conclusion: Evaluation of antibodies studies have shown that the predicted immunogenic fragment by best immunobioinformatics tools antigenic properties is high., so this Recombinant UreB protein is a good candidate for the design of H. pylori vaccine and diagnostic kits.","PeriodicalId":371988,"journal":{"name":"2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"39 9 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":"115899472","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

Uncovering news-Twitter reciprocity via interaction patterns 通过互动模式揭示新闻与推特的互惠关系

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2809329

Yue Ning, S. Muthiah, R. Tandon, Naren Ramakrishnan

引用次数: 11

The impact of students and TAs' participation on students' academic performance in MOOC MOOC中学生和助教的参与对学生学习成绩的影响

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2809428

Yunping Feng, Di Chen, Zihao Zhao, Hao-peng Chen, Puzhao Xi

引用次数: 19

Centrality for graphs with numerical attributes 具有数值属性的图的中心性

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2808844

Oualid Benyahia, C. Largeron

引用次数: 9

Implementation of chaotic analysis on retweet time series 转发时间序列混沌分析的实现

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2808881

Yuanyuan Bao, Chengqi Yi, Jingchi Jiang, Y. Xue, Yingfei Dong

引用次数: 1

Linear threshold model in temporal networks — Seed selection for social influence 时间网络中的线性阈值模型。社会影响的种子选择

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2809346

Radosław Michalski

引用次数: 3

Tweet sentiment: From classification to quantification 微博情绪:从分类到量化

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2809327

Wei Gao, F. Sebastiani

{"title":"Tweet sentiment: From classification to quantification","authors":"Wei Gao, F. Sebastiani","doi":"10.1145/2808797.2809327","DOIUrl":"https://doi.org/10.1145/2808797.2809327","url":null,"abstract":"Sentiment classification has become a ubiquitous enabling technology in the Twittersphere, since classifying tweets according to the sentiment they convey towards a given entity (be it a product, a person, a political party, or a policy) has many applications in political science, social science, market research, and many others. In this paper we contend that most previous studies dealing with tweet sentiment classification (TSC) use a suboptimal approach. The reason is that the final goal of most such studies is not estimating the class label (e.g., Positive, Negative, or Neutral) of individual tweets, but estimating the relative frequency (a.k.a. \"prevalence\") of the different classes in the dataset. The latter task is called quantification, and recent research has convincingly shown that it should be tackled as a task of its own, using learning algorithms and evaluation measures different from those used for classification. In this paper we show, on a multiplicity of TSC datasets, that using a quantification-specific algorithm produces substantially better class frequency estimates than a state-of-the-art classification-oriented algorithm routinely used in TSC. We thus argue that researchers interested in tweet sentiment prevalence should switch to quantification-specific (instead of classification-specific) learning algorithms and evaluation measures.","PeriodicalId":371988,"journal":{"name":"2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"29 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":"128897513","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: 81

A methodology for applying social network analysis metrics to biological interaction networks 一种将社会网络分析指标应用于生物相互作用网络的方法

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2808824

J. S. Silva, A. Saraiva

{"title":"A methodology for applying social network analysis metrics to biological interaction networks","authors":"J. S. Silva, A. Saraiva","doi":"10.1145/2808797.2808824","DOIUrl":"https://doi.org/10.1145/2808797.2808824","url":null,"abstract":"We propose a methodology for applying Social Network Analysis (SNA) metrics to biological Interaction Network studies in the Biodiversity Informatics domain, which may serve as a guide for this activity to other researchers. The methodology is structured into four steps: (i) mapping the data types and the interactions available; (ii) defining the key-questions to be answered and the analysis variables; (iii) choosing the SNA metrics appropriate to the context of the research; and (iv) performing the biological analysis with the support of SNA. Among the material resources used in the development of this research are: SNA metrics (network and species level) and the programs used for its calculation; Statistical Analysis approach (Exploratory Data Analysis and Multivariate Data Analysis) as a support tool; and Business Process Model and Notation (BPMN) to formalize the methodology. From this research, we found that a systematic method to guide the steps one research can facilitate the researchers' works and the interaction with experts from several fields of knowledge. In addition, we noted that there is the possibility of applying this methodology to underexplored knowledge fields.","PeriodicalId":371988,"journal":{"name":"2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"53 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":"130663668","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: 4

Social network analysis of TV drama characters via deep concept hierarchies 基于深层概念层次的电视剧角色社会网络分析

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2809306

Chang-Jun Nan, Kyung-Min Kim, Byoung-Tak Zhang

{"title":"Social network analysis of TV drama characters via deep concept hierarchies","authors":"Chang-Jun Nan, Kyung-Min Kim, Byoung-Tak Zhang","doi":"10.1145/2808797.2809306","DOIUrl":"https://doi.org/10.1145/2808797.2809306","url":null,"abstract":"TV drama is a kind of big data, containing enormous knowledge of modern human society. As the character-centered stories unfold, diverse knowledge, such as economics, politics and the culture, is displayed. However, unless we have efficient dynamic multi-modal data processing and picture processing methods, we cannot analyze drama data effectively. Here, we adopt the recently proposed deep concept hierarchies (DCH) and convolutional-recursive neural network (C-RNN) models to analyze the social network between the drama characters. DCH uses multi hierarchies structure to translate the vision-language concepts of drama characters into diversified abstract concepts, and utilizes Markov Chain Monte Carlo algorithm to improve the retrieval efficiency of organizing conceptual spaces. Adopting approximately 4400-minute data of TV drama - Friends, we process face recognition on the characters by using convolutional-recursive deep learning model. Then we establish the social network between the characters by deep concept hierarchies model and analyze their affinity and the change of social network while the stories unfold.","PeriodicalId":371988,"journal":{"name":"2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"43 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":"130737934","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: 22