2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)最新文献_第6页

Triggering patterns of topology changes in dynamic graphs 触发动态图中拓扑变化的模式

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921577

Mehdi Kaytoue-Uberall, Y. Pitarch, M. Plantevit, C. Robardet

引用次数: 15

Detecting deception in Online Social Networks 在线社交网络中的欺骗检测

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921614

Jalal S. Alowibdi, U. Buy, Philip S. Yu, Leon Stenneth

{"title":"Detecting deception in Online Social Networks","authors":"Jalal S. Alowibdi, U. Buy, Philip S. Yu, Leon Stenneth","doi":"10.1109/ASONAM.2014.6921614","DOIUrl":"https://doi.org/10.1109/ASONAM.2014.6921614","url":null,"abstract":"Over the past decade Online Social Networks (OSNs) have been helping hundreds of millions of people develop reliable computer-mediated relations. However, many user profiles in OSNs contain misleading, inconsistent or false information. Existing studies have shown that lying in OSNs is quite widespread, often for protecting a user's privacy. In order for OSNs to continue expanding their role as a communication medium in our society, it is crucial for information posted on OSNs to be trusted. Here we define a set of analysis methods for detecting deceptive information about user genders in Twitter. In addition, we report empirical results with our stratified data set consisting of 174,600 Twitter profiles with a 50-50 breakdown between male and female users. Our automated approach compares gender indicators obtained from different profile characteristics including first name, user name, and layout colors. We establish the overall accuracy of each indicator and the strength of all possible values for each indicator through extensive experimentations with our data set. We define male trending users and female trending users based on two factors, namely the overall accuracy of each characteristic and the relative strength of the value of each characteristic for a given user. We apply a Bayesian classifier to the weighted average of characteristics for each user. We flag for possible deception profiles that we classify as male or female in contrast with a self-declared gender that we obtain independently of Twitter profiles. Finally, we use manual inspections on a subset of profiles that we identify as potentially deceptive in order to verify the correctness of our predictions.","PeriodicalId":143584,"journal":{"name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130443060","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: 26

Agent-based simulation research on group emotion evolution of public emergency 基于agent的突发公共事件群体情绪演化仿真研究

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921632

Bo Li, Duoyong Sun, Zi-Han Lin, Chaomin Ou

{"title":"Agent-based simulation research on group emotion evolution of public emergency","authors":"Bo Li, Duoyong Sun, Zi-Han Lin, Chaomin Ou","doi":"10.1109/ASONAM.2014.6921632","DOIUrl":"https://doi.org/10.1109/ASONAM.2014.6921632","url":null,"abstract":"The use of agent based social simulation in computational analysis of public emergency management is becoming a prominent approach in social science research. Due to the complexity of rumor spreading and the radical emotion infecting processes, the results of the group emotion evolution analysis often lose effectiveness, which will affect the control and strategy establishment in emergency management. In this paper, we study the group emotion evolution process with the agent based simulation method. Along the way, the events-chain model is proposed to modeling the over-lapping influence of the temporal successive events. The simulation experiments of correlation events and events-chain model of a real public incident are performed based on the constructed framework. The results show that the group emotion evolution processes of correlation events have different patterns with the single event, and they cannot analyze as two independent events. Meanwhile, the results of real incident show that the events-chain model can simulate the real group response with consistency of the evolution.","PeriodicalId":143584,"journal":{"name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","volume":"247 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123379089","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

Detecting highly overlapping community structure based on Maximal Clique Networks 基于最大克利克网络检测高度重叠的群落结构

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921582

Peng Wu, Li Pan

引用次数: 6

Application of network analysis on healthcare 网络分析在医疗保健中的应用

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921647

Fei Wang, Uma Srinivasan, M. S. Uddin, S. Chawla

{"title":"Application of network analysis on healthcare","authors":"Fei Wang, Uma Srinivasan, M. S. Uddin, S. Chawla","doi":"10.1109/ASONAM.2014.6921647","DOIUrl":"https://doi.org/10.1109/ASONAM.2014.6921647","url":null,"abstract":"The healthcare sector holds large amounts of semantically rich electronic data generated and used by different sections of the health care community. Data analytic techniques such as data mining and predictive modelling are being used to gain new insights into health care costs, performance and quality of care. In this context, social network analysis (SNA) has the unique ability to play a new role in exploring the context and situations that lead to efficient and effective healthcare. In this paper we describe a specific context of private healthcare in Australia and describe our SNA based approach (applied to health insurance claims) to understand the nature of collaboration among doctors treating hospital inpatients and explore the impact of collaboration on cost and quality of care. In particular, we use network analysis to (a) design collaboration models among surgeons, anaesthetists and assistants who work together while treating patients admitted for specific types of treatments (b) identify and extract specific types of network topologies that indicate the way doctors collaborate while treating patients and (c) analyse the impact of these topologies on cost and quality of care provided to those patients.","PeriodicalId":143584,"journal":{"name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130900586","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

Influence in social networks: A unified model? 社交网络的影响力:一个统一的模型?

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921624

Ajitesh Srivastava, C. Chelmis, V. Prasanna

{"title":"Influence in social networks: A unified model?","authors":"Ajitesh Srivastava, C. Chelmis, V. Prasanna","doi":"10.1109/ASONAM.2014.6921624","DOIUrl":"https://doi.org/10.1109/ASONAM.2014.6921624","url":null,"abstract":"Understanding how information flows in online social networks is of great importance. It is generally difficult to obtain accurate prediction results of cascades over such networks, therefore a variety of diffusion models have been proposed in the literature to simulate diffusion processes instead. We argue that such models require extensive simulation results to produce good estimates of future spreads. In this work, we take a complimentary approach. We present a generalized, analytical model of influence in social networks that captures social influence at various levels of granularity, ranging from pairwise influence, to local neighborhood, to the general population, and external events, therefore capturing the complex dynamics of human behavior. We demonstrate that our model can integrate a variety of diffusion models. Particularly, we show that commonly used diffusion models in social networks can be reduced to special cases of our model, by carefully defining their parameters. Our goal is to provide a closed-form expression to approximate the probability of infection for every node in an arbitrary, directed network at any time t. We quantitatively evaluate the approximation quality of our analytical solution as compared to numerous popular diffusion models on a real-world dataset and a series of synthetic graphs.","PeriodicalId":143584,"journal":{"name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130834516","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: 17

Who were you talking to - Mining interpersonal relationships from cellphone network data 你在跟谁说话?从手机网络数据中挖掘人际关系

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921630

Mo Yu, Wenjun Si, Guojie Song, Z. Li, J. Yen

引用次数: 9

Overlapping Stochastic Community Finding 重叠随机群落发现

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921554

Aaron F. McDaid, N. Hurley, T. B. Murphy

引用次数: 2

Online Naive Bayes classification for network intrusion detection 基于在线朴素贝叶斯分类的网络入侵检测

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921657

Fatma Gumus, C. O. Sakar, Z. Erdem, Olcay Kursun

引用次数: 36

Time-aware reciprocity prediction in trust network 基于时间感知的信任网络互易预测

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921589

X. Feng, Jichang Zhao, Zhiwen Fang, Ke Xu

{"title":"Time-aware reciprocity prediction in trust network","authors":"X. Feng, Jichang Zhao, Zhiwen Fang, Ke Xu","doi":"10.1109/ASONAM.2014.6921589","DOIUrl":"https://doi.org/10.1109/ASONAM.2014.6921589","url":null,"abstract":"Study of reciprocity helps to find influential factors for users building relationships, which greatly facilitates the social behavior understanding in trust networks. In the previous literature, the dynamics of both network structure and user generated content are rarely considered. Our investigation of the available timing information from a real-world network demonstrates that time delay has significant impact on reciprocity formation. In particular, we find structural factors possess greater effect on short-term reciprocity while factors based on user generated content become more important for long-term reciprocity. Based on the empirical analysis, we redefine the reciprocity prediction problem as a learning task specific to each pair of users with different reciprocal delays. Evaluations show that our time-aware framework eventually outperforms the conventional classifiers that ignore the temporal information. Meanwhile, we tackle the problem of concept drift through fitting the evolving trend of features for Naive Bayes and performing periodic retraining for Logistic Regression classifiers, respectively.","PeriodicalId":143584,"journal":{"name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133831548","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