@article {17673,
	title = {Mobile Data Offloading through Opportunistic Communications and Social Participation},
	journal = {Mobile Computing, IEEE Transactions on},
	volume = {11},
	year = {2012},
	month = {2012/05//},
	pages = {821 - 834},
	abstract = {3G networks are currently overloaded, due to the increasing popularity of various applications for smartphones. Offloading mobile data traffic through opportunistic communications is a promising solution to partially solve this problem, because there is almost no monetary cost for it. We propose to exploit opportunistic communications to facilitate information dissemination in the emerging Mobile Social Networks (MoSoNets) and thus reduce the amount of mobile data traffic. As a case study, we investigate the target-set selection problem for information delivery. In particular, we study how to select the target set with only k users, such that we can minimize the mobile data traffic over cellular networks. We propose three algorithms, called Greedy, Heuristic, and Random, for this problem and evaluate their performance through an extensive trace-driven simulation study. Our simulation results verify the efficiency of these algorithms for both synthetic and real-world mobility traces. For example, the Heuristic algorithm can offload mobile data traffic by up to 73.66 percent for a real-world mobility trace. Moreover, to investigate the feasibility of opportunistic communications for mobile phones, we implement a proof-of-concept prototype, called Opp-off, on Nokia N900 smartphones, which utilizes their Bluetooth interface for device/service discovery and content transfer.},
	keywords = {3G mobile communication, 3G networks, Bluetooth interface, cellular networks, cellular radio, content transfer, device/service discovery, graph theory, greedy algorithm, heuristic algorithm, information delivery, information dissemination, mobile data offloading, mobile data traffic, mobile phones, mobile social networks, MoSoNets, Nokia N900 smartphones, Opp-off, opportunistic communications, random algorithm, real-world mobility traces, smart phones, social networking (online), social participation, target-set selection problem, telecommunication network routing, Telecommunication traffic, trace-driven simulation study},
	isbn = {1536-1233},
	doi = {10.1109/TMC.2011.101},
	author = {Han,Bo and Hui,Pan and Kumar,V. S.A and Marathe,M. V and Shao,Jianhua and Srinivasan, Aravind}
}
@conference {17144,
	title = {EventGraphs: Charting Collections of Conference Connections},
	booktitle = {2011 44th Hawaii International Conference on System Sciences (HICSS)},
	year = {2011},
	month = {2011/01/04/7},
	pages = {1 - 10},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {EventGraphs are social media network diagrams of conversations related to events, such as conferences. Many conferences now communicate a common "hashtag" or keyword to identify messages related to the event. EventGraphs help make sense of the collections of connections that form when people follow, reply or mention one another and a keyword. This paper defines EventGraphs, characterizes different types, and shows how the social media network analysis add-in NodeXL supports their creation and analysis. The structural patterns to look for in EventGraphs are highlighted and design ideas for their improvement are discussed.},
	keywords = {conference connections, Conferences, Data visualization, EventGraphs, hashtag, measurement, Media, message identification, multimedia computing, NodeXL, Real time systems, social media network diagrams, social networking (online), Twitter},
	isbn = {978-1-4244-9618-1},
	doi = {10.1109/HICSS.2011.196},
	author = {Hansen,D. and Smith,M. A and Shneiderman, Ben}
}
@conference {17181,
	title = {Group-in-a-Box Layout for Multi-faceted Analysis of Communities},
	booktitle = {Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Confernece on Social Computing (SocialCom)},
	year = {2011},
	month = {2011/10/09/11},
	pages = {354 - 361},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Communities in social networks emerge from interactions among individuals and can be analyzed through a combination of clustering and graph layout algorithms. These approaches result in 2D or 3D visualizations of clustered graphs, with groups of vertices representing individuals that form a community. However, in many instances the vertices have attributes that divide individuals into distinct categories such as gender, profession, geographic location, and similar. It is often important to investigate what categories of individuals comprise each community and vice-versa, how the community structures associate the individuals from the same category. Currently, there are no effective methods for analyzing both the community structure and the category-based partitions of social graphs. We propose Group-In-a-Box (GIB), a meta-layout for clustered graphs that enables multi-faceted analysis of networks. It uses the tree map space filling technique to display each graph cluster or category group within its own box, sized according to the number of vertices therein. GIB optimizes visualization of the network sub-graphs, providing a semantic substrate for category-based and cluster-based partitions of social graphs. We illustrate the application of GIB to multi-faceted analysis of real social networks and discuss desirable properties of GIB using synthetic datasets.},
	keywords = {Algorithm design and analysis, category based social graph partitions, clustered graphs, clustering, Clustering algorithms, Communities, data visualisation, force-directed, gender, geographic location, graph layout algorithms, graph theory, group-in-a-box, group-in-a-box layout, Image edge detection, Layout, meta-layout, multifaceted community analysis, network subgraph visualization, network visualization, pattern clustering, profession, semantic substrates, Social network services, social networking (online), social networks, treemap space filling technique, Visualization},
	isbn = {978-1-4577-1931-8},
	doi = {10.1109/PASSAT/SocialCom.2011.139},
	author = {Rodrigues,E.M. and Milic-Frayling,N. and Smith,M. and Shneiderman, Ben and Hansen,D.}
}
@conference {19035,
	title = {Link prediction by de-anonymization: How We Won the Kaggle Social Network Challenge},
	year = {2011},
	month = {2011},
	pages = {1825 - 1834},
	abstract = {This paper describes the winning entry to the IJCNN 2011 Social Network Challenge run by Kaggle.com. The goal of the contest was to promote research on real-world link prediction, and the dataset was a graph obtained by crawling the popular Flickr social photo sharing website, with user identities scrubbed. By de-anonymizing much of the competition test set using our own Flickr crawl, we were able to effectively game the competition. Our attack represents a new application of de-anonymization to gaming machine learning contests, suggesting changes in how future competitions should be run. We introduce a new simulated annealing-based weighted graph matching algorithm for the seeding step of de-anonymization. We also show how to combine de-anonymization with link prediction-the latter is required to achieve good performance on the portion of the test set not de-anonymized-for example by training the predictor on the de-anonymized portion of the test set, and combining probabilistic predictions from de-anonymization and link prediction.},
	keywords = {deanonymization, Flickr social photo sharing Website, graph theory, IJCNN 2011 social network challenge, Kaggle social network challenge, learning (artificial intelligence), machine learning, realworld link prediction, Simulated annealing, simulated annealing-based weighted graph matching algorithm, social networking (online)},
	author = {Narayanan, A. and Elaine Shi and Rubinstein, B.I.P.}
}
@conference {17299,
	title = {Motivation for Participation in Online Neighborhood Watch Communities: An Empirical Study Involving Invitation Letters},
	booktitle = {Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Confernece on Social Computing (SocialCom)},
	year = {2011},
	month = {2011/10/09/11},
	pages = {760 - 765},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {This paper presents a three-part experiment designed to investigate the motivations of users of a community safety and neighborhood watch social networking website. The experiment centers around an intervention into the invitation system that current users employ to invite nonmembers to join the site, and involves several versions of an invitation email which differ by expressing one of four possible motivations for using such a site. The research presented investigates how potential users{\textquoteright} choice of whether or not to join the site is affected by the use case presented by the invitation. It also includes an investigation of the motivations of current users of the site, as reported in an online survey. The experiment yielded no significant difference in responses to the emails. Overall, invitations that included a specific motivation slightly outperformed those which did not, but not to a statistically significant degree. We conclude that although users have specific motivations for using the site, as reported in the survey, attempting to increase response rates to invitation emails by suggesting use cases of the site is surprisingly unlikely to be successful.},
	keywords = {Art, Communities, community safety, Electronic mail, Interviews, invitation email, invitations, motivation, neighborhood watch, Online communities, online neighborhood watch communities, online survey, participation, Safety, Security, social media, Social network services, social networking (online), social networking Website},
	isbn = {978-1-4577-1931-8},
	doi = {10.1109/PASSAT/SocialCom.2011.108},
	author = {Violi,N. and Shneiderman, Ben and Hanson,A. and Rey,P. J}
}
@conference {17306,
	title = {NetVisia: Heat Map \& Matrix Visualization of Dynamic Social Network Statistics \& Content},
	booktitle = {Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Confernece on Social Computing (SocialCom)},
	year = {2011},
	month = {2011/10/09/11},
	pages = {19 - 26},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Visualizations of static networks in the form of node-link diagrams have evolved rapidly, though researchers are still grappling with how best to show evolution of nodes over time in these diagrams. This paper introduces NetVisia, a social network visualization system designed to support users in exploring temporal evolution in networks by using heat maps to display node attribute changes over time. NetVisia{\textquoteright}s novel contributions to network visualizations are to (1) cluster nodes in the heat map by similar metric values instead of by topological similarity, and (2) align nodes in the heat map by events. We compare NetVisia to existing systems and describe a formative user evaluation of a NetVisia prototype with four participants that emphasized the need for tool tips and coordinated views. Despite the presence of some usability issues, in 30-40 minutes the user evaluation participants discovered new insights about the data set which had not been discovered using other systems. We discuss implemented improvements to NetVisia, and analyze a co-occurrence network of 228 business intelligence concepts and entities. This analysis confirms the utility of a clustered heat map to discover outlier nodes and time periods.},
	keywords = {business intelligence concept, business intelligence entity, competitive intelligence, data visualisation, dynamic networks, dynamic social network, heat map, Heating, Image color analysis, Information Visualization, Layout, matrix visualization, measurement, NetVisia system, network evolution, network visualization, node-link diagrams, outlier node, social network content, Social network services, social network statistics, social networking (online), social networks, static network visualization, time period, topological similarity, Training, usability, user evaluation, User interfaces},
	isbn = {978-1-4577-1931-8},
	doi = {10.1109/PASSAT/SocialCom.2011.216},
	author = {Gove,R. and Gramsky,N. and Kirby,R. and Sefer,E. and Sopan,A. and Dunne,C. and Shneiderman, Ben and Taieb-Maimon,M.}
}
@conference {17643,
	title = {Predicting Trust and Distrust in Social Networks},
	booktitle = {Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Confernece on Social Computing (SocialCom)},
	year = {2011},
	month = {2011/10/09/11},
	pages = {418 - 424},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {As user-generated content and interactions have overtaken the web as the default mode of use, questions of whom and what to trust have become increasingly important. Fortunately, online social networks and social media have made it easy for users to indicate whom they trust and whom they do not. However, this does not solve the problem since each user is only likely to know a tiny fraction of other users, we must have methods for inferring trust - and distrust - between users who do not know one another. In this paper, we present a new method for computing both trust and distrust (i.e., positive and negative trust). We do this by combining an inference algorithm that relies on a probabilistic interpretation of trust based on random graphs with a modified spring-embedding algorithm. Our algorithm correctly classifies hidden trust edges as positive or negative with high accuracy. These results are useful in a wide range of social web applications where trust is important to user behavior and satisfaction.},
	keywords = {distrust prediction, Electronic publishing, Encyclopedias, graph theory, inference algorithm, Inference algorithms, inference mechanisms, Internet, negative trust, online social networks, positive trust, Prediction algorithms, probability, random graphs, security of data, social media, social networking (online), spring-embedding algorithm, Training, trust inference, trust probabilistic interpretation, user behavior, user satisfaction, user-generated content, user-generated interactions},
	isbn = {978-1-4577-1931-8},
	doi = {10.1109/PASSAT/SocialCom.2011.56},
	author = {DuBois,T. and Golbeck,J. and Srinivasan, Aravind}
}
@conference {19042,
	title = {Privacy settings from contextual attributes: A case study using Google Buzz},
	year = {2011},
	month = {2011},
	pages = {257 - 262},
	abstract = {Social networks provide users with privacy settings to control what information is shared with connections and other users. In this paper, we analyze factors influencing changes in privacy-related settings in the Google Buzz social network. Specifically, we show statistics on contextual data related to privacy settings that are derived from crawled datasets and analyze the characteristics of users who changed their privacy settings. We also investigate potential neighboring effects among such users.},
	keywords = {contextual attributes, data privacy, Google Buzz, privacy settings, social networking (online), social networks, Statistics},
	author = {Mashima, D. and Elaine Shi and Chow, R. and Sarkar, P. and Li,C. and Song,D.}
}
@conference {17484,
	title = {Visual Analysis of Temporal Trends in Social Networks Using Edge Color Coding and Metric Timelines},
	booktitle = {Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Confernece on Social Computing (SocialCom)},
	year = {2011},
	month = {2011/10/09/11},
	pages = {549 - 554},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We present Net EvViz, a visualization tool for analysis and exploration of a dynamic social network. There are plenty of visual social network analysis tools but few provide features for visualization of dynamically changing networks featuring the addition or deletion of nodes or edges. Our tool extends the code base of the Node XL template for Microsoft Excel, a popular network visualization tool. The key features of this work are (1) The ability of the user to specify and edit temporal annotations to the network components in an Excel sheet, (2) See the dynamics of the network with multiple graph metrics plotted over the time span of the graph, called the Timeline, and (3) Temporal exploration of the network layout using an edge coloring scheme and a dynamic Time slider. The objectives of the new features presented in this paper are to let the data analysts, computer scientists and others to observe the dynamics or evolution in a network interactively. We presented Net EvViz to five users of Node XL and received positive responses.},
	keywords = {Color, computer scientists, data analysts, data visualisation, Data visualization, dynamic social network, dynamic timeslider, edge color coding, excel sheet, Image coding, image colour analysis, Layout, measurement, metric timelines, Microsoft excel, multiple graph metrics, Net EvViz, network components, network layout, network visualization tool, NodeXL template, social networking (online), temporal trends, Twitter, Visualization},
	isbn = {978-1-4577-1931-8},
	doi = {10.1109/PASSAT/SocialCom.2011.212},
	author = {Khurana,U. and Nguyen,Viet-An and Hsueh-Chien Cheng and Ahn,Jae-wook and Chen,Xi and Shneiderman, Ben}
}
@conference {17168,
	title = {First Steps to Netviz Nirvana: Evaluating Social Network Analysis with NodeXL},
	booktitle = {International Conference on Computational Science and Engineering, 2009. CSE {\textquoteright}09},
	volume = {4},
	year = {2009},
	month = {2009/08/29/31},
	pages = {332 - 339},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Social Network Analysis (SNA) has evolved as a popular, standard method for modeling meaningful, often hidden structural relationships in communities. Existing SNA tools often involve extensive pre-processing or intensive programming skills that can challenge practitioners and students alike. NodeXL, an open-source template for Microsoft Excel, integrates a library of common network metrics and graph layout algorithms within the familiar spreadsheet format, offering a potentially low-barrier-to-entry framework for teaching and learning SNA. We present the preliminary findings of 2 user studies of 21 graduate students who engaged in SNA using NodeXL. The majority of students, while information professionals, had little technical background or experience with SNA techniques. Six of the participants had more technical backgrounds and were chosen specifically for their experience with graph drawing and information visualization. Our primary objectives were (1) to evaluate NodeXL as an SNA tool for a broad base of users and (2) to explore methods for teaching SNA. Our complementary dual case-study format demonstrates the usability of NodeXL for a diverse set of users, and significantly, the power of a tightly integrated metrics/visualization tool to spark insight and facilitate sense-making for students of SNA.},
	keywords = {Computer science, computer science education, data visualisation, Data visualization, Educational institutions, graph drawing, graph layout algorithm, Information services, Information Visualization, Internet, Libraries, Microsoft Excel open-source template, MILC, multi-dimensional in-depth long-term case studies, Netviz Nirvana, NodeXL, Open source software, Programming profession, SNA, social network analysis, Social network services, social networking (online), spreadsheet programs, structural relationship, teaching, visual analytics, visualization tool, Web sites},
	isbn = {978-1-4244-5334-4},
	doi = {10.1109/CSE.2009.120},
	author = {Bonsignore,E. M and Dunne,C. and Rotman,D. and Smith,M. and Capone,T. and Hansen,D. L and Shneiderman, Ben}
}
@article {17238,
	title = {Integrating Statistics and Visualization for Exploratory Power: From Long-Term Case Studies to Design Guidelines},
	journal = {IEEE Computer Graphics and Applications},
	volume = {29},
	year = {2009},
	month = {2009/06//May},
	pages = {39 - 51},
	abstract = {Evaluating visual-analytics systems is challenging because laboratory-based controlled experiments might not effectively represent analytical tasks. One such system, Social Action, integrates statistics and visualization in an interactive exploratory tool for social network analysis. This article describes results from long-term case studies with domain experts and extends established design goals for information visualization.},
	keywords = {case studies, Control systems, Data analysis, data mining, data visualisation, Data visualization, data-mining, design guidelines, Employment, exploration, Filters, Guidelines, Information Visualization, insights, laboratory-based controlled experiments, Performance analysis, social network analysis, Social network services, social networking (online), social networks, SocialAction, statistical analysis, Statistics, visual analytics, visual-analytics systems, Visualization},
	isbn = {0272-1716},
	doi = {10.1109/MCG.2009.44},
	author = {Perer,A. and Shneiderman, Ben}
}
