ACM Transactions on

the Web (TWEB)

Latest Articles

Scalable and Efficient Web Search Result Diversification

It has been shown that top-k retrieval quality can be considerably improved by taking not only relevance but also diversity into account. However,... (more)

PeaCE-Ful Web Event Extraction and Processing as Bitemporal Mutable Events

The web is the largest bulletin board of the world. Events of all types, from flight arrivals to business meetings, are announced on this board.... (more)

A Large-Scale Evaluation of U.S. Financial Institutions’ Standardized Privacy Notices

Financial institutions in the United States are required by the Gramm-Leach-Bliley Act to provide annual privacy notices. In 2009, eight federal... (more)

A Comprehensive Survey and Classification of Approaches for Community Question Answering

Community question-answering (CQA) systems, such as Yahoo! Answers or Stack Overflow, belong to a prominent group of successful and popular Web... (more)

Prediction and Predictability for Search Query Acceleration

A commercial web search engine shards its index among many servers, and therefore the response time of a search query is dominated by the slowest... (more)


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About TWEB

The journal Transactions on the Web (TWEB) publishes refereed articles reporting the results of research on Web content, applications, use, and related enabling technologies.

The scope of TWEB is described on the Call for Papers page. Authors are invited to submit original research papers for consideration by following the directions on the Author Guidelines page.

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Forthcoming Articles
Web Content Classification Using Distributions of Subjective Quality Evaluations

Web content quality is evaluated today by human users or by machine-learning algorithms trained on human ratings. However, human ratings can be associated with a high uncertainty and subjective  influenced by demographic or psychological factors. We propose a new approach for the design of Web content quality classes from human ratings: the use of entire distributions to construct classes. By avoiding aggregation, our approach does not lose information and can deal with highly volatile or disagreeing ratings. The approach bases on the concept of Earth Mover Distance (EMD), a measure of distance for distributions. We evaluate the proposed approach on four datasets, obtained from diverse quality evaluation services or experiments. The classes designed using our approach closely match clusters discovered in the data using the EMD measure. We also consider the impact of the composition of small samples on the distributions that are the basis of our classification approach. We show that using distributions based on small samples of 10 evaluations is still robust to several demographic and psychological variables. This observation suggests that the proposed approach could be used in practice for Web content quality evaluation, even for highly uncertain and subjective ratings.

Effort Mediates Access to Information in Online Social Networks

Individuals' access to information in a social network depends on how it is distributed and where in the network individuals position themselves. In addition, individuals vary in how much effort they invest in managing their social connections. Using data from a social media site, we study how the interplay between effort and network position affects social media users' access to diverse and novel information. Previous studies of the role of networks in information access were limited in their ability to measure the diversity of information. We address this problem by learning the topics of interest to social media users from the messages they share online with followers. We use the learned topics to measure the diversity of information users receive from the people they follow online. We confirm that users in structurally diverse network positions, which bridge otherwise disconnected regions of the follower network, are exposed to more diverse information. We also show that users who invest more effort in their activity on the site not only place themselves in more structurally diverse positions within the network than the less engaged users, but they also receive more novel and diverse information when in similar network positions. These findings indicate that the relationship between network structure and access to information in networks is more nuanced than previously thought.

Localness of location-based knowledge sharing: A Study of Naver KiN "Here"

In location-based social Q&A, the questions related to a local community (e.g., local services and places) are typically answered by local residents (i.e., people who have the local knowledge). This study aims to deepen our understanding of location-based knowledge sharing through investigating general users behavioral characteristics, the topical and typological patterns related to the geographic characteristics, geographic locality of user activities, and motivations of local knowledge sharing. To this end, we analyzed a 12-month period Q&A dataset from Naver KiN Here and a supplementary survey dataset from 285 mobile users. Our results revealed several unique characteristics of location-based social Q&A. When compared with conventional social Q&A sites, Naver KiN Here had distinctive users behavior patterns and different topical/typological patterns. In addition, Naver KiN Here exhibited a strong spatial locality where the answers mostly had 1-3 spatial clusters of contributions, and a typical cluster spanned a few neighboring districts. We also uncovered unique motivators, e.g., ownership of local knowledge and a sense of local community. The findings reported in the paper have significant implications for the design of Q&A systems, especially location-based social Q&A systems.

Information Sharing by Viewers via Second Screens for In Real Life Events

The use of second screen devices with social media facilitates conversational interaction concerning broadcast media events, creating what we refer to as a social soundtrack. In this research, we evaluate the change of the Super Bowl XLIX social soundtrack across three social media platforms on the topical categories of: commercials, music, and game at three game phrases (Pre, During, and Post). We perform statistical analysis on more than 3M, 800K, and 50K posts from Twitter, Instagram and Tumblr, respectively. Findings show that the volume of posts in the During phase is fewer compared to Pre and Post phases; however, the hourly mean in the During phase is considerably higher than it is in the other two phases. We identify the predominant phase and category of interaction across all three social media sites. We also determine the significance of change in absolute scale across the Super Bowl categories (commercials, music, game) and in both absolute and relative scales across Super Bowl phases (Pre, During, Post) for the three social network platforms (Twitter, Tumblr, Instagram). Results show that significant phase-category relationships exist for all three social networks. The results identify the During phase as the predominant one for all three categories on all social media sites w.r.t the absolute volume of conversations in a continuous scale. From relative volume perspective, the During phase is highest for the music category for the majority of social networks, the game category in Tumblr and commercials on Twitter. For the commercial and game categories, however, the Post phase is higher than the During phase for Twitter and Instagram, respectively. Regarding predominant category identification, the game category is prominent for the majority of the social soundtracks for all phases, with the exception of Tumblr, which has dominant peaks for music and/or commercials relative to game in all three phases. These results are important in identifying the influence that second screen technology has on social media conversations from an information-sharing perspective across different social media platforms; this indicates that viewer roles are transitioning from passive to more active.

From Footprint to Evidence: An Exploratory Study of Mining Social Data for Credit Scoring

With the booming popularity of online social networks like Twitter and Sina Weibo, numerous online user footprints are accumulated on the social web. Recently, leveraging the rich social data to facilitate personal credit scoring has become a topic of growing interest in consumer financial industry. Compared with traditional financial transaction data, the heterogeneous social data presents both opportunities and challenges. In this article, we seek a deep understanding of how to learn users' credit from the online social data. Particularly, we explore the social-data-based credit scoring problem under the micro-blogging setting for its open, simple and real-time nature. To quantitatively measure the evidence hidden in social data, we choose to conduct an analytical and empirical study on Sina Weibo, the largest and most popular tweet-style site in China. Summarizing results from credit scoring literatures, we propose three social-data-based credit scoring principles as guidelines for our feature extraction process. In addition, we glean six related insights that arise from empirical observations. Based on the proposed principles and insights, we extract prediction features mainly from three parts of users' social data, including demographics, tweets, and network. Empirical studies on the extracted features show that online social media data does have good potentials in discriminating good credit users from the bad credit ones. To harness these broad range of features, we put forward a two-tier stacking and boosting enhanced ensemble learning framework. Furthermore, we perform experiments on a real-world Sina Weibo dataset of over 6 million tweets and 30,000 users whose credit labels are known through our third-party partner. Experimental results show that (I) Our approach achieves a roughly 0.625 AUC value with all proposed social features as input, and (II) Our learning algorithm can outperform traditional credit scoring methods by as much as 17\% for social-data-based personal credit scoring.

Manipulation among the arbiters of collective intelligence: How Wikipedia administrators mold public opinion

Our reliance on networked, collectively built information is a vulnerability when the quality or reliability of this information is poor. Wikipedia, one such collectively built information source, is often our first stop for information on all kinds of topics; its quality has stood up to many tests, and it prides itself on having a "Neutral Point of View". Enforcement of neutrality is in the hands of comparatively few, powerful administrators. We find a surprisingly large number of editors who change their behavior and begin focusing more on a particular controversial topic once they are promoted to administrator status. The conscious and unconscious biases of these few, but powerful, administrators may be shaping the information on many of the most sensitive topics on Wikipedia; some may even be explicitly infiltrating the ranks of administrators in order to promote their own points of view. Neither prior history nor vote counts during an administrator's election can identify those editors most likely to change their behavior in this suspicious manner. We find that an alternative measure, which gives more weight to influential voters, can successfully reject these suspicious candidates. This has important implications for how we harness collective intelligence: even if wisdom exists in a collective opinion (like a vote), that signal can be lost unless we carefully distinguish the true expert voter from the noisy or manipulative voter.

Scanpath Trend Analysis on Web Pages: Clustering Eye Tracking Scanpaths

Eye tracking studies have widely been used in improving the design and usability of web pages, and in the research of understanding how users interact with them. However, there is limited research in clustering users' eye movement sequences (i.e., scanpaths) on web pages to identify a general direction they follow. Existing research tends to be reductionist which means the resulting path is so short that is not useful. Moreover, there is little work on correlating users' scanpaths with visual elements of web pages and the underlying source code which means the result cannot be used for further processing. In order to address these limitations, we introduce a new concept in clustering scanpaths called "Scanpath Trend Analysis (STA)" which does not only consider the visual elements visited by all users, but also considers the visual elements visited by the majority in any order. We present an algorithm which automatically conducts this trend analysis to identify a trending scanpath of multiple web users in terms of visual elements of web pages. In contrast to existing research, the STA algorithm first analyses mostly visited visual elements in given scanpaths, then clusters the scanpaths by using these visual elements based on their overall positions in the individual scanpaths, and then constructs a trending scanpath in terms of these visual elements. This algorithm was experimentally evaluated by an eye tracking study with 3 groups of participants on 6 web pages for 2 different kinds of tasks, and therefore there were 36 (3x6x2) different combinations. Our experimental results show that the STA algorithm generates a trending scanpath that supports users tasks and addresses the reductionist problem of existing work by preventing the loss of commonly visited visual elements for all the combinations. Based on the statistical tests, the STA algorithm also generates a trending scanpath which is more similar to the inputted scanpaths compared to other existing work in 28 out of 36 combinations and this is not due to a random chance (p < 0:05). In the rest of the combinations, the STA algorithm still performs significantly better than some of other existing work. This algorithm contributes to behaviour analysis research on the web which can be used for different purposes, for example re-engineering web pages guided by the trending scanpath to improve users experience or guiding designers to improve their design.

Spam Mobile Apps: Characteristics, Detection, and in the Wild Analysis

Increased popularity of smartphones has attracted a large number of developers to offer various applications for the different smartphone platforms via the respective app markets. One consequence of this popularity is that the app markets are also becoming populated with spam apps. These spam apps reduce the users quality of experience and increase the workload of app market operators to identify these apps and remove them. Spam apps can come in many forms such as apps not having a specific functionality, those having unrelated app descriptions or unrelated keywords or similar apps being made available several times and across diverse categories. Market operators maintain anti-spam policies and apps are removed through continuous monitoring. Through a systematic crawl of a popular app market and by identifying apps that were removed over a period of time, we propose a method to detect spam apps solely using app metadata available at the time of publication. We first propose a methodology to manually label a sample of removed apps, according to a set of checkpoint heuristics that reveal the reasons behind removal. This analysis suggests that approximately 35% of the apps being removed are very likely to be spam apps. We then map the identified heuristics to several quantifiable features and show how distinguishing these features are for spam apps. We build an Adaptive Boost classifier for early identification of spam apps using only the metadata of the apps. Our classifier achieves an accuracy of over 95% with precision varying between 85% 95% and recall varying between 38%98%. We further show that a limited number of features, in the range of 10 to 30, generated from app metadata is sufficient to achieve a satisfactory level of performance. By applying the classifier on a set of apps present at the app market during our crawl, we estimate that at least 2.7% of them are potentially spam apps. By performing additional manual verification, we show that human reviewers agree with 82% of our classifier predictions.

MyAdChoices: Bringing Transparency and Control to Online Advertising

The intrusiveness and the increasing invasiveness of online advertising have, in the last few years, raised serious concerns regarding user privacy and Web usability. As a reaction to these concerns, we have witnessed the emergence of a myriad of ad-blocking and anti-tracking tools, whose aim is to return control to users over advertising. The problem with these technologies, however, is that they are extremely limited and radical in their approach: users can only choose either to block or allow all ads. With around 200 million people regularly using these tools, the economic model of the Web ---in which users get content free in return for allowing advertisers to show them ads--- is at serious peril. In this paper, we propose a smart Web technology that aims at bringing transparency to online advertising, so that users can make an informed and equitable decision regarding ad blocking. The proposed technology is implemented as a Web-browser extension and enables users to exert fine-grained control over advertising, thus providing them with certain guarantees in terms of privacy and browsing experience, while preserving the Internet economic model. Experimental results in a real environment demonstrate the suitability and feasibility of our approach, and provide preliminary findings on behavioral targeting from real user browsing profiles.

E-commerce Reputation Manipulation: The Emergence of Reputation-Escalation-as-a-Service

In online markets, a store's reputation is closely tied to its profitability. Sellers' desire to quickly achieve high reputation has fueled a profitable underground business, which operates as a specialized crowdsourcing marketplace and accumulates wealth by allowing online sellers to harness human laborers to conduct fake transactions for improving their stores' reputations. We term such an underground market a seller-reputation-escalation (SRE) market. In this article, we investigate the impact of the SRE service on reputation escalation by performing in-depth measurements of the prevalence of the SRE service, the business model and market size of SRE markets, and the characteristics of sellers and offered laborers. To this end, we have infiltrated five SRE markets and studied their operations using daily data collection over a continuous period of two months. We identified more than 11,000 online sellers posting at least 219,165 fake-purchase tasks on the five SRE markets. These transactions earned at least $46,438 in revenue for the five SRE markets, and the total value of merchandise involved exceeded $3,452,530. Our study demonstrates that online sellers using the SRE service can increase their stores' reputations at least 10 times faster than legitimate ones while about 25% of them were visibly penalized. Even worse, we found a newly launched service that can, within a single day, boost a seller's reputation by such a degree that would require a legitimate seller at least a year to accomplish. Armed with our analysis of the operational characteristics of the underground economy, we offer some insights into potential mitigation strategies. Finally, we revisit the SRE ecosystem one year later to evaluate the latest dynamism of the SRE markets especially the statuses of the online stores once identified to launch fake transaction campaigns on the SRE markets. We observe that the SRE markets are not as active as they were one year ago and about 17% of the involved online stores become inaccessible likely because they have been forcibly shut down by the corresponding E-commerce marketplace for conducting fake transactions.

Exploring and Analyzing the Tor Hidden Services Graph

The exploration and analysis of Web graphs has flourished in the recent past, producing a large number of relevant and interesting research results. However, the unique characteristics of the Tor network limit the applicability of standard techniques and demand for specific algorithms to explore and analyze it. The attention of the research community has focused on assessing the security of the Tor infrastructure (i.e., its ability to actually provide the intended level of anonymity) and on discussing what Tor is currently being used for. Since there are no foolproof techniques for discovering automatically Tor hidden services, little or no information is available about the topology of the Tor Web graph. Even less is known on the relationship between content similarity and topological structure. The present paper aims at addressing such lack of information. Among its contributions: a study on automatic Tor Web exploration/data collection approaches; the adoption of novel representative metrics for evaluating Tor data; a novel in-depth analysis of the hid- den services graph; a rich correlation analysis of hidden services semantics and topology. Finally, a broad interesting set of novel insights/considerations over the Tor Web organization and content are provided.

MultiWiki: Interlingual Text Passage Alignment in Wikipedia

In this article we present a novel problem of text passage alignment across interlingual article pairs in Wikipedia. This approach enables identification and interlinking of text passages written in different languages and containing overlapping information. Interlingual text passage alignment can enable Wikipedia editors and readers to better understand language-specific context of entities, provide valuable insights in cultural differences and build a basis for qualitative analysis of the articles. An important challenge in this context is the trade-off between the granularity of the extracted text passages and the precision of the alignment. Whereas short text passages can result in more precise alignment, longer text passages can facilitate a better overview of the differences in an article pair. To better understand these aspects from the user perspective, we conduct a user study at the example of the German and the English Wikipedia and collect a user-annotated benchmark. Then we propose MultiWiki - a method that adopts an integrated approach to the text passage alignment using semantic similarity measures and greedy algorithms and achieves precise results with respect to the user-defined alignment. MultiWiki is fully implemented and is currently available in four language pairs.

LDoW-PaN: Linked Data on the Web - Presentation and Navigation

This work aimed to propose the LDoW-PaN, which is a Linked Data presentation and navigation model focused on the average user. The LDoW-PaN model is an extension of the Dexter Hypertext Reference Model, which was presented by Halasz and Schwartz \citeyear{Halasz:1994}. Through the LDoW-PaN model, ordinary people - who have no experience with technologies that involve the Linked Data environment - can interact with the Web of Data (RDF) more closely to how they interact with the Web of Documents (HTML). To evaluate the proposal, some tools were developed, including the following: i) a Web Service, which implements the lower-level layers of the LDoW-PaN model; ii) a client-side script library, which implements the presentation and navigation layer; and iii) a browser extension, which uses these tools to provide Linked Data presentation and navigation for users browsing the Web. The browser extension was developed using user interface approaches that are well known, well accepted and evaluated by the Web research community, such as faceted navigation and presentation through tooltips. Therefore, the prototype evaluation included computational complexity measures and an analysis of the performance of the operations provided by the proposed model instead of examining and testing user interfaces.

Toward Automated Online Photo Privacy

Images are now one of the most common forms of content shared in online user-contributed sites and social Web 2.0 applications. In this paper, we present an extensive study exploring privacy and sharing needs of users' uploaded images. We develop learning models to estimate adequate privacy settings for newly uploaded images, based on carefully selected image-specific features. Our study investigates both visual and textual features of images for privacy classification. We consider both basic image-specific features, commonly used for image processing, as well as more sophisticated and abstract visual features. Additionally, we include a visual representation of the sentiment evoked by images. To our knowledge, sentiment has never been used in the context of image classification for privacy purposes. We identify the smallest set of features, that by themselves or combined together with others, can perform well in properly predicting the degree of sensitivity of users' images. We consider both the case of binary privacy settings (i.e. public, private), as well as the case of more complex privacy options, characterized by multiple sharing options. Our results show that with few carefully selected features, one may achieve high accuracy, especially when high-quality tags are available.


Publication Years 2007-2016
Publication Count 194
Citation Count 1992
Available for Download 194
Downloads (6 weeks) 2188
Downloads (12 Months) 17634
Downloads (cumulative) 167265
Average downloads per article 862
Average citations per article 10
First Name Last Name Award
Maria Bielikova ACM Senior Member (2009)
Dan Boneh ACM Prize in Computing (2014)
Athman Bouguettaya ACM Distinguished Member (2012)
ACM Senior Member (2007)
Andrei Broder ACM Paris Kanellakis Theory and Practice Award (2012)
Carlos A. Castillo ACM Senior Member (2014)
Lorrie Faith Cranor ACM Senior Member (2006)
Ernesto Damiani ACM Distinguished Member (2008)
Schahram Dustdar ACM Distinguished Member (2009)
Djoerd Hiemstra ACM Senior Member (2009)
Eric Horvitz ACM AAAI Allen Newell Award (2015)
Craig Knoblock ACM Distinguished Member (2008)
Yiqun Liu ACM Senior Member (2016)
Dmitri Loguinov ACM Distinguished Member (2014)
ACM Senior Member (2007)
Filippo Menczer ACM Distinguished Member (2013)
Mourad Ouzzani ACM Senior Member (2009)
Jian Pei ACM Senior Member (2007)
Naren Ramakrishnan ACM Distinguished Member (2009)
John T Riedl ACM Software System Award (2010)
ACM Distinguished Member (2007)
Prashant J Shenoy ACM Distinguished Member (2009)
ACM Senior Member (2006)
Xing Xie ACM Senior Member (2010)
Qiang Yang ACM Distinguished Member (2011)
Ben Yanbin Zhao ACM Distinguished Member (2015)
Yu Zheng ACM Senior Member (2011)

First Name Last Name Paper Counts
Ismail Altingövde 6
Ryen White 5
Berkant Cambazoglu 4
Wolfgang Nejdl 4
Xing Xie 4
Ingmar Weber 4
Weiying Ma 4
Weiyi Meng 3
Ricardo Baeza-Yates 3
Rifat Ozcan 3
Fabio Casati 3
Ling Liu 3
Özgür Ulusoy 3
Alessandro Bozzon 2
Eric Horvitz 2
Clyde Giles 2
Anirban Mahanti 2
Piero Fraternali 2
Barry Smyth 2
Markus Strohmaier 2
Monika Henzinger 2
Qiong Luo 2
Xiangye Xiao 2
Philip Yu 2
Andrei Broder 2
Phillipa Gill 2
Freddy Lécué 2
Yu Zheng 2
Marco Brambilla 2
Carey Williamson 2
Niklas Carlsson 2
Prashant Shenoy 2
James Miller 2
Eepeng Lim 2
Boualem Benatallah 2
Cristóbal Arellano 2
Clement Yu 2
Ziv Bar-Yossef 2
Stefan Siersdorfer 2
Eda Baykan 2
Óscar Díaz 2
Mudhakar Srivatsa 2
Sergiu Chelaru 2
Florian Daniel 2
Christo Wilson 2
Luca Becchetti 1
Eelco Herder 1
Hari Sundaram 1
Islam Elgedawy 1
Zahir Tari 1
Bhuvan Urgaonkar 1
Jure Leskovec 1
Aristides Gionis 1
Vassilis Plachouras 1
Wolf Siberski 1
Tim Furche 1
Pedro Leon 1
Blase Ur 1
Nishida Toyoaki 1
Jianke Zhu 1
Maciej Drozdowski 1
Rik Eshuis 1
Andreas Hotho 1
Michael Paul 1
Guibing Guo 1
Jie Zhang 1
Wensheng Wu 1
Valeria Fionda 1
Haibin Zhang 1
Liran Katzir 1
Kenneth Fletcher 1
Willianmassami Watanabe 1
Stefano Paraboschi 1
Harrick Vin 1
Sangameshwar Patil 1
Wouter Gelade 1
Ashwin Swaminathan 1
Aameek Singh 1
Thomi Pilioura 1
Claudio Ardagna 1
Ernesto Damiani 1
Patricia Victor 1
Kim Marriott 1
Maider Azanza 1
Ghazwa Malak 1
Linda Badri 1
Ludmila Marian 1
Craig Knoblock 1
Mishari Almishari 1
Florent Garcin 1
Suleyman Kozat 1
Falk Scholer 1
Siddharth Mitra 1
Pedro Valderas 1
Xiaowei Yang 1
Mor Naaman 1
Weizhong Shao 1
Ben Zhao 1
Maarten De Rijke 1
Myeongjae Jeon 1
Wouter Weerkamp 1
Matteo Picozzi 1
Yang Zhou 1
Saptarshi Ghosh 1
Muhammad Zafar 1
Simon Jonassen 1
Hang Li 1
Xiaolin Wang 1
Waigen Yee 1
Min Zhang 1
Zhengxin Ma 1
Xihui Chen 1
Jun Pang 1
Ran Xue 1
Ïsmaïlcem Arpinar 1
Tao Yu 1
Asser Tantawi 1
Paul Cotter 1
Paul Heymann 1
Michael Schrefl 1
Qiang Yang 1
Xianchao Zhang 1
Stephan Doerfel 1
Simon Walk 1
Ya Zhang 1
Xiaoqingfrank Liu 1
Sabrina De Capitani Di Vimercati 1
Zan Sun 1
Jalal Mahmud 1
I Ramakrishnan 1
Marcus Fontoura 1
Luc Moreau 1
Jan Bussche 1
Emre Kıcıman 1
Geert Bex 1
Ydo Wexler 1
Valentin Robu 1
Harry Halpin 1
Marián Boguñá 1
Derek Leonard 1
Andrew Tappenden 1
Michael Rabinovich 1
Adish Singla 1
Ou Wu 1
Chuan Yue 1
Martine Cock 1
Yafei Li 1
Subbarao Kambhampati 1
Mourad Badri 1
Alejandro Vaisman 1
Flavio Rizzolo 1
Silviu Cucerzan 1
Cesare Pautasso 1
Rattapoom Tuchinda 1
Pedro Szekely 1
Yannis Tzitzikas 1
Radu Jurca 1
Andrew Turpin 1
Athena Vakali 1
Michael Ovelgönne 1
Xiaowei Yang 1
Uri Schonfeld 1
Ivan Skuliber 1
Tomislav Stefanec 1
Krisztian Balog 1
Yazhe Wang 1
Jamie Callan 1
Baihua Zheng 1
Parantapa Bhattacharya 1
Guoli Li 1
Marco Montali 1
Wai Lam 1
Luis Leiva 1
Jinyong Jung 1
Iris Miliaraki 1
Huijia Yu 1
Ke Wang 1
Yangqiu Song 1
Bhaskar DasGupta 1
You Wang 1
Guy Jourdan 1
Jose Pedro 1
Sibel Adalı 1
Meenakshi Nagarajan 1
Li Ding 1
Junichi Tatemura 1
Robert Stevens 1
Keith Bradley 1
Yue Zhang 1
Saehoon Kim 1
Lorrie Cranor 1
Enver Kayaaslan 1
Mauro Conti 1
Arbnor Hasani 1
Paola Mello 1
Sergio Storari 1
Zhen Liao 1
Silvia Quarteroni 1
Jiawei Han 1
Marian Dörk 1
Davide Mazza 1
Ophir Frieder 1
Liyun Ru 1
Arie Van Deursen 1
Diego Fernández 1
Paul Thomas 1
Ahmed Hassan 1
Giorgos Margaritis 1
Athman Bouguettaya 1
Andreas Scholz 1
Stefano Leonardi 1
Boanerges Aleman-Meza 1
Amit Sheth 1
Thomas Lavergne 1
Hartmut Obendorf 1
Suzanne Embury 1
Bernard Jansen 1
Lada Adamic 1
Vanessa Murdock 1
Einat Amitay 1
Zoltán Gyöngyi 1
Ivan Srba 1
Mária Bieliková 1
Yonghui Xu 1
Jialiang Shi 1
Brahim Medjahed 1
Franco Frattolillo 1
Guangyou Zhou 1
Claudio GutiéRrez 1
Masashi Crete-Nishihata 1
Jakub Dalek 1
Jing Wang 1
Stephen Hardiman 1
Cornelia Caragea 1
Prasenjit Mitra 1
Opher Dubrovsky 1
Sihyung Lee 1
Houari Sahraoui 1
Xiaodi Huang 1
Rosa Alarcón 1
Ingo Weber 1
John Hurley 1
Reza Sherkat 1
Nikos Mamoulis 1
Pablo Castells 1
Euiseong Seo 1
Cinzia Cappiello 1
Maristella Matera 1
Zhiyuan Su 1
Ming Li 1
Gabriele Tolomei 1
Alessandro Giuliani 1
Cevdet Aykanat 1
Xuanhieu Phan 1
Bruno Crispo 1
Vinod Muthusamy 1
Maja Pešić 1
Federico Chesani 1
Daxin Jiang 1
Enhong CHEN 1
Jing Jiang 1
Ben Zhao 1
Nikolay Mehandjiev 1
Thomas Johnston 1
Wenpeng Sha 1
Peng Huang 1
SangKeun Lee 1
Sara Comai 1
Yiqun Liu 1
Shaoping Ma 1
Ali Mesbah 1
Stefan Lenselink 1
Gilad Mishne 1
Akhmed Umyarov 1
Pınar Karagöz 1
Xianchao Zhang 1
Mustafa Dincturk 1
Stefan Krompass 1
Irene Garrig'os 1
Emmanuel Chauveau 1
Belle Tseng 1
Carole Goble 1
Halvard Skogsrud 1
Giovanni Pacifici 1
Aleksandar Kuzmanovic 1
Idit Keidar 1
Dan Boneh 1
Krishna Puttaswamy 1
Youngjae Kim 1
Stefano Calzavara 1
Michele Bugliesi 1
Salvatore Orlando 1
Lidong Bing 1
Manolis Koubarakis 1
Hady Lauw 1
Guangyu Zhu 1
Brian Beirne 1
Ali Neyestani 1
Badr Atassi 1
Jens Eliasson 1
Gregor Bochmann 1
Iosif Onut 1
Daniel Gmach 1
Alfons Kemper 1
Jose Maz'on 1
Tanguy Urvoy 1
Pascal Filoche 1
Yun Chi 1
Khalid Belhajjame 1
Norman Paton 1
Régis Saint-Paul 1
Peter Dolog 1
Kweijay Lin 1
Fabrizio Silvestri 1
Hector Garcia-Molina 1
Alissa Cooper 1
Sameh Elnikety 1
Michael Huemer 1
Chunyan Miao 1
Zibin Zheng 1
Michael Lyu 1
Salima Benbernou 1
Jakub Marszałkowski 1
Bruno Ávila 1
Weiliang Zhao 1
Giuseppe Pirró 1
Xiuzhen Zhang 1
Giuseppe Psaila 1
Peter Desnoyers 1
Jiaqian Gu 1
Huaqing Min 1
Dariusz Mokwa 1
Philipp Singer 1
Florian Geigl 1
Huiyuan Zheng 1
Greg Wiseman 1
Ram Gopal 1
Ram Ramesh 1
John Dunagan 1
Saher Esmeir 1
Uwe Zdun 1
Schahram Dustdar 1
Peter Bailey 1
Gonzalo Navarro 1
Marco Comuzzi 1
Hsintsang Lee 1
Xiaodong Wang 1
Marco Anisetti 1
Jing Zhao 1
Manishkumar Jha 1
Barbara Poblete 1
Zhisheng Li 1
Marco Aiello 1
Mikhail Bilenko 1
Jesus Bellido 1
Federica Paci 1
Mourad Ouzzani 1
Dimitris Zeginis 1
Boi Faltings 1
Vicente Pelechano 1
Chris Grier 1
Shuo Tang 1
Emi Garcia-Palacios 1
Aojan Su 1
Dan Hong 1
Hongbo Fu 1
Ivan Budiselić 1
Jeaho Hwang 1
Joonwon Lee 1
Yukun Chen 1
Wil Van Der Aalst 1
Taklam Wong 1
Stefano Ceri 1
Yafei Dai 1
Junghyun Lee 1
Sheelagh Carpendale 1
Yuval Merhav 1
Weize Kong 1
Fidel Cacheda 1
Lu Zhang 1
Pablo Pereira 1
Nan Mou 1
Wenxin Liang 1
Tim Finin 1
Bo Yang 1
Jiming Liu 1
Yuru Lin 1
Filippo Geraci 1
Ian Reay 1
Scott Dick 1
Amruta Joshi 1
Changai Sun 1
Natalia Kwasnikowska 1
Jennifer Golbeck 1
Prabhakar Raghavan 1
Alex Rogers 1
Cecilia Curlango-Rosas 1
Gabriel López-Morteo 1
Hussein Alzoubi 1
Lei Shi 1
Nele Verbiest 1
Frederick Lochovsky 1
Bharath Mohan 1
Michele Colajanni 1
Mariano Consens 1
Sara Casolari 1
Hyeyoung Paik 1
Elisa Bertino 1
Sara Comai 1
Giovanni Toffetti 1
Samueltalmadge King 1
Jing Li 1
Bojana Bislimovska 1
Andrea Pugliese 1
Chengkok Koh 1
Iván Cantador 1
Andrew Bortz 1
Adam Barth 1
Ivan Zuzak 1
Niloy Ganguly 1
Eloisa Vargiu 1
Giuliano Armano 1
Natsuda Kaothanthong 1
Quannan Li 1
Roberto Vivó 1
Jian Pei 1
Yalou Huang 1
Denilson Barbosa 1
John Riedl 1
Vreixo Formoso 1
Alexander Tuzhilin 1
A Vural 1
Rumen Kyusakov 1
Frans Effendi 1
Giovanni Grasso 1
Christian Schallhart 1
Hengjie Song 1
Jianshu Weng 1
Xinyu Wang 1
Wenjie Song 1
Mourad Ouziri 1
Zaki Malik 1
Jan Mizgajski 1
Nikolay Mehandjiev 1
Denis Helic 1
Neil Yorke-Smith 1
Wenbin Cai 1
Muhan Zhang 1
Sharon Goldberg 1
Bing Liu 1
Ana Dias 1
Mingdong Tang 1
Sushil Jajodia 1
Rossano Schifanella 1
Ciro Cattuto 1
Filippo Menczer 1
Shiva Ramanna 1
Yon Dourisboure 1
Evgeniy Gabrilovich 1
Marco Aiello 1
Stijn Vansummeren 1
Darko Kirovski 1
Naren Ramakrishnan 1
Joseph Magnani 1
Ana Maguitman 1
Miguel Serrano 1
Aphrodite Tsalgatidou 1
Peter Moulder 1
Nathan Hurst 1
Weifeng Su 1
Hejun Wu 1
Dinh Phung 1
Alexander Lazovik 1
Arjun Talwar 1
Amit Yadav 1
Derek Eager 1
Sakir Sezer 1
Djoerd Hiemstra 1
Kyungbaek Kim 1
Jianwei Gan 1
Raj Sharman 1
Carsten Hentrich 1
Marco Pellegrini 1
Xin Zhang 1
Yan Shang 1
Han Liu 1
Benjamin Livshits 1
Andrew Tomkins 1
Jasmine Novak 1
Jeremy Schiff 1
Barbara Pernici 1
Martin Arlitt 1
Maxim Gurevich 1
Seungjoon Lee 1
Oliver Spatscheck 1
Vicki Hanson 1
John Richards 1
Benjamin Keller 1
Myra Spiliopoulou 1
Eirini Kaldeli 1
Raju Balakrishnan 1
Renée Miller 1
Arun Iyengar 1
Michail Vlachos 1
Mingfang Wu 1
Sergio Duarte Torres 1
VS Subrahmanian 1
Ying Hu 1
Alejandro Bellogín 1
Collin Jackson 1
Goran Delac 1
Krishna Gummadi 1
Andrea Casini 1
Cam Nguyen 1
Takeshi Tokuyama 1
Liwei Liu 1
Tim Weninger 1
Hans Jacobsen 1
Huanhuan Cao 1
Jongwoo Ha 1
Filipe Mesquita 1
Fei Chen 1
Víctor Carneiro 1
Li Zhang 1
Eduard Dragut 1
Jerker Delsing 1
Qi Yu 1
Seunghwan Ryu 1
Michael Schäfer 1
Mike Spreitzer 1
Bernardo Huberman 1
Flavio Junqueira 1
Georgia Koutrika 1
Wei Wei 1
Xiaogang Han 1
Yuanhong Shen 1
Rafael Lins 1
Daniel Zoller 1
Thomas Niebler 1
Weifeng Su 1
Yaoyi Chiang 1
Yan Wang 1
Sujatha Gollapalli 1
Timothy Wood 1
Luca Aiello 1
Alain Barrat 1
Benjamin Markines 1
Helen Wang 1
Charles Reis 1
Thomas Risse 1
Stefano Tranquillini 1
Pavel Kucherbaev 1
Frank Neven 1
Francisco Claude 1
Cherian Mathew 1
Esther David 1
Weiming Hu 1
Francesco Saonara 1
Brett Adams 1
Svetha Venkatesh 1
Ehsan Warriach 1
Mauro Andreolini 1
Jian Yin 1
Massimo Mecella 1
Vassilis Christophides 1
Mayank Agrawal 1
Vassiliki Koutsonikola 1
Matthias Bröcheler 1
Michael Sirivianos 1
Tye Rattenbury 1
Alessandra Sala 1
Xinxin Fan 1
James Thom 1
Stergios Anastasiadis 1
Stefano Soi 1
Martin Wimmer 1
Yi Qian 1
Sven Casteleyn 1
Carlos Castillo 1
Debora Donato 1
Anupam Joshi 1
Harald Weinreich 1
Matthias Mayer 1
Karen Church 1
Matthew Richardson 1
Seungwon Hwang 1
Yuxiong He 1
Seungjin Choi 1
Kaweh Naini 1
Qingyao Wu 1
Zhaoxing Li 1
Shaoping Zhu 1
Wenxin Liang 1
Iheb Amor 1
Jian Yang 1
Adam Senft 1
Renata Fortes 1
Sara Foresti 1
Pierangela Samarati 1
Rahul Singh 1
Saikat Mukherjee 1
Mohammad Alrifai 1
Vanja Josifovski 1
Lance Riedel 1
Tong Zhang 1
Giridhar Kumaran 1
Renan Cattelan 1
Hana Shepherd 1
Micah Dubinko 1
Ravi Kumar 1
Nicholas Jennings 1
Santo Fortunato 1
Alessandro Vespignani 1
Dmitri Loguinov 1
Gregorio Ponce 1
Jacobus Van Der Merwe 1
Haining Wang 1
Chris Cornelis 1
Hongmin Cai 1
Tianqiang Huang 1

Affiliation Paper Counts
Universite Libre de Bruxelles 1
University of Padua 1
University of Michigan 1
Charles Sturt University 1
Guangdong Polytechnic Normal University 1
University of Buenos Aires 1
Universitat de Barcelona 1
Universidad Jaume I 1
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Princeton University 1
University of Sannio 1
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Johns Hopkins University 1
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Koc University 1
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University of Koblenz-Landau 1
Chonnam National University 1
Jilin University 1
Vrije Universiteit Amsterdam 1
IBM Almaden Research Center 1
Wayne State University 1
Telefonica 1
University of Colorado at Colorado Springs 1
The University of British Columbia 1
Central China Normal University 1
Kyoto University 1
South National University 1
Beihang University 1
Indian Institute of Science 1
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Hong Kong Baptist University 1
Federal University of Uberlandia 1
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Yonsei University 1
Kassel University 1
George Mason University 1
University of Magdeburg 1
Fujian Normal University 1
San Diego State University 1
Boston University 1
European Organization for Nuclear Research 1
Seoul Women's University 1
Aalborg University 1
University of Connecticut 1
Georgetown University 1
Oak Ridge National Laboratory 1
University of North Texas 1
Hunan University of Science and Technology 1
University of Ferrara 1
City University London 1
University of Edinburgh 1
Federal Technological University of Parana 1
Universite de Pau et des Pays de L'Adour 1
Universidad de Granada 1
Northeastern University 1
IBM Zurich Research Laboratory 1
Commonwealth Scientific and Industrial Research Organization 1
Temple University 1
University of Cambridge 1
Eastern Michigan University 1
Korea Advanced Institute of Science & Technology 1
Pontifical Catholic University of Rio de Janeiro 1
Intel Corporation 1
Nanjing University 1
Cyprus University of Technology 1
Nanyang Technological University School of Computer Engineering 1
Siemens USA 1
Istituto di Scienza e Tecnologie dell'Informazione A. Faedo 1
Computer Sciences Corporation in Deutschland 1
Orange Labs 1
Turgut Ozal University 1
Qatar Foundation 1
Case Western Reserve University 2
University of Amsterdam 2
University of Ioannina 2
University of Bergamo 2
Nanyang Technological University 2
University of Lugano 2
Slovak University of Technology in Bratislava 2
Northwestern University 2
Sungkyunkwan University 2
University of Sao Paulo 2
Tohoku University 2
Duke University 2
University of Dundee 2
Linkoping University 2
University of Montreal 2
HP Labs 2
Universitat d'Alicante 2
University of Turin 2
Universidad de Chile 2
Aristotle University of Thessaloniki 2
Nankai University 2
Vienna University of Technology 2
Wright State University 2
Simon Fraser University 2
University of Calabria 2
University of Twente 2
Indiana University 2
Sun Yat-Sen University 2
Italian National Research Council 2
University of Quebec in Trois-Rivieres 2
New York University 2
Johannes Kepler University Linz 2
Missouri University of Science and Technology 2
University of Maryland, Baltimore County 2
Tata Research Development and Design Centre 2
Rensselaer Polytechnic Institute 2
Huazhong University of Science and Technology 2
University of Vienna 2
University of California, Los Angeles 2
Pontificia Universidad Catolica de Chile 2
The University of Georgia 2
Rutgers, The State University of New Jersey 2
Federal University of Pernambuco 2
University of Cagliari 2
Izmir University 2
University of Crete 3
Max Planck Institute for Software Systems 3
Chinese University of Hong Kong 3
University of Modena and Reggio Emilia 3
Gottfried Wilhelm Leibniz Universitat 3
University College Dublin 3
Delft University of Technology 3
The University of Hong Kong 3
University of Zagreb 3
Purdue University 3
France Telecom Division Recherche et Developpement 3
University of Wurzburg 3
NEC Laboratories America, Inc. 3
University of Science and Technology Beijing 3
University of Science and Technology of China 3
Chinese Academy of Sciences 3
Monash University 3
Graz University of Technology 3
Binghamton University State University of New York 3
Shanghai Jiaotong University 3
Universidad Autonoma de Madrid 3
University of Oxford 3
Virginia Tech 3
Swiss Federal Institute of Technology, Lausanne 3
University of Bologna 3
University of Hamburg 3
Universite Paris Descartes 3
Queen's University Belfast 3
Curtin University of Technology, Perth 3
University at Buffalo, State University of New York 3
University of Roma La Sapienza 3
Indian Institute of Technology, Delhi 3
Eindhoven University of Technology 3
Ghent University 3
University of Luxembourg 3
Institute for Scientific Interchange Foundation 3
Zhejiang University 4
Korea University 4
Lulea University of Technology 4
University of California, Irvine 4
Poznan University of Technology 4
IBM Research 4
Universidad Politecnica de Valencia 4
University of Massachusetts Amherst 4
Peking University 4
Singapore Management University 4
Georgia Institute of Technology 4
Texas A and M University 4
Technion - Israel Institute of Technology 4
Universidad de A Coruna 4
Stony Brook University 4
University of Ottawa, Canada 4
University of Athens 4
University of Southern California 4
University of Calgary 5
Arizona State University 5
University of Groningen 5
Hasselt University 5
Ca' Foscari University of Venice 5
Macquarie University 5
Technical University of Munich 5
University of Maryland 5
South China University of Technology 5
University of Southampton 5
University of the Basque Country 5
University of Illinois at Urbana-Champaign 6
Middle East Technical University 6
RMIT University 6
University of Toronto 6
Carnegie Mellon University 6
University of New South Wales 6
University of California, Santa Barbara 6
University of Milan 6
Pennsylvania State University 7
Bilkent University 7
IBM Thomas J. Watson Research Center 7
University of Alberta 7
University of Manchester 8
Dalian University of Technology 8
Microsoft 8
Hong Kong University of Science and Technology 9
University of Trento 10
Tsinghua University 10
University of Illinois at Chicago 10
Stanford University 11
Microsoft Research Asia 11
Yahoo Research Barcelona 12
Politecnico di Milano 15
Microsoft Research 17
Yahoo Research Labs 20
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