ACM Transactions on

the Web (TWEB)

Latest Articles


This work aimed to propose LDoW-PaN, 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. Through the LDoW-PaN model, ordinary people—who have no experience with technologies that involve the Linked Data environment—can interact with the... (more)

Clickstream User Behavior Models

The next generation of Internet services is driven by users and user-generated content. The complex nature of user behavior makes it highly challenging to manage and secure online services. On one hand, service providers cannot effectively prevent attackers from creating large numbers of fake identities to disseminate unwanted content (e.g., spam).... (more)

Canonical Forms for Isomorphic and Equivalent RDF Graphs

Existential blank nodes greatly complicate a number of fundamental operations on Resource Description Framework (RDF) graphs. In particular, the... (more)

A Study of Web Print

This article analyzes a proprietary log of printed web pages and aims at answering questions regarding the content people print (what), the reasons they print (why), as well as attributes of their print profile (who). We present a classification of pages printed based on their print intent and we describe our methodology for processing the print... (more)

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.... (more)

Collusive Opinion Fraud Detection in Online Reviews

We address the collusive opinion fraud problem in online review portals, where groups of people work together to deliver deceptive reviews for... (more)


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
Faster Base64 Encoding and Decoding using AVX2 Instructions

Web developers use base64 formats to include images, fonts, sounds and other resources directly inside HTML, JavaScript, JSON and XML files. We estimate that billions of base64 messages are decoded every day. We are motivated to improve the efficiency of base64 encoding and decoding. Compared to state-of-the-art implementations, we multiply the speeds of both the encoding (H10×) and the decoding (H7×). We achieve these good results by using the single-instruction-multiple-data (SIMD) instructions available on recent Intel processors (AVX2). Our accelerated software abides by the specification and reports errors when encountering characters outside of the base64 set. It is available online as free software under a liberal license.

Activity Recommendation with Partners

Recommending social activities, such as watching movies or having dinner is a typical function found in social networks or e-commerce sites. Besides certain websites which manage activity-related locations (e.g.,, many items on product sale platforms (e.g, can naturally be mapped to social activities. For example, movie tickets can be thought of as activity items, which map to social activity "watch a movie". Traditional recommenders estimate the degree of interest by the target user to candidate activity items and, accordingly, promote the top-k activity items to the user. However, these systems ignore an important characteristic of social activities: people like to participate in them with their folks. This paper is the first one to consider this fact and improves the effectiveness of recommendation in two directions. First, we show that people, more often than not, prefer to find partners before participation in social activities. This means that if a system recommends an activity item alone, the user may give up the item, if she cannot think of a partner to attend the activity together. Therefore, we study the problem of activity-partner recommendation; i.e., for each recommended activity item, find a suitable partner for the user. This (i) saves the users time for finding activity partners, (ii) increases the likelihood that the activity item will be selected by the user, (iii) improves the effectiveness of recommender systems to users overall and enkindles their social enthusiasm. Our partner recommender is built upon the users historical attendance preferences, their social context, and geographic information. Second, we explore the impact of finding suitable partners to improve the effectiveness of recommending activities to users. Assuming that users tend to select the activities for which they can find suitable partners, we propose a partner-aware activity recommendation model, which integrates this hypothesis into conventional recommendation approaches. Our method first estimates the probability that the target user can find suitable partners for each candidate activity item, and then considers this probability when ranking them. Finally, the recommended items not only match the users' interests, but also have high chances to be selected by the users, because the users can find suitable partners to attend the corresponding activities together. We conduct experiments on real data that evaluate the effectiveness of activity-partner recommendation and partner-aware activity recommendation. The results verify that (i) suggesting partners greatly improves the likelihood that a recommended activity item is selected by the target user and (ii) considering the existence of suitable partners in the ranking of recommended items improves the accuracy of recommendation significantly.

Recommendation in a Changing World: Exploiting Temporal Dynamics in Ratings and Reviews

Users preferences, and consequently their ratings and re- views to items, change over time. Likewise, characteristics of items are also time-varying. By dividing data into time periods, temporal Recommender Systems (RSs) improve recommendation accuracy by exploring the temporal dynamics in user rating data. However, temporal RSs have to cope with rating sparsity in each time period. Meanwhile, reviews generated by users contain rich information about their preferences, which can be exploited to address rating sparsity and further improve the performance of temporal RSs. In this paper, we develop a temporal rating model with topics that jointly mines the temporal dynamics of both user-item ratings and reviews. Studying temporal drifts in reviews help us understand item rating evolutions and user interest changes over time. Our model also automatically splits the review text in each time period into interim words and intrinsic words. By linking interim words and intrinsic words to short-term and long-term item features respectively, we jointly mine the temporal changes in user and item latent features together with the associated review text in a single learning stage. Through experiments on 28 real world datasets collected from Amazon, we show that the rating prediction accuracy of our model significantly outperforms the existing state-of-art RS models. And our model can automatically identify representative interim words in each time period as well as intrinsic words cross all time periods. This can be very useful in understanding the time evolution of users preferences and items characteristics.

Exploring the Emerging Type of Comment for Online Videos: DanMu

DanMu, an emerging type of user-generated comment has been increasingly popular in recent years. Many online video platforms such as have provided the DanMu function. Unlike traditional online review such as reviews at that are outside videos, the DanMu is scrolling marquee comment, which is overlaid directly on top of the video and synchronized to a specific playback time. Such comments are displayed as streams of moving subtitles overlaid on the video screen. Viewers could easily write DanMus while watching videos and the written DanMus will be immediately overlaid onto the video and displayed to writers themselves and other viewers as well. Such DanMu systems have greatly enabled users to communicate with each other in a much more direct way creating a real-time sense of sharing watching experience. Although we see much unique natures of DanMu and great impact on online video systems, to the best of our knowledge, there is no work that has provided a comprehensive study on DanMu. In this paper, as a pilot study, we analyse the unique characteristics of DanMu from various perspectives. Specifically, we first illustrate some unique distributions of DanMus by comparing with traditional reviews (TReviews) that we collected from a real DanMu-enabled online video system. Second we discover two interesting patterns in DanMu data: herding effect and multiple-burst phenomena that are significantly different from those in TRviews and reveal important insights about the growth of DanMus on a video. Towards exploring antecedents of both herding effect and multiple-burst phenomena, we propose to further detect leading DanMus within bursts because those leading DanMus make the most contribution to both patterns. A framework is proposed to detect leading DanMus that effectively combines multiple factors contributing to lead DanMus. Based on the identified characteristics of DanMu, finally we propose to predict the distribution of future DanMus (i.e., the growth of DanMus), which is important for many DanMu-enabled online video systems. This prediction task includes two aspects: one is to predict which videos future DanMus will be posted for; the other one is to predict which segments of a video future DanMus will be posted on, we develop two sophisticated models to solve both problems. Finally, intensive experiments are conducted with a real-world data set to validate all methods developed in this paper.

Adaptive Knowledge Propagation in Web Ontologies

The increasing availability of structured machine-processable knowledge in the Web of Data calls for machine learning methods to support standard reasoning based services (such as query-answering and logic inference). Inductive and transductive reasoning algorithms can efficiently exploit statistical regularities in the inherently incomplete knowledge bases distributed across the Web. This paper focuses on the problem of predicting missing class-memberships and property values of individual resources in Web ontologies. We propose a transductive learning method for inferring missing properties about individuals: given a class-membership/property value learning problem, we address the task of identifying relations which are likely to link similar individuals, and efficiently propagating knowledge across such (possibly diverse) relations. Our experimental evaluation demonstrates the effectiveness of the proposed method.

Modeling and Simulating the Web of Things from an Information Retrieval Perspective

Internet and Web technologies have changed our lives in ways we are not even fully aware. In the near future, Internet will interconnect more than 50 billion of things of the real world, nodes will sense billions of features of interest and properties, and things will be represented with Web-based bi-directional services with high-dynamic content and real-time data. This is the new era of the Internet and the Web of Things. The emergence of such paradigms implies the evolution and integration of the systems which they interact with. Thereby, it is essential to develop abstract models for representing, and simulating the Web of Things in order to establish new approaches. A model of the Web of Things based on a structured XML representation is described in this paper. We also present a simulator whose ultimate goal is to encapsulate the expected dynamics of the Web of Things, for the future development of Information Retrieval (IR) systems. The sim- ulator generates a real-time collection of XML documents, which contain spatio-temporal contexts, textual and sensed information with highly dynamic dimensions. The simulator is characterized among others for its flexibility and versatility to represent real-world scenarios and a unique perspective from information retrieval, we tested the simulator in terms of fundamentals variables.

Knowledge Graph Embedding: A Locally and Temporally Adaptive Translation based Approach

A Knowledge graph is a graph with entities of different types as nodes and various relations among them as edges. The constructions of knowledge graphs in the past decades facilitate many applications, such as link prediction, web search analysis, question answering, etc. Knowledge graph embedding aims to represent entities and relations in a large-scale knowledge graph as elements in a continuous vector space. Existing methods, e.g., TransE, TransH and TransR, learn the embedding representation by defining a global margin-based loss function over the data. However, the optimal loss function is determined during experiments whose parameters are examined among a closed set of candidates. Moreover, embeddings over two knowledge graphs with different entities and relations share the same set of candidate loss functions, ignoring the locality of both graphs. This leads to the limited performance of embedding related applications. In this paper, a locally adaptive translation method for knowledge graph embedding, called TransA, is proposed to find the optimal loss function by adaptively determining its margin over different knowledge graphs. Then the convergence of TransA is verified from the aspect of its uniform stability. To make the embedding methods up-to-date when new vertices and edges are added into the knowledge graph, the incremental algorithm for TransA, called iTransA, is proposed by adaptively adjusting the optimal margin over time. Experiments on two benchmark data sets demonstrate the superiority of the proposed method, as compared to the-state-of-the-art ones.

Caching to Reduce Mobile App Energy Consumption

Mobile applications consume device energy for their operations, and the fast rate of battery depletion on mobile devices poses a major usability hurdle. After the display, data communication is the second-biggest consumer of mobile device energy. At the same time, software applications that run on mobile devices represent a fast-growing product segment. Typically, these applications serve as front-end display mechanisms, which fetch data from remote servers and display the information to the user in an appropriate format -- incurring significant data communication overheads in the process. In this work, we propose methods to reduce energy overheads in mobile devices due to data communication by leveraging data caching technology. A review of existing caching mechanisms revealed that they are primarily designed for optimizing performance and cannot be easily ported to mobile devices for energy savings. Further, architectural differences between traditional client-server and mobile communications infrastructures make the use of existing caching technologies unsuitable in mobile devices. In this paper, we propose a set of two new caching approaches specifically designed with the constraints of mobile devices in mind: (a) a response caching approach, and (b) an object caching approach. Our experiments show that, even for a small cache size of 250 MB, object caching can reduce energy consumption on average by 45%, compared to the no-cache case, and response caching can reduce energy consumption by 20% compared to the no-cache case. The benefits increase with larger cache sizes. These results demonstrate the efficacy of our proposed method, and raise the possibility of significantly extending mobile device battery life.

A Fast and Scalable Mechanism for Web Service Composition

In recent times, automated business processes and web services have become ubiquitous in diverse application spaces. Efficient composition of web services in real time while providing necessary Quality of Service (QoS) guarantees is a computationally complex problem and several heuristic based approaches have been proposed to compose services optimally. In this paper, we present the design of a scalable QoS-aware service composition mechanism which balances the computational complexity of service composition with the QoS guarantees of the composed service and achieves scalability. On one hand, we handle the case of a single QoS parameter using an intelligent search and pruning mechanism in the composed service space and show that our methodology yields near optimal solution on real benchmarks. On the other hand, we handle the case of multiple QoS parameters using aggregation techniques. As a final contribution, we explore search time versus solution quality trade-off using parameterized search algorithms that produce better quality solutions at the cost of time. We present experimental results to show the efficiency of our proposed mechanism.


Publication Years 2007-2017
Publication Count 224
Citation Count 2291
Available for Download 224
Downloads (6 weeks) 1554
Downloads (12 Months) 15930
Downloads (cumulative) 179541
Average downloads per article 802
Average citations per article 10
First Name Last Name Award
Ricardo A Baeza-Yates ACM Fellows (2009)
Elisa Bertino ACM Fellows (2003)
Maria Bielikova ACM Senior Member (2009)
Dan Boneh ACM Fellows (2016)
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)
ACM Fellows (2007)
Carlos A. Castillo ACM Senior Member (2014)
Stefano Ceri ACM Fellows (2013)
Chen-Nee Chuah ACM Distinguished Member (2012)
ACM Senior Member (2006)
Lorrie Faith Cranor ACM Fellows (2014)
ACM Senior Member (2006)
Ernesto Damiani ACM Distinguished Member (2008)
Schahram Dustdar ACM Distinguished Member (2009)
Christos Faloutsos ACM Fellows (2010)
Elena Ferrari ACM Distinguished Member (2011)
Ophir Frieder ACM Fellows (2005)
Hector Garcia-Molina ACM Fellows (1997)
Lee Giles ACM Fellows (2006)
Vicki Hanson ACM Fellows (2004)
Simon Harper ACM Distinguished Member (2014)
ACM Senior Member (2009)
Monika Henzinger ACM Fellows (2016)
Djoerd Hiemstra ACM Senior Member (2009)
Eric Horvitz ACM AAAI Allen Newell Award (2015)
ACM Fellows (2014)
Craig Knoblock ACM Distinguished Member (2008)
Ming Li ACM Fellows (2006)
Bing Liu ACM Fellows (2015)
Yiqun Liu ACM Senior Member (2016)
Dmitri Loguinov ACM Distinguished Member (2014)
ACM Senior Member (2007)
Filippo Menczer ACM Distinguished Member (2013)
Renee J Miller ACM Fellows (2009)
Mourad Ouzzani ACM Senior Member (2009)
Jian Pei ACM Fellows (2015)
ACM Senior Member (2007)
Prabhakar Raghavan ACM Fellows (2001)
Naren Ramakrishnan ACM Distinguished Member (2009)
John T Riedl ACM Software System Award (2010)
ACM Fellows (2009)
ACM Distinguished Member (2007)
Michael Rung-Tsong Lyu ACM Fellows (2015)
Prashant J Shenoy ACM Distinguished Member (2009)
ACM Senior Member (2006)
Ingmar Weber ACM Senior Member (2017)
Xing Xie ACM Senior Member (2010)
Qiang Yang ACM Distinguished Member (2011)
Philip S Yu ACM Fellows (1997)
Lixia Zhang ACM Fellows (2006)
Ben Yanbin Zhao ACM Distinguished Member (2015)
Yu Zheng ACM Distinguished Member (2016)
ACM Senior Member (2011)
Yu Zheng ACM Distinguished Member (2016)
ACM Senior Member (2011)

First Name Last Name Paper Counts
İsmail Altıngövde 6
Ryen White 5
Berkant Cambazoglu 4
Weiying Ma 4
Ingmar Weber 4
Xing Xie 4
Wolfgang Nejdl 4
Markus Strohmaier 3
Ben Zhao 3
Ricardo Baeza-Yates 3
Anirban Mahanti 3
Ling Liu 3
Rifat Ozcan 3
Fabio Casati 3
Özgür Ulusoy 3
Weiyi Meng 3
Cristóbal Arellano 2
Sergiu Chelaru 2
Carey Williamson 2
Haitao Zheng 2
Gang Wang 2
Niklas Carlsson 2
Florian Daniel 2
Mudhakar Srivatsa 2
Prashant Shenoy 2
Bernard Jansen 2
Cornelia Caragea 2
Óscar Díaz 2
Eda Baykan 2
Stefan Siersdorfer 2
Xiangye Xiao 2
Enhong Chen 2
Boualem Benatallah 2
Clement Yu 2
Andrei Broder 2
Clyde Giles 2
Philipp Singer 2
Christo Wilson 2
Denis Helic 2
Eepeng Lim 2
Aphrodite Tsalgatidou 2
James Miller 2
Marco Aiello 2
Philip YU 2
Alessandro Bozzon 2
Eric Horvitz 2
Phillipa Gill 2
Ziv Bar-Yossef 2
Piero Fraternali 2
Barry Smyth 2
Andreas Hotho 2
Monika Henzinger 2
Qiong Luo 2
Freddy Lécué 2
Yu Zheng 2
Marco Brambilla 2
Dominik Deja 1
Sukru Eraslan 1
Barbara Carminati 1
Jiaqian Gu 1
Huaqing Min 1
Dariusz Mokwa 1
Florian Geigl 1
Huiyuan Zheng 1
Greg Wiseman 1
Ahmet Sarıyüce 1
Comandur Seshadhri 1
Elad Kravi 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
Sergio Rojas-Galeano 1
Mohammad Rahman 1
Bolun Wang 1
B Prakash 1
Marco Comuzzi 1
Hsintsang Lee 1
Xiaodong Wang 1
Jing Zhao 1
Manishkumar Jha 1
Barbara Poblete 1
Zhisheng Li 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
Simon Gottschalk 1
Rahul Balakavi 1
Jagdish Achara 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
Lu Zhang 1
Pablo Pereira 1
Nan Mou 1
Wenxin Liang 1
Tim Finin 1
Bo Yang 1
Jiming Liu 1
Yuru Lin 1
Fidel Cacheda 1
Massimo Bernaschi 1
James Thom 1
Seunghwan Ryu 1
Michael Schäfer 1
Mike Spreitzer 1
Flavio Junqueira 1
Georgia Koutrika 1
Bernardo Huberman 1
Guangming Guo 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
Ümit Çatalyürek 1
Sujatha Gollapalli 1
Timothy Wood 1
Thomas Risse 1
Luca Aiello 1
Alain Barrat 1
Benjamin Markines 1
Helen Wang 1
Charles Reis 1
Stefano Tranquillini 1
Pavel Kucherbaev 1
Frank Neven 1
Francisco Claude 1
Cherian Mathew 1
Esther David 1
Daiping Liu 1
Weiming Hu 1
Francesco Saonara 1
Ehsan Warriach 1
Brett Adams 1
Svetha Venkatesh 1
Mauro Andreolini 1
Jian Yin 1
Massimo Mecella 1
Vassiliki Koutsonikola 1
Vassilis Christophides 1
Mayank Agrawal 1
Prasant Mohapatra 1
Matthias Bröcheler 1
Michael Sirivianos 1
Tye Rattenbury 1
Alessandra Sala 1
Xinxin Fan 1
Sihyung Lee 1
Enver Kayaaslan 1
Mauro Conti 1
Arbnor Hasani 1
Jiawei Han 1
Paola Mello 1
Sergio Storari 1
Zhen Liao 1
Silvia Quarteroni 1
Marian Dörk 1
Davide Mazza 1
Ophir Frieder 1
Liyun Ru 1
Arie Van Deursen 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
Ivan Srba 1
Mária Bieliková 1
Diego Fernández 1
Alessandro Celestini 1
Suzanne Embury 1
Vanessa Murdock 1
Zoltán Gyöngyi 1
Einat Amitay 1
Lada Adamic 1
Radoslaw Nielek 1
Feida Zhu 1
Le Wu 1
Malik Magdon-Ismail 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
Eleni Koutrouli 1
Jing Wang 1
Stephen Hardiman 1
Prasenjit Mitra 1
Opher Dubrovsky 1
Filippo Geraci 1
Ian Reay 1
Scott Dick 1
Amruta Joshi 1
Changai Sun 1
Jennifer Golbeck 1
Bharath Mohan 1
Prabhakar Raghavan 1
Alex Rogers 1
Yongjae Lee 1
Natalia Kwasnikowska 1
Divya Sambasivan 1
Lei Li 1
Cecilia Curlango-Rosas 1
Gabriel López-Morteo 1
Hussein Alzoubi 1
Lei Shi 1
Marco Anisetti 1
Nele Verbiest 1
Frederick Lochovsky 1
Mariano Consens 1
Sara Casolari 1
Michele Colajanni 1
Jing Li 1
Hyeyoung Paik 1
Elisa Bertino 1
Sara Comai 1
Giovanni Toffetti 1
Samueltalmadge King 1
Silvia Uribe 1
Javier Parra-Arnau 1
Bojana Bislimovska 1
Andrea Pugliese 1
Iván Cantador 1
Adam Barth 1
Andrew Bortz 1
Ivan Zuzak 1
Chengkok Koh 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
Yi Qian 1
Sven Casteleyn 1
Stergios Anastasiadis 1
Stefano Soi 1
Martin Wimmer 1
Carlos Castillo 1
Debora Donato 1
Anupam Joshi 1
Harald Weinreich 1
Matthias Mayer 1
Seungwon Hwang 1
Yuxiong He 1
Seungjin Choi 1
Kaweh Naini 1
Cássio Prazeres 1
Xinyi Zhang 1
Aidan Hogan 1
Jie Zhang 1
Karen Church 1
Matthew Richardson 1
Qi Liu 1
Allen Lavoie 1
Leila Bahri 1
Qingyao Wu 1
Zhaoxing Li 1
Shaoping Zhu 1
Wenxin Liang 1
Iheb Amor 1
Jian Yang 1
Adam Senft 1
Valeria De Antonellis 1
Renata Fortes 1
Sara Foresti 1
Pierangela Samarati 1
Rahul Singh 1
Mohammad Alrifai 1
Saikat Mukherjee 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
Alexey Drutsa 1
Haining Wang 1
Angelos Stavrou 1
Dmitri Loguinov 1
Gregorio Ponce 1
Jacobus Van Der Merwe 1
Haining Wang 1
Chris Cornelis 1
Hongmin Cai 1
Tianqiang Huang 1
Houari Sahraoui 1
Xiaodi Huang 1
Rosa Alarcón 1
Reza Sherkat 1
Nikos Mamoulis 1
Ingo Weber 1
John Hurley 1
Mohamed Kaafar 1
Jeonhyung Kang 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
Thomas Johnston 1
Vinod Muthusamy 1
Maja Pešić 1
Federico Chesani 1
Daxin Jiang 1
Jing Jiang 1
Wenpeng Sha 1
Peng Huang 1
SangKeun Lee 1
Sara Comai 1
Yiqun Liu 1
Shaoping Ma 1
Stefan Lenselink 1
Gilad Mishne 1
Akhmed Umyarov 1
Pınar Karagöz 1
Irene Garrig'os 1
Xianchao Zhang 1
Mustafa Dincturk 1
Stefan Krompass 1
Emmanuel Chauveau 1
Belle Tseng 1
Nikolay Mehandjiev 1
Ali Mesbah 1
Giovanni Grasso 1
Christian Schallhart 1
Flavio Lombardi 1
Carole Goble 1
Halvard Skogsrud 1
Giovanni Pacifici 1
Frans Effendi 1
Maria Rafalak 1
Simon Harper 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
Neil Yorke-Smith 1
Wenbin Cai 1
Muhan Zhang 1
Sharon Goldberg 1
Mingdong Tang 1
Yehoshua Sagiv 1
Fatemeh Vahedian 1
Ana Dias 1
Bing Liu 1
Sushil Jajodia 1
Rossano Schifanella 1
Ciro Cattuto 1
Filippo Menczer 1
Shiva Ramanna 1
Yon Dourisboure 1
Evgeniy Gabrilovich 1
Stijn Vansummeren 1
Ydo Wexler 1
Darko Kirovski 1
Naren Ramakrishnan 1
Joseph Magnani 1
Ana Maguitman 1
Miguel Serrano 1
Christos Faloutsos 1
Pavel Serdyukov 1
Haitao Xu 1
Peter Moulder 1
Nathan Hurst 1
Weifeng Su 1
Hejun Wu 1
Alexander Lazovik 1
Dinh Phung 1
Arjun Talwar 1
Amit Yadav 1
Derek Eager 1
Sakir Sezer 1
Elena Demidova 1
Partha Mukherjee 1
José Menéndez 1
Djoerd Hiemstra 1
Kyungbaek Kim 1
Jianwei Gan 1
Idit Keidar 1
Dan Boneh 1
Krishna Puttaswamy 1
Aleksandar Kuzmanovic 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
Jose Maz'on 1
Gregor Bochmann 1
Iosif Onut 1
Daniel Gmach 1
Alfons Kemper 1
Tanguy Urvoy 1
Pascal Filoche 1
Yun Chi 1
Sameh Elnikety 1
Michael Huemer 1
Christo Wilson 1
Qian Lin 1
Stefano Guarino 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
Adam Wierzbicki 1
Elena Ferrari 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
Ali Pınar 1
Devis Bianchini 1
Yaron Kanza 1
Bamshad Mobasher 1
Giuseppe Psaila 1
Peter Desnoyers 1
Raj Sharman 1
Carsten Hentrich 1
Marco Pellegrini 1
Xin Zhang 1
Yan Shang 1
Han Liu 1
Benjamin Livshits 1
Benjamin Keller 1
Andrew Tomkins 1
Jasmine Novak 1
Jeremy Schiff 1
Yasushi Sakurai 1
Barbara Pernici 1
Martin Arlitt 1
Maxim Gurevich 1
Seungjoon Lee 1
Oliver Spatscheck 1
Vicki Hanson 1
John Richards 1
Eirini Kaldeli 1
Raju Balakrishnan 1
Myra Spiliopoulou 1
Renée Miller 1
Arun Iyengar 1
Michail Vlachos 1
Mingfang Wu 1
Aruna Seneviratne 1
Sergio Duarte Torres 1
VS Subrahmanian 1
Alejandro Bellogín 1
Collin Jackson 1
Goran Delac 1
Ying Hu 1
Krishna Gummadi 1
Andrea Casini 1
Cam Nguyen 1
Takeshi Tokuyama 1
Tim Weninger 1
Hans Jacobsen 1
Huanhuan Cao 1
Jongwoo Ha 1
Filipe Mesquita 1
Fei Chen 1
Li Zhang 1
Eduard Dragut 1
Jerker Delsing 1
Qi Yu 1
Luca Becchetti 1
Eelco Herder 1
Hari Sundaram 1
Pedro Leon 1
Blase Ur 1
Wolf Siberski 1
Tim Furche 1
Liwei Liu 1
Víctor Carneiro 1
Shiliang Tang 1
Georgia Koutrika 1
Islam Elgedawy 1
Zahir Tari 1
Bhuvan Urgaonkar 1
Aristides Gionis 1
Vassilis Plachouras 1
Jure Leskovec 1
Michał Kąkol 1
Nishida Toyoaki 1
Jianke Zhu 1
Maciej Drozdowski 1
Rik Eshuis 1
Michael Paul 1
Guibing Guo 1
Jie Zhang 1
Wensheng Wu 1
Valeria Fionda 1
Kenneth Fletcher 1
Haibin Zhang 1
Michele Melchiori 1
Robin Burke 1
A Panagopoulos 1
Liran Katzir 1
Willianmassami Watanabe 1
Stefano Paraboschi 1
Sangameshwar Patil 1
Harrick Vin 1
Wouter Gelade 1
Ashwin Swaminathan 1
Aameek Singh 1
Chennee Chuah 1
Tianyi Wang 1
Zengbin Zhang 1
Kim Marriott 1
Claudio Ardagna 1
Ernesto Damiani 1
Patricia Victor 1
Maider Azanza 1
Ghazwa Malak 1
Linda Badri 1
Ludmila Marian 1
Craig Knoblock 1
Mishari Almishari 1
Xiaowei Yang 1
Florent Garcin 1
Suleyman Kozat 1
Falk Scholer 1
Siddharth Mitra 1
Pedro Valderas 1
Federico Álvarez 1
Suranga Seneviratne 1
Thomi Pilioura 1
Kristina Lerman 1
Mor Naaman 1
Weizhong Shao 1
Ben Zhao 1
Myeongjae Jeon 1
Matteo Picozzi 1
Wouter Weerkamp 1
Maarten De Rijke 1
Yang Zhou 1
Muhammad Zafar 1
Saptarshi Ghosh 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
Bhaskar DasGupta 1
Ïsmaïlcem Arpinar 1
Michael Schrefl 1
André Rocha 1
Tao Yu 1
Asser Tantawi 1
Paul Cotter 1
Paul Heymann 1
Chu Guan 1
Yeliz Yeşilada 1
Sanmay Das 1
Qiang Yang 1
Xianchao Zhang 1
Stephan Doerfel 1
Simon Walk 1
Ya Zhang 1
Xiaoqingfrank Liu 1
Eliyahu Safra 1
Sabrina De Capitani Di Vimercati 1
Zan Sun 1
Jalal Mahmud 1
I Ramakrishnan 1
Marcus Fontoura 1
Emre Kıcıman 1
Geert Bex 1
Valentin Robu 1
Harry Halpin 1
Marián Boguñá 1
Jinyoung Han 1
Luc Moreau 1
Jan Bussche 1
Xing Li 1
Yasuko Matsubara 1
Gleb Gusev 1
Derek Leonard 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
Flavio Rizzolo 1
Alejandro Vaisman 1
Silviu Cucerzan 1
Cesare Pautasso 1
Rattapoom Tuchinda 1
Pedro Szekely 1
Athena Vakali 1
Yannis Tzitzikas 1
Radu Jurca 1
Andrew Turpin 1
Claude Castelluccia 1
Andrew Tappenden 1
Anna Squicciarini 1
Michael Ovelgönne 1
Xiaowei Yang 1
Uri Schonfeld 1
Ivan Skuliber 1
Tomislav Stefanec 1
Yazhe Wang 1
Jamie Callan 1
Baihua Zheng 1
Krisztian Balog 1
Parantapa Bhattacharya 1
Guoli Li 1
Marco Montali 1
Wai Lam 1
Luis Leiva 1
Keith Bradley 1
Iris Miliaraki 1
Huijia Yu 1
Yangqiu Song 1
Sibel Adalı 1
You Wang 1
Guy Jourdan 1
Jose Pedro 1
Meenakshi Nagarajan 1
Li Ding 1
Junichi Tatemura 1
Lorrie Cranor 1
Saehoon Kim 1
Ke Wang 1
Chang Xu 1
Robert Stevens 1
Yue Zhang 1
Jinyong Jung 1

Affiliation Paper Counts
Universite Libre de Bruxelles 1
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Indian Institute of Engineering Science and Technology, Shibpur, Howrah, India 1
University of Michigan 1
Charles Sturt University 1
Guangdong Polytechnic Normal University 1
University of Buenos Aires 1
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Johns Hopkins University 1
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Koc University 1
University of Michigan-Dearborn 1
Chonnam National University 1
Jilin University 1
Hebrew University of Jerusalem 1
Vrije Universiteit Amsterdam 1
IBM Almaden Research Center 1
Wayne State University 1
Telefonica 1
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Central China Normal University 1
Kyoto University 1
South National University 1
Beihang University 1
Indian Institute of Science 1
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European Organization for Nuclear Research 1
Wroclaw University of Technology 1
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Oak Ridge National Laboratory 1
Hunan University of Science and Technology 1
University of Ferrara 1
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City University London 1
University of Edinburgh 1
Federal Technological University of Parana 1
Universite de Pau et des Pays de L'Adour 1
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IBM Zurich Research Laboratory 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
IBM Canada Ltd. 1
IBM Ireland Limited 1
Case Western Reserve University 2
University of Amsterdam 2
University of Ioannina 2
University of Bergamo 2
University of Lugano 2
Slovak University of Technology in Bratislava 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
Washington University in St. Louis 2
Universitat d'Alicante 2
IBM Research 2
University of Turin 2
Federal University of Bahia 2
Aristotle University of Thessaloniki 2
University of Southern California, Information Sciences Institute 2
Sandia National Laboratories, California 2
Nankai University 2
Vienna University of Technology 2
Wright State University 2
George Mason University 2
Simon Fraser University 2
University of Calabria 2
University of Twente 2
Kumamoto University 2
Indiana University 2
Sun Yat-Sen University 2
University of Quebec in Trois-Rivieres 2
New York University 2
Johannes Kepler University Linz 2
University of Delaware 2
University of North Texas 2
Missouri University of Science and Technology 2
University of Maryland, Baltimore County 2
Tata Research Development and Design Centre 2
Huazhong University of Science and Technology 2
University of Vienna 2
University of California, Los Angeles 2
Northeastern University 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
Pohang University of Science and Technology 2
Izmir University 2
Qatar Computing Research institute 2
CSIRO Data61 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
University of Insubria 3
Delft University of Technology 3
The University of Hong Kong 3
Northwestern University 3
University of Zagreb 3
Purdue University 3
France Telecom Division Recherche et Developpement 3
INRIA Rhone-Alpes 3
Universidad de Chile 3
AT&T Inc. 3
University of Brescia 3
NEC Laboratories America, Inc. 3
University of Science and Technology Beijing 3
Chinese Academy of Sciences 3
Monash University 3
Binghamton University State University of New York 3
Shanghai Jiaotong University 3
Rensselaer Polytechnic Institute 3
Universidad Autonoma de Madrid 3
University of Oxford 3
DePaul University 3
Swiss Federal Institute of Technology, Lausanne 3
University of Bologna 3
University of Hamburg 3
Technical University of Madrid 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
GESIS - Leibniz Institute for the Social Sciences 3
Zhejiang University 4
Korea University 4
Nanyang Technological University 4
Lulea University of Technology 4
University of California, Irvine 4
Poznan University of Technology 4
HP Labs 4
Universidad Politecnica de Valencia 4
University of Wurzburg 4
University of Massachusetts Amherst 4
Peking University 4
Graz University of Technology 4
Texas A and M University 4
Universidad de A Coruna 4
Stony Brook University 4
University of Ottawa, Canada 4
University of Southern California 4
Commonwealth Scientific and Industrial Research Organization 4
University of Calgary 5
Arizona State University 5
University of Groningen 5
Hasselt University 5
Ca' Foscari University of Venice 5
University of California, Davis 5
Singapore Management University 5
Macquarie University 5
Georgia Institute of Technology 5
Technical University of Munich 5
University of Maryland 5
Technion - Israel Institute of Technology 5
South China University of Technology 5
Virginia Tech 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
Italian National Research Council 6
University of Southampton 6
University of Milan 6
Bilkent University 7
IBM Thomas J. Watson Research Center 7
University of Science and Technology of China 7
Carnegie Mellon University 7
University of New South Wales 7
University of Athens 7
University of Alberta 7
Dalian University of Technology 8
Microsoft Corporation 8
University of Manchester 9
Hong Kong University of Science and Technology 9
Pennsylvania State University 10
University of Trento 10
University of Illinois at Chicago 10
Stanford University 11
Tsinghua University 11
Microsoft Research Asia 11
Yahoo Research Barcelona 12
Politecnico di Milano 15
Microsoft Research 17
University of California, Santa Barbara 17
Yahoo Research Labs 20
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