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A Bayesian Method for Comparing Hypotheses About Human Trails

When users interact with the Web today, they leave sequential digital trails on a massive scale. Examples of such human trails include Web navigation,... (more)

Multirelational Recommendation in Heterogeneous Networks

Recommender systems are key components in information-seeking contexts where personalization is sought. However, the dominant framework for... (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
Distance Measures for Detecting Geo-Social Similarity

This paper investigates the problem of geo-social similarity among users of online social networks, based on the locations of their activities (e.g., posting messages or photographs). Finding pairs of geo-socially similar users or detecting that two sets of locations (of activities) belong to the same user has important applications in privacy protection, recommendation systems, urban planning, public health, etc. It is explained and shown empirically that common distance measures between sets of locations are inadequate for determining geo-social similarity. Two novel distance measures between sets of locations are introduced. One is the mutually nearest distance that is based on computing a matching between two sets. The second measure uses a quad-tree index. It is highly scalable, but incurs the overhead of creating and maintaining the index. Algorithms with optimization techniques are developed for computing the two distance measures and also for finding the k-most similar users of a given one. Extensive experiments, using geo-tagged messages from Twitter, show that the new distance measures are both more accurate and more efficient than existing ones.

WISeR: A Multi-dimensional Framework for Searching and Ranking Web APIs

Mashups are agile applications that aggregate RESTful services, developed by third parties, whose functions are exposed as Web APIs within public repositories. Web API search may benefit from selection criteria that combine several dimensions used to describe the APIs (like categories, tags and technical features, e.g., protocols and data formats). Nevertheless, other dimensions might be fruitfully exploited to support Web API search. Among them, the collective knowledge, that is based on past experiences of other developers, may be used to suggest the right APIs for a target application. Past experiences might emerge from the co-occurrence of Web APIs in the same mashups and from ratings assigned by developers after using the Web APIs to create their own mashups or after using mashups developed by others. This paper aims at advancing the current state of the art in technologies for Web API search and ranking, by addressing two key issues: multi-dimensional modeling and multi-dimensional framework for selection. The model for Web API characterization embraces multiple descriptive dimensions, inspired by considering several public repositories, that focus on different and only partially overlapping dimensions. The proposed Web API selection framework, called WISeR (Web apI Search and Ranking), is based on search and ranking functions exploiting the multi-dimensional descriptions in order to optimise the identification of candidate Web APIs to be proposed, according to the given requirements. Furthermore, WISeR adapts to changes that occur during the Web API selection and mashup development, by revising the dimensional attributes in order to conform the developer's preferences and constraints. We also present an experimental evaluation of the framework.

Canonical Forms for Isomorphic and Equivalent RDF Graphs: Algorithms for Leaning and Labelling Blank Nodes

Existential blank nodes greatly complicate a number of fundamental operations on RDF graphs. In particular, the problems of determining if two RDF graphs have the same structure modulo blank node labels (i.e. if they are isomorphic), or determining if two RDF graphs have the same meaning under simple semantics (i.e., if they are simple-equivalent), have no known polynomial-time algorithms. In this paper, we propose methods that can produce two canonical forms of an RDF graph. The first canonical form preserves isomorphism such that any two isomorphic RDF graphs will produce the same canonical form; this iso-canonical form is produced by modifying the well-known canonical labelling algorithm Nauty for application to RDF graphs. The second canonical form additionally preserves simple-equivalence such that any two simple-equivalent RDF graphs will produce the same canonical form; this equi-canonical form is produced by, in a preliminary step, leaning the RDF graph, and then computing the iso-canonical form. These algorithms have a number of practical applications, such as for identifying isomorphic or equivalent RDF graphs in a large collection without requiring pair-wise comparison, for computing checksums or signing RDF graphs, for applying consistent Skolemisation schemes where blank nodes are mapped in a canonical manner to IRIs, and so forth. Likewise a variety of algorithms can be simplified by presupposing RDF graphs in one of these canonical forms. Although both algorithms require exponential steps in the worst case, in our evaluation we demonstrate that there indeed exist difficult synthetic cases, but we also provide results over 9.9 million RDF graphs which demonstrate that such cases occur infrequently in the real world, and that both canonical forms can be efficiently computed in all but a handful of such cases.

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 Tudou.com have provided the DanMu function. Unlike traditional online review such as reviews at Youtube.com 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.

Modeling and Evaluating a Robust Feedback-based Reputation System for E-commerce Platforms

Despite the steady growth of e-commerce communities in the past two decades, little has changed in the way these communities manage reputation for building trust and for protecting their members financial interests against fraud. As these communities mature and the defects of their reputation systems are revealed, further potential for deception against their members is created, that pushes the need for novel reputation mechanisms. Although a high volume of research works have explored the concepts of reputation and trust in e-communities, most of the proposed reputation systems target decentralized e-communities, focusing on issues related with the decentralized reputation management; they have not thus been integrated in e-commerce platforms. This works objective is to provide an attack resilient feedback-based reputation system for modern e-commerce platforms, while minimizing the incurred financial burden of potent security schemes. Initially, we discuss a series of attacks and issues in reputation systems and study the different approaches of these problems from related works, while also considering the structural properties, defense mechanisms and policies of existing platforms. Then we present our proposition for a robust reputation system which consists of a novel reputation metric and attack prevention mechanisms. Finally, we describe the simulation framework and tool that we have implemented for thoroughly testing and evaluating the metrics resilience against attacks and present the evaluation experiments and their results. We consider the presented simulation framework as the second contribution of our paper, aiming at facilitating the simulation and elaborative evaluation of reputation systems which specifically target e-commerce platforms.

Clickstream User Behavior Models

The next generation of Internet services is driven by users and user generated content. User behaviors are diverse and often unpredictable, making it more challenging than ever to secure online services. On one hand, existing services cannot prevent attackers from creating large numbers of fake user accounts (or Sybils), who generate massive amount of forged and malicious content such as fake online reviews, social spam, malware, and Sybil-based political lobbying efforts. On the other hand, abusive behaviors from real users (e.g., cyberbullying, trolling) are significantly threatening the well being of online communities. In this paper, we develop a novel framework for user behavior modeling based on clickstream traces, i.e., sequences of click events that users generate when using the online services. The core of our proposal is clickstream similarity graph, which uses similarity distance between pairs of clickstreams to capture user similarity. The result produces clusters that capture users with similar behavioral patterns. Based on this clickstream model, we develop two practical systems: The first system is a semi-supervised system to detect malicious user accounts (Sybils). We validate the system using ground-truth traces of 16,000 real and Sybil users from Renren, a large Chinese social network with 220M users. We demonstrate that our system achieves high detection accuracy with a minimal requirement of ground-truth inputs. The second system is an unsupervised system to capture more fine-grained user behavior. Instead of simply performing binary classification on users (either malicious or benign), this model identifies natural clusters of different user behaviors, and automatically extracts key features to interpret the captured behaviors. Applying this system to Renren and another real-world online social network Whisper (100K users), we help service providers to identify unexpected user behaviors (malicious accounts in Renren, hostile chatters in Whisper) and even predict users' future actions (dormant users in Whisper). Both systems have received positive feedback from our industrial collaborators including Renren, LinkedIn and Whisper, after testing our prototypes on their internal clickstream data. Following positive results, these companies have expressed strong interest in further experimentation and possible internal deployment.

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.

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.

Nucleus Decompositions for Identifying Hierarchy of Dense Subgraphs

Finding dense substructures in a graph is a fundamental graph mining operation, with applications in bioinformatics, social networks, and visualization to name a few. Yet most standard formulations of this problem (like clique, quasi-clique, k-densest subgraph) are NP-hard. Furthermore, the goal is rarely to find the true optimum, but to identify many (if not all) dense substructures, understand their distribution in the graph, and ideally determine relationships among them. Current dense subgraph finding algorithms usually optimize some objective, and only find a few such subgraphs without providing any structural relations. We define the nucleus decomposition of a graph, which represents the graph as a forest of nuclei. Each nucleus is a subgraph where smaller cliques are present in many larger cliques. The forest of nuclei is a hierarchy by containment, where the edge density increases as we proceed towards leaf nuclei. Sibling nuclei can have limited intersections, which enables discovering overlapping dense subgraphs. With the right parameters, the nucleus decomposition generalizes the classic notions of k-cores and k-truss decompositions. We give provably efficient algorithms for nucleus decompositions, and empirically evaluate their behavior in a variety of real graphs. The tree of nuclei consistently gives a global, hierarchical snapshot of dense substructures, and outputs dense subgraphs of higher quality than other state-of-the-art solutions. Our algorithm can process graphs with tens of millions of edges in less than an hour.

A Study of Web Print: What People Print in the Digital Era?

This paper focuses on analyzing and understanding what people print from the web (and why) using web print logs. Our web print log analysis is organized around three dimensions: print content (what people print), print intent (why people print) and print profiles (how user profiles look like). We use a set of measures for studying these dimensions: popularity, trends, user activity, user diversity, and consistency. Our study aims at providing insights into how printing is positioned and realized given the reality of the web. The paper analyzes a live, proprietary, real-world data feed containing anonymized URLs printed by users who consented to provide interaction data through a publicly distributed browser plug-in print application. We present a classification of pages printed based on their print intent and we describe our system for processing web print logs. We present several findings that reveal interesting insights into printing.

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.

Collusive Opinion Fraud Detection in Online Reviews: A Probabilistic Modeling Approach

We address the collusive opinion fraud problem in online review portals where groups of people work together to deliver deceptive positive or negative reviews with the goal of manipulating the reputations of targeted items. Such collusive fraud is considered much harder to defend against as these participants (or we call colluders) are capable of evading detection by shaping their behaviors collectively so as not to appear suspicious. To alleviate this problem, countermeasures have been proposed which proceed by leveraging the collective behaviors of colluders. The motivation stems from the observation that colluders' actions are very much synchronized since they are instructed by the same campaigns that specify common items to target and schedules to follow. However, the collective behaviors examined in existing solutions focus mostly on the external appearance of fraud campaigns, such as the campaign size and target set size, which can become ineffective once colluders change their behaviors collectively. Moreover, the detection algorithms used in existing approaches are designed to only make collusion inference on the input data; predictive models that can be deployed for emerging fraud practice detection cannot be learned from the data. In this article, to complement existing studies on collusive opinion fraud characterization and detection, we explore more subtle behavioral trails in collusive fraud practice. In particular, a suite of homogeneity-based measures are proposed to capture the interrelationships between colluders within campaigns. Moreover, a novel statistical model is proposed to further characterize, recognize, and predict collusive fraud in online reviews. The proposed model is fully unsupervised and highly flexible to incorporate effective measures available for better modeling and prediction. Through experiments on two real-world datasets, we show that our new method outperforms state-of-the-arts with respect to characterization and detection abilities.

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.

Bibliometrics

Publication Years 2007-2017
Publication Count 214
Citation Count 2169
Available for Download 214
Downloads (6 weeks) 1727
Downloads (12 Months) 15893
Downloads (cumulative) 177500
Average downloads per article 829
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)
Chen-Nee Chuah ACM Distinguished Member (2012)
ACM Senior Member (2006)
Lorrie Faith Cranor ACM Senior Member (2006)
Ernesto Damiani ACM Distinguished Member (2008)
Schahram Dustdar ACM Distinguished Member (2009)
Elena Ferrari ACM Distinguished Member (2011)
Simon Harper ACM Distinguished Member (2014)
ACM Senior 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)
Ingmar Weber ACM Senior Member (2017)
Xing Xie ACM Senior Member (2010)
Qiang Yang ACM Distinguished Member (2011)
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
Wolfgang Nejdl 4
Xing Xie 4
Weiying Ma 4
Berkant Cambazoglu 4
Ingmar Weber 4
Ling Liu 3
Weiyi Meng 3
Özgür Ulusoy 3
Fabio Casati 3
Ricardo Baeza-Yates 3
Anirban Mahanti 3
Rifat Ozcan 3
Piero Fraternali 2
Barry Smyth 2
Qiong Luo 2
Andrei Broder 2
Ziv Bar-Yossef 2
Monika Henzinger 2
Xiangye Xiao 2
Bernard Jansen 2
Niklas Carlsson 2
Florian Daniel 2
Freddy Lécué 2
Yu Zheng 2
Marco Brambilla 2
Sergiu Chelaru 2
Carey Williamson 2
Mudhakar Srivatsa 2
Cristóbal Arellano 2
Prashant Shenoy 2
Clyde Giles 2
Philip YU 2
Boualem Benatallah 2
Eric Horvitz 2
Alessandro Bozzon 2
Marco Aiello 2
Phillipa Gill 2
Eepeng Lim 2
Christo Wilson 2
Ben Zhao 2
Clement Yu 2
Markus Strohmaier 2
Enhong Chen 2
Stefan Siersdorfer 2
Eda Baykan 2
Óscar Díaz 2
Cornelia Caragea 2
James Miller 2
Jesus Bellido 1
Federica Paci 1
Mourad Ouzzani 1
Boi Faltings 1
Vicente Pelechano 1
Chris Grier 1
Shuo Tang 1
Emi Garcia-Palacios 1
Gang Wang 1
Bolun Wang 1
Haitao Zheng 1
Sergio Rojas-Galeano 1
Mohammad Rahman 1
B Prakash 1
Simon Gottschalk 1
Jagdish Achara 1
Dimitris Zeginis 1
Rahul Balakavi 1
Aojan Su 1
Dan Hong 1
Hongbo Fu 1
Ivan Budiselić 1
Joonwon Lee 1
Jeaho Hwang 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
James Thom 1
Seunghwan Ryu 1
Michael Schäfer 1
Mike Spreitzer 1
Bernardo Huberman 1
Flavio Junqueira 1
Georgia Koutrika 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
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
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
Daiping Liu 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
Paola Mello 1
Sergio Storari 1
Zhen Liao 1
Silvia Quarteroni 1
Marian Dörk 1
Davide Mazza 1
Ophir Frieder 1
Liyun Ru 1
Diego Fernández 1
Paul Thomas 1
Ahmed Hassan 1
Jiawei Han 1
Arie Van Deursen 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
Suzanne Embury 1
Lada Adamic 1
Vanessa Murdock 1
Zoltán Gyöngyi 1
Einat Amitay 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
Jing Wang 1
Stephen Hardiman 1
Prasenjit Mitra 1
Opher Dubrovsky 1
Filippo Geraci 1
Ian Reay 1
Scott Dick 1
Amruta Joshi 1
Natalia Kwasnikowska 1
Changai Sun 1
Jennifer Golbeck 1
Bharath Mohan 1
Prabhakar Raghavan 1
Alex Rogers 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
Divya Sambasivan 1
Lei Li 1
Javier Parra-Arnau 1
Silvia Uribe 1
Yongjae Lee 1
Bojana Bislimovska 1
Andrea Pugliese 1
Iván Cantador 1
Adam Barth 1
Andrew Bortz 1
Chengkok Koh 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
Yi Qian 1
Stergios Anastasiadis 1
Stefano Soi 1
Martin Wimmer 1
Debora Donato 1
Sven Casteleyn 1
Carlos Castillo 1
Anupam Joshi 1
Harald Weinreich 1
Matthias Mayer 1
Seungwon Hwang 1
Yuxiong He 1
Seungjin Choi 1
Kaweh Naini 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
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
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
Haining Wang 1
Angelos Stavrou 1
Alexey Drutsa 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
Nikolay Mehandjiev 1
Bruno Crispo 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
Thomas Johnston 1
Ali Mesbah 1
Xianchao Zhang 1
Mustafa Dincturk 1
Stefan Krompass 1
Irene Garrig'os 1
Emmanuel Chauveau 1
Belle Tseng 1
Giovanni Grasso 1
Christian Schallhart 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
Denis Helic 1
Wenbin Cai 1
Muhan Zhang 1
Sharon Goldberg 1
Mingdong Tang 1
Ana Dias 1
Neil Yorke-Smith 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
Aphrodite Tsalgatidou 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
Christos Faloutsos 1
Haitao Xu 1
Elena Demidova 1
Partha Mukherjee 1
José Menéndez 1
Pavel Serdyukov 1
Djoerd Hiemstra 1
Kyungbaek Kim 1
Jianwei Gan 1
Idit Keidar 1
Aleksandar Kuzmanovic 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
Sameh Elnikety 1
Michael Huemer 1
Khalid Belhajjame 1
Norman Paton 1
Régis Saint-Paul 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
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
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
Peter Dolog 1
Mingfang Wu 1
Yasushi Sakurai 1
Aruna Seneviratne 1
Sergio Duarte Torres 1
VS Subrahmanian 1
Alejandro Bellogín 1
Collin Jackson 1
Ying Hu 1
Goran Delac 1
Krishna Gummadi 1
Andrea Casini 1
Cam Nguyen 1
Takeshi Tokuyama 1
Liwei Liu 1
Hans Jacobsen 1
Huanhuan Cao 1
Jongwoo Ha 1
Filipe Mesquita 1
Fei Chen 1
Víctor Carneiro 1
Li Zhang 1
Tim Weninger 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
Islam Elgedawy 1
Zahir Tari 1
Bhuvan Urgaonkar 1
Jure Leskovec 1
Aristides Gionis 1
Vassilis Plachouras 1
Michał Kąkol 1
Nishida Toyoaki 1
Jianke Zhu 1
Maciej Drozdowski 1
Rik Eshuis 1
Andreas Hotho 1
Michael Paul 1
Guibing Guo 1
Wensheng Wu 1
Kenneth Fletcher 1
Haibin Zhang 1
Liran Katzir 1
Willianmassami Watanabe 1
Jie Zhang 1
Stefano Paraboschi 1
Sangameshwar Patil 1
Harrick Vin 1
Wouter Gelade 1
Ashwin Swaminathan 1
Aameek Singh 1
Thomi Pilioura 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
Tianyi Wang 1
Zengbin Zhang 1
Federico Álvarez 1
Suranga Seneviratne 1
Chennee Chuah 1
Kristina Lerman 1
Mor Naaman 1
Weizhong Shao 1
Ben Zhao 1
Myeongjae Jeon 1
Wouter Weerkamp 1
Maarten De Rijke 1
Matteo Picozzi 1
Yang Zhou 1
Muhammad Zafar 1
Saptarshi Ghosh 1
Simon Jonassen 1
Valeria Fionda 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
Michael Schrefl 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
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
Valentin Robu 1
Harry Halpin 1
Marián Boguñá 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
Andrew Tappenden 1
Rattapoom Tuchinda 1
Pedro Szekely 1
Yannis Tzitzikas 1
Radu Jurca 1
Andrew Turpin 1
Xing Li 1
Yasuko Matsubara 1
Claude Castelluccia 1
Athena Vakali 1
Jinyoung Han 1
Gleb Gusev 1
Anna Squicciarini 1
Michael Ovelgönne 1
Xiaowei Yang 1
Uri Schonfeld 1
Ivan Skuliber 1
Tomislav Stefanec 1
Krisztian Balog 1
Parantapa Bhattacharya 1
Yazhe Wang 1
Jamie Callan 1
Baihua Zheng 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
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
Robert Stevens 1
Yue Zhang 1
Keith Bradley 1
Dominik Deja 1
Sukru Eraslan 1
Barbara Carminati 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
Jing Zhao 1
Manishkumar Jha 1
Barbara Poblete 1
Zhisheng Li 1
Mikhail Bilenko 1

Affiliation Paper Counts
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
GESIS - Leibniz Institute for the Social Sciences 1
IBM Canada Ltd. 1
IBM Ireland Limited 1
Universite Libre de Bruxelles 1
University of Padua 1
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
Universitat de Barcelona 1
Universidad Jaume I 1
Bar-Ilan University 1
Hefei University of Technology 1
Illinois Institute of Technology 1
Princeton University 1
IBM India Research Laboratory 1
University of Sannio 1
Indian Institute of Technology, Kharagpur 1
Johns Hopkins University 1
Rice University 1
University of Minnesota System 1
Koc University 1
University of Michigan-Dearborn 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
University of Waterloo 1
Hong Kong Baptist University 1
Federal University of Uberlandia 1
University of Saskatchewan 1
University of Washington, Seattle 1
The College of William and Mary 1
Yonsei University 1
Kassel University 1
University of Magdeburg 1
Fujian Normal University 1
San Diego State University 1
Boston University 1
European Organization for Nuclear Research 1
Wroclaw University of Technology 1
Seoul Women's University 1
Aalborg University 1
University of Connecticut 1
Georgetown University 1
Oak Ridge National Laboratory 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
Temple University 1
University of Cambridge 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
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
HP Labs 2
Universitat d'Alicante 2
IBM Research 2
University of Turin 2
Universidad de Chile 2
Aristotle University of Thessaloniki 2
University of Southern California, Information Sciences Institute 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
Italian National Research Council 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
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
University of Wurzburg 3
AT&T Inc. 3
NEC Laboratories America, Inc. 3
University of Science and Technology Beijing 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
Rensselaer Polytechnic Institute 3
Universidad Autonoma de Madrid 3
University of Oxford 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
Zhejiang University 4
Korea University 4
Lulea University of Technology 4
University of California, Irvine 4
Poznan University of Technology 4
Universidad Politecnica de Valencia 4
University of Massachusetts Amherst 4
Peking 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
Virginia Tech 4
University of Ottawa, Canada 4
University of Athens 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
Technical University of Munich 5
University of Maryland 5
South China University of Technology 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
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 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
University of California, Santa Barbara 13
Politecnico di Milano 15
Microsoft Research 17
Yahoo Research Labs 20
 
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