Paper Digest: WWW 2015 Highlights

May 12, 2015June 27, 2020 admin

The Web Conference (WWW) is one of the top internet conferences in the world.

To help the community quickly catch up on the work presented in this conference, Paper Digest Team processed all accepted papers, and generated one highlight sentence (typically the main topic) for each paper. Readers are encouraged to read these machine generated highlights / summaries to quickly get the main idea of each paper.

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Paper Digest Team
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TABLE 1: WWW 2015 Papers

	Title	Authors	Highlight
1	Optimizing Display Advertising in Online Social Networks	Zeinab Abbassi, Aditya Bhaskara, Vishal Misra	In this work, we propose formal probabilistic models to capture this phenomenon, and study the algorithmic problem that then arises.
2	Frankenplace: Interactive Thematic Mapping for Ad Hoc Exploratory Search	Benjamin Adams, Grant McKenzie, Mark Gahegan	In this paper we describe the architecture of an interactive thematic map search engine, Frankenplace, designed to facilitate document exploration at the intersection of theme and place.
3	Towards Reconciling SPARQL and Certain Answers	Shqiponja Ahmetaj, Wolfgang Fischl, Reinhard Pichler, Mantas Šimkus, Sebastian Skritek	For OWL 2 QL entailment, we present algorithms for the evaluation of an interesting fragment of SPARQL (the so-called well-designed SPARQL).
4	Donor Retention in Online Crowdfunding Communities: A Case Study of DonorsChoose.org	Tim Althoff, Jure Leskovec	We present a large-scale study of millions of donors and donations on DonorsChoose.org, a crowdfunding platform for education projects.
5	Budget-Constrained Item Cold-Start Handling in Collaborative Filtering Recommenders via Optimal Design	Oren Anava, Shahar Golan, Nadav Golbandi, Zohar Karnin, Ronny Lempel, Oleg Rokhlenko, Oren Somekh	We formalize this problem as an optimization problem: given a new item, a pool of available users, and a budget constraint, select which users to assign with the task of rating the new item in order to minimize the prediction error of our model.
6	Improved Theoretical and Practical Guarantees for Chromatic Correlation Clustering	Yael Anava, Noa Avigdor-Elgrabli, Iftah Gamzu	Our main contribution is a fast and easy-to-implement constant approximation framework for the problem, which builds on a novel reduction of the problem to that of correlation clustering.
7	Global Diffusion via Cascading Invitations: Structure, Growth, and Homophily	Ashton Anderson, Daniel Huttenlocher, Jon Kleinberg, Jure Leskovec, Mitul Tiwari	In this paper, we study the diffusion of LinkedIn, an online professional network comprising over 332 million members, a large fraction of whom joined the site as part of a signup cascade.
8	Recommendation Subgraphs for Web Discovery	Arda Antikacioglu, R. Ravi, Srinath Sridhar	We formalize the concept of recommendations used for discovery as a natural graph optimization problem on a bipartite graph and propose three methods for solving the problem in increasing order of sophistication: a local random sampling algorithm, a greedy algorithm and a more involved partitioning based algorithm.
9	Is Sniping A Problem For Online Auction Markets?	Matt Backus, Thomas Blake, Dimitriy V. Masterov, Steven Tadelis	We show the effect to be causal using a carefully selected subset of auctions from eBay.com and instrumental variables estimation strategy.
10	Essential Web Pages Are Easy to Find	Ricardo Baeza-Yates, Paolo Boldi, Flavio Chierichetti	In this paper we address the problem of estimating the index size needed by web search engines to answer as many queries as possible by exploiting the marked difference between query and click frequencies.
11	Design and Analysis of Benchmarking Experiments for Distributed Internet Services	Eytan Bakshy, Eitan Frachtenberg	We develop statistical models of distributed Internet service performance based on data from Perflab, a production system used at Facebook which vets thousands of changes to the company’s codebase each day.
12	ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly	Alex Beutel, Amr Ahmed, Alexander J. Smola	Instead of using low-rank factorization we take a drastically different approach, based on the simple insight that an additive model of co-clusterings allows one to approximate matrices efficiently.
13	Who, What, When, and Where: Multi-Dimensional Collaborative Recommendations Using Tensor Factorization on Sparse User-Generated Data	Preeti Bhargava, Thomas Phan, Jiayu Zhou, Juhan Lee	In this paper, we present a system and an approach for performing multi-dimensional collaborative recommendations for Who (User), What (Activity), When (Time) and Where (Location), using tensor factorization on sparse user-generated data.
14	Secrets, Lies, and Account Recovery: Lessons from the Use of Personal Knowledge Questions at Google	Joseph Bonneau, Elie Bursztein, Ilan Caron, Rob Jackson, Mike Williamson	We examine the first large real-world data set on personal knowledge question’s security and memorability from their deployment at Google.
15	Supporting Ethical Web Research: A New Research Ethics Review	Anne Bowser, Janice Y. Tsai	We describe the creation of a new ethics framework and a research ethics submission system (RESS) within Microsoft Research (MSR).
16	Sequential Hypothesis Tests for Adaptive Locality Sensitive Hashing	Aniket Chakrabarti, Srinivasan Parthasarathy	In this work we revisit the LSH problem from a Frequentist setting and formulate sequential tests for composite hypothesis (similarity greater than or less than threshold) that can be leveraged by such LSH algorithms for adaptively pruning candidates aggressively.
17	Opinion Spam Detection in Web Forum: A Real Case Study	Yu-Ren Chen, Hsin-Hsi Chen	In this paper, we conduct a real case study based on a set of internal records of opinion spams leaked from a shady marketing campaign.
18	Summarizing Entity Descriptions for Effective and Efficient Human-centered Entity Linking	Gong Cheng, Danyun Xu, Yuzhong Qu	To avoid overloading human users with too much information and help them more efficiently choose an entity from candidates, we aim to substitute entire entity descriptions with compact, equally effective structured summaries that are automatically generated.
19	Semantic Tagging of Mathematical Expressions	Pao-Yu Chien, Pu-Jen Cheng	In this work, we propose a novel STME approach that relies on neither text along with expressions, nor labelled training data. To evaluate our system, we build large-scale training and test datasets automatically from a public math forum.
20	Collaborative Ranking with a Push at the Top	Konstantina Christakopoulou, Arindam Banerjee	We consider three specific formulations, based on collaborative p-norm push, infinite push, and reverse-height push, and propose efficient optimization methods for learning these models.
21	Parallel Streaming Signature EM-tree: A Clustering Algorithm for Web Scale Applications	Christopher Michael De Vries, Lance De Vine, Shlomo Geva, Richi Nayak	We introduce a scalable algorithm that clusters hundreds of millions of web pages into hundreds of thousands of clusters.
22	Network-based Origin Confusion Attacks against HTTPS Virtual Hosting	Antoine Delignat-Lavaud, Karthikeyan Bhargavan	We present evidence that such vulnerable virtual host configurations are widespread, even on the most popular and security-scrutinized websites, thus allowing a network adversary to hijack pages, or steal secure cookies and single sign-on tokens.
23	The Dynamics of Micro-Task Crowdsourcing: The Case of Amazon MTurk	Djellel Eddine Difallah, Michele Catasta, Gianluca Demartini, Panagiotis G. Ipeirotis, Philippe Cudré-Mauroux	In this paper, we adopt a data-driven approach to (A) perform a long-term analysis of a popular micro-task crowdsourcing platform and understand the evolution of its main actors (workers, requesters, and platform).
24	Hierarchical Neural Language Models for Joint Representation of Streaming Documents and their Content	Nemanja Djuric, Hao Wu, Vladan Radosavljevic, Mihajlo Grbovic, Narayan Bhamidipati	We consider the problem of learning distributed representations for documents in data streams.
25	Future User Engagement Prediction and Its Application to Improve the Sensitivity of Online Experiments	Alexey Drutsa, Gleb Gusev, Pavel Serdyukov	We propose a novel approach to improve the sensitivity of user engagement metrics (that are widely used in A/B tests) by utilizing prediction of the future behavior of an individual user.
26	Enriching Structured Knowledge with Open Information	Arnab Dutta, Christian Meilicke, Heiner Stuckenschmidt	We propose an approach for semantifying web extracted facts.
27	A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems	Ali Mamdouh Elkahky, Yang Song, Xiaodong He	In this work, we propose a content-based recommendation system to address both the recommendation quality and the system scalability.
28	Cookies That Give You Away: The Surveillance Implications of Web Tracking	Steven Englehardt, Dillon Reisman, Christian Eubank, Peter Zimmerman, Jonathan Mayer, Arvind Narayanan, Edward W. Felten	To evaluate the effectiveness of our attack, we introduce a methodology that combines web measurement and network measurement.
29	Efficient Densest Subgraph Computation in Evolving Graphs	Alessandro Epasto, Silvio Lattanzi, Mauro Sozio	We study the densest subgraph problem in the the dynamic graph model, for which we present the first scalable algorithm with provable guarantees.
30	A Practical Framework for Privacy-Preserving Data Analytics	Liyue Fan, Hongxia Jin	In this paper, we propose a practical framework for data analytics, while providing differential privacy guarantees to individual data contributors.
31	Compressed Indexes for String Searching in Labeled Graphs	Paolo Ferragina, Francesco Piccinno, Rossano Venturini	This paper takes inspiration from the Facebook Unicorn’s platform and proposes some compressed-indexing schemes for large graphs whose nodes are labeled with strings of variable length – i.e., node’s attributes such as user’s (nick-)name – that support sophisticated search operations which involve both the linked structure of the graph and the string content of its nodes.
32	Improving Paid Microtasks through Gamification and Adaptive Furtherance Incentives	Oluwaseyi Feyisetan, Elena Simperl, Max Van Kleek, Nigel Shadbolt	Following these initial insights, we define a predictive model for estimating the most appropriate incentives for individual workers, based on their previous contributions.
33	Tagging Personal Photos with Transfer Deep Learning	Jianlong Fu, Tao Mei, Kuiyuan Yang, Hanqing Lu, Yong Rui	To deal with these challenges, in this paper, we present a novel transfer deep learning approach to tag personal photos.
34	MobInsight: On Improving The Performance of Mobile Apps in Cellular Networks	Vijay Gabale, Dilip Krishnaswamy	In this work, we perform a systematic measurement study of more than 50 popular apps and 2 cellular networks, and discover that while cellular networks have predictable latency, it is the path between exit points of cellular networks (e.g., GGSN) and cloud-servers that degrades apps performance.
35	Rethinking Security of Web-Based System Applications	Martin Georgiev, Suman Jana, Vitaly Shmatikov	We show that the access-control models of these platforms are (a) incompatible and (b) prone to unintended delegation of native-access rights: when applications request native access for their own code, they unintentionally enable it for untrusted third-party code, too.
36	Cardinal Contests	Arpita Ghosh, Patrick Hummel	We model and analyze cardinal contests, where a principal running a rank-order tournament has access to an absolute measure of the qualities of agents’ submissions in addition to their relative rankings, and ask how modifying the rank-order tournament to incorporate cardinal information can improve incentives for effort.
37	Accessible On-Line Floor Plans	Cagatay Goncu, Anuradha Madugalla, Simone Marinai, Kim Marriott	We present a new model for accessible presentation of on-line information graphics and demonstrate its use for presenting floor plans.
38	Network A/B Testing: From Sampling to Estimation	Huan Gui, Ya Xu, Anmol Bhasin, Jiawei Han	In this paper, we study the problem of network A/B testing in real networks, which have substantially different characteristics from the simulated random networks studied in previous works.
39	User Session Identification Based on Strong Regularities in Inter-activity Time	Aaron Halfaker, Os Keyes, Daniel Kluver, Jacob Thebault-Spieker, Tien Nguyen, Kenneth Shores, Anuradha Uduwage, Morten Warncke-Wang	In this work, we demonstrate a strong regularity in the temporal rhythms of user initiated events across several different domains of online activity (incl.
40	Incentivizing High Quality Crowdwork	Chien-Ju Ho, Aleksandrs Slivkins, Siddharth Suri, Jennifer Wortman Vaughan	We study the causal effects of financial incentives on the quality of crowdwork.
41	Skolemising Blank Nodes while Preserving Isomorphism	Aidan Hogan	In this paper, we propose and evaluate a scheme to produce canonical labels for blank nodes in RDF graphs.
42	Scalable Methods for Adaptively Seeding a Social Network	Thibaut Horel, Yaron Singer	In particular, we develop algorithms for linear influence models with provable approximation guarantees that can be gracefully parallelized. To show the effectiveness of our methods we collected data from various verticals social network users follow.
43	User Review Sites as a Resource for Large-Scale Sociolinguistic Studies	Dirk Hovy, Anders Johannsen, Anders Søgaard	Our research aims to remedy both problems by exploring a large new data source, international review websites with user profiles.
44	When Does Improved Targeting Increase Revenue?	Patrick Hummel, R. Preston McAfee	In second price auctions with symmetric bidders, we find that improved targeting via enhanced information disclosure decreases revenue when there are two bidders and increases revenue if there are at least four bidders.
45	Social Status and Badge Design	Nicole Immorlica, Greg Stoddard, Vasilis Syrgkanis	In this paper, we study how to design virtual incentive mechanisms that maximize total contributions to a website when users are motivated by social status.
46	Mapping Temporal Horizons: Analysis of Collective Future and Past related Attention in Twitter	Adam Jatowt, Émilien Antoine, Yukiko Kawai, Toyokazu Akiyama	In this work we investigate how microblogging users collectively refer to time.
47	Path Sampling: A Fast and Provable Method for Estimating 4-Vertex Subgraph Counts	Madhav Jha, C. Seshadhri, Ali Pinar	We provide a sampling algorithm that provably and accurately approximates the frequencies of all 4-vertex pattern subgraphs.
48	Automatic Online Evaluation of Intelligent Assistants	Jiepu Jiang, Ahmed Hassan Awadallah, Rosie Jones, Umut Ozertem, Imed Zitouni, Ranjitha Gurunath Kulkarni, Omar Zia Khan	We develop consistent and automatic approaches that can evaluate different tasks in voice-activated intelligent assistants.
49	Incorporating Social Context and Domain Knowledge for Entity Recognition	Jie Tang, Zhanpeng Fang, Jimeng Sun	In this paper, we propose the SOCINST model to formalize the problem into a probabilistic model.
50	Querying Web-Scale Information Networks Through Bounding Matching Scores	Jiahui Jin, Samamon Khemmarat, Lixin Gao, Junzhou Luo	In this paper, we propose an efficient algorithm for finding the best k answers for a given query without precomputing graph indices.
51	LN-Annote: An Alternative Approach to Information Extraction from Emails using Locally-Customized Named-Entity Recognition	YoungHoon Jung, Karl Stratos, Luca P. Carloni	To address these problems, we propose LN-Annote, a new method to extract personal information from the email that is locally available on mobile devices (without remote access to the cloud). We present an extensive set of experiment results: beside proving the feasibility of our approach, they demonstrate its efficiency in terms of the named-entity extraction performance as well as the execution speed and the energy consumption spent in mobile devices.
52	Describing and Understanding Neighborhood Characteristics through Online Social Media	Mohamed Kafsi, Henriette Cramer, Bart Thomee, David A. Shamma	To surface the content that is characteristic for a region, we present the geographical hierarchy model (GHM), a probabilistic model based on the assumption that data observed in a region is a random mixture of content that pertains to different levels of a hierarchy.
53	Active Learning for Multi-relational Data Construction	Hiroshi Kajino, Akihiro Kishimoto, Adi Botea, Elizabeth Daly, Spyros Kotoulas	In this paper, we formalize the problem of dataset construction as active learning problems and present the Active Multi-relational Data Construction (AMDC) method.
54	The Social World of Content Abusers in Community Question Answering	Imrul Kayes, Nicolas Kourtellis, Daniele Quercia, Adriana Iamnitchi, Francesco Bonchi	Based on our empirical observations, we build a classifier that is able to detect abusive users with an accuracy as high as 83%.
55	The Lifecycles of Apps in a Social Ecosystem	Isabel Kloumann, Lada Adamic, Jon Kleinberg, Shaomei Wu	In this work we address this challenge through an analysis of the collection of apps on Facebook Login, developing a novel framework for analyzing both temporal and social properties.
56	Getting More for Less: Optimized Crowdsourcing with Dynamic Tasks and Goals	Ari Kobren, Chun How Tan, Panagiotis Ipeirotis, Evgeniy Gabrilovich	We directly address this problem by presenting techniques that optimize the crowdsourcing process by jointly maximizing the user longevity in the system and the true value that the system derives from user participation.
57	Evolution of Conversations in the Age of Email Overload	Farshad Kooti, Luca Maria Aiello, Mihajlo Grbovic, Kristina Lerman, Amin Mantrach	In this paper, we report results of a large-scale study of more than 2 million users exchanging 16 billion emails over several months.
58	Events and Controversies: Influences of a Shocking News Event on Information Seeking	Danai Koutra, Paul N. Bennett, Eric Horvitz	We seek to identify and study information-seeking behavior and access to alternative versus reinforcing viewpoints following shocking, emotional, and large-scale news events.
59	Statistically Significant Detection of Linguistic Change	Vivek Kulkarni, Rami Al-Rfou, Bryan Perozzi, Steven Skiena	We propose a new computational approach for tracking and detecting statistically significant linguistic shifts in the meaning and usage of words.
60	Replacing the Irreplaceable: Fast Algorithms for Team Member Recommendation	Liangyue Li, Hanghang Tong, Nan Cao, Kate Ehrlich, Yu-Ru Lin, Norbou Buchler	In this paper, we study the problem of TEAM MEMBER REPLACEMENT — given a team of people embedded in a social network working on the same task, find a good candidate to best replace a team member who becomes unavailable to perform the task for certain reason (e.g., conflicts of interests or resource capacity).
61	Robust Group Linkage	Pei Li, Xin Luna Dong, Songtao Guo, Andrea Maurino, Divesh Srivastava	We present a robust two-stage algorithm: the first stage identifies pivots–maximal sets of records that are very likely to belong to the same group, while being robust to possible erroneous values; the second stage collects strong evidence from the pivots and leverages it for merging more records into the same group, while being tolerant to differences in local values of an attribute.
62	Uncovering the Small Community Structure in Large Networks: A Local Spectral Approach	Yixuan Li, Kun He, David Bindel, John E. Hopcroft	In this paper, we propose a novel approach for finding overlapping communities called LEMON (Local Expansion via Minimum One Norm).
63	Scalable Parallel EM Algorithms for Latent Dirichlet Allocation in Multi-Core Systems	Xiaosheng Liu, Jia Zeng, Xi Yang, Jianfeng Yan, Qiang Yang	To handle web-scale content analysis on just a single PC, we propose multi-core parallel expectation-maximization (PEM) algorithms to infer and estimate LDA parameters in shared memory systems.
64	Grading the Graders: Motivating Peer Graders in a MOOC	Yanxin Lu, Joe Warren, Christopher Jermaine, Swarat Chaudhuri, Scott Rixner	In this paper, we detail our efforts at creating and running a controlled study designed to examine how students in a MOOC might be motivated to do a better job during peer grading.
65	Measurement and Analysis of Mobile Web Cache Performance	Yun Ma, Xuanzhe Liu, Shuhui Zhang, Ruirui Xiang, Yunxin Liu, Tao Xie	To address these issues, in this paper, we present a proactive approach for a comprehensive measurement study on mobile Web cache performance.
66	SCULPT: A Schema Language for Tabular Data on the Web	Wim Martens, Frank Neven, Stijn Vansummeren	We present a formal model for SCULPT and obtain a linear time combined complexity evaluation algorithm.
67	The Web as a Jungle: Non-Linear Dynamical Systems for Co-evolving Online Activities	Yasuko Matsubara, Yasushi Sakurai, Christos Faloutsos	We present ECOWEB, (i.e., Ecosystem on the Web), which is an intuitive model designed as a non-linear dynamical system for mining large-scale co-evolving online activities.
68	Spanning Edge Centrality: Large-scale Computation and Applications	Charalampos Mavroforakis, Richard Garcia-Lebron, Ioannis Koutis, Evimaria Terzi	In this article we bring theory into practice, with careful and optimized implementations that allow the fast computation of spanning centrality in very large graphs with millions of nodes.
69	No Escape From Reality: Security and Privacy of Augmented Reality Browsers	Richard McPherson, Suman Jana, Vitaly Shmatikov	We start by analyzing the functional requirements that AR browsers must support in order to present AR content.
70	Discovering Meta-Paths in Large Heterogeneous Information Networks	Changping Meng, Reynold Cheng, Silviu Maniu, Pierre Senellart, Wangda Zhang	Since this problem is computationally intractable, we propose a greedy algorithm to select the most relevant meta-paths.
71	From "Selena Gomez" to "Marlon Brando": Understanding Explorative Entity Search	Iris Miliaraki, Roi Blanco, Mounia Lalmas	In this paper, we perform a large-scale analysis into how users interact with the entity results returned by Spark. Based on this analysis, we develop a set of query and user-based features that reflect the click behavior of users and explore their effectiveness in the context of a prediction task.
72	Children Seen But Not Heard: When Parents Compromise Children’s Online Privacy	Tehila Minkus, Kelvin Liu, Keith W. Ross	In this paper, we conduct a study to see how widespread these behaviors are among adults on Facebook and Instagram.
73	TrueView: Harnessing the Power of Multiple Review Sites	Amanda J. Minnich, Nikan Chavoshi, Abdullah Mueen, Shuang Luan, Michalis Faloutsos	Our work is an early effort that explores the advantages and the challenges in using multiple reviewing sites towards more informed decision making.
74	QUOTUS: The Structure of Political Media Coverage as Revealed by Quoting Patterns	Vlad Niculae, Caroline Suen, Justine Zhang, Cristian Danescu-Niculescu-Mizil, Jure Leskovec	In this paper we propose a framework based on quoting patterns for quantifying and characterizing the degree to which media outlets exhibit systematic bias.
75	Energy and Performance of Smartphone Radio Bundling in Outdoor Environments	Ana Nika, Yibo Zhu, Ning Ding, Abhilash Jindal, Y. Charlie Hu, Xia Zhou, Ben Y. Zhao, Haitao Zheng	In this study, we seek to answer these questions using extensive measurements to empirically characterize both energy and performance for radio bundling approaches.
76	PriVaricator: Deceiving Fingerprinters with Little White Lies	Nick Nikiforakis, Wouter Joosen, Benjamin Livshits	In this paper we propose PriVaricator, a solution to the problem of browser-based fingerprinting.
77	Diagnoses, Decisions, and Outcomes: Web Search as Decision Support for Cancer	Michael J. Paul, Ryen W. White, Eric Horvitz	Using this corpus, we present a variety of analyses toward the goal of understanding search and decision making about treatments.
78	PocketTrend: Timely Identification and Delivery of Trending Search Content to Mobile Users	Gennady Pekhimenko, Dimitrios Lymberopoulos, Oriana Riva, Karin Strauss, Doug Burger	To understand how trending search topics are formed and evolve over time, we analyze 21 million queries submitted during periods where popular events caused search query volume spikes.
79	Overcoming Relational Learning Biases to Accurately Predict Preferences in Large Scale Networks	Joseph J. Pfeiffer, Jennifer Neville, Paul N. Bennett	In this work, we address each of these limitations.
80	Deriving an Emergent Relational Schema from RDF Data	Minh-Duc Pham, Linnea Passing, Orri Erling, Peter Boncz	We motivate and describe techniques that allow to detect an "emergent" relational schema from RDF data.
81	The Digital Life of Walkable Streets	Daniele Quercia, Luca Maria Aiello, Rossano Schifanella, Adam Davies	To partly automate the computation of those scores, we explore the possibility of using the social media data of Flickr and Foursquare to automatically identify safe and walkable streets.
82	Beyond Models: Forecasting Complex Network Processes Directly from Data	Bruno Ribeiro, Minh X. Hoang, Ambuj K. Singh	In this work we show that model-free forecasting is possible.
83	Weakly Supervised Extraction of Computer Security Events from Twitter	Alan Ritter, Evan Wright, William Casey, Tom Mitchell	We therefore propose a weakly supervised approach, in which extractors for new categories of events are easy to define and train, by specifying a small number of seed examples.
84	Groupsourcing: Team Competition Designs for Crowdsourcing	Markus Rokicki, Sergej Zerr, Stefan Siersdorfer	In this paper, we investigate how team mechanisms can be leveraged to further improve the cost efficiency of crowdsourcing competitions.
85	Authentication Melee: A Usability Analysis of Seven Web Authentication Systems	Scott Ruoti, Brent Roberts, Kent Seamons	We report the results of four within-subjects usability studies for seven web authentication systems.
86	Finding the Hierarchy of Dense Subgraphs using Nucleus Decompositions	Ahmet Erdem Sariyuce, C. Seshadhri, Ali Pinar, Umit V. Catalyurek	We give provably efficient algorithms for nucleus decompositions, and empirically evaluate their behavior in a variety of real graphs.
87	Bringing CUPID Indoor Positioning System to Practice	Souvik Sen, Dongho Kim, Stephane Laroche, Kyu-Han Kim, Jeongkeun Lee	In this paper, we present CUPID2.0 which improved our previously proposed CUPID indoor positioning system to overcome these limitations.
88	Early Detection of Spam Mobile Apps	Suranga Seneviratne, Aruna Seneviratne, Mohamed Ali Kaafar, Anirban Mahanti, Prasant Mohapatra	Through a systematic crawl of a popular app market and by identifying a set of removed apps, we propose a method to detect spam apps solely using app metadata available at the time of publication.
89	N-gram IDF: A Global Term Weighting Scheme Based on Information Distance	Masumi Shirakawa, Takahiro Hara, Shojiro Nishio	Based on our findings, we propose N-gram IDF, a theoretical extension of IDF for handling words and phrases of any length.
90	Query Suggestion and Data Fusion in Contextual Disambiguation	Milad Shokouhi, Marc Sloan, Paul N. Bennett, Kevyn Collins-Thompson, Siranush Sarkizova	In this paper, we explore these complementary approaches and how they might be combined.
91	Asymmetric Minwise Hashing for Indexing Binary Inner Products and Set Containment	Anshumali Shrivastava, Ping Li	In this paper, we propose asymmetric minwise hashing ({\em MH-ALSH}), to provide a solution to this well-known problem.
92	Language Understanding in the Wild: Combining Crowdsourcing and Machine Learning	Edwin D. Simpson, Matteo Venanzi, Steven Reece, Pushmeet Kohli, John Guiver, Stephen J. Roberts, Nicholas R. Jennings	To overcome this problem, we present a novel Bayesian approach to language understanding that relies on aggregated crowdsourced judgements.
93	HypTrails: A Bayesian Approach for Comparing Hypotheses About Human Trails on the Web	Philipp Singer, Denis Helic, Andreas Hotho, Markus Strohmaier	In this work, we present a general approach called HypTrails for comparing a set of hypotheses about human trails on the Web, where hypotheses represent beliefs about transitions between states.
94	Exploiting Collective Hidden Structures in Webpage Titles for Open Domain Entity Extraction	Wei Song, Shiqi Zhao, Chao Zhang, Hua Wu, Haifeng Wang, Lizhen Liu, Hanshi Wang	We present a novel method for open domain named entity extraction by exploiting the collective hidden structures in webpage titles.
95	ROCKER: A Refinement Operator for Key Discovery	Tommaso Soru, Edgard Marx, Axel-Cyrille Ngonga Ngomo	In this paper, we address this research gap by specifying a refinement operator, dubbed ROCKER, which we prove to be finite, proper and non-redundant.
96	Random Walk TripleRush: Asynchronous Graph Querying and Sampling	Philip Stutz, Bibek Paudel, Mihaela Verman, Abraham Bernstein	In this paper we propose to rethink query execution in a triple store as a highly parallelized asynchronous graph exploration on an active index data structure.
97	Open Domain Question Answering via Semantic Enrichment	Huan Sun, Hao Ma, Wen-tau Yih, Chen-Tse Tsai, Jingjing Liu, Ming-Wei Chang	In this paper, we develop a new QA system that mines answers directly from the Web, and meanwhile employs KBs as a significant auxiliary to further boost the QA performance.
98	All Who Wander: On the Prevalence and Characteristics of Multi-community Engagement	Chenhao Tan, Lillian Lee	In this paper, we examine three aspects of multi-community engagement: the sequence of communities that users post to, the language that users employ in those communities, and the feedback that users receive, using longitudinal posting behavior on Reddit as our main data source, and DBLP for auxiliary experiments.
99	LINE: Large-scale Information Network Embedding	Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, Qiaozhu Mei	In this paper, we propose a novel network embedding method called the “LINE,” which is suitable for arbitrary types of information networks: undirected, directed, and/or weighted.
100	Leveraging Pattern Semantics for Extracting Entities in Enterprises	Fangbo Tao, Bo Zhao, Ariel Fuxman, Yang Li, Jiawei Han	To address these challenges, we propose an end-to-end framework for extracting entities in enterprises, taking the input of enterprise corpus and limited seeds to generate a high-quality entity collection as output.
101	Density-friendly Graph Decomposition	Nikolaj Tatti, Aristides Gionis	We start by defining what it means for a subgraph to be locally-dense, and we show that our definition entails a nested chain decomposition of the graph, similar to the one given by k-cores, but in this case the components are arranged in order of increasing density.
102	Crowd Fraud Detection in Internet Advertising	Tian Tian, Jun Zhu, Fen Xia, Xin Zhuang, Tong Zhang	In this paper, we carefully examine the characteristics of the group behaviors of crowd fraud and identify three persistent patterns, which are moderateness, synchronicity and dispersivity.
103	Provably Fast Inference of Latent Features from Networks: with Applications to Learning Social Circles and Multilabel Classification	Charalampos Tsourakakis	In this work we focus on a fundamental theoretical question related to the above phenomena with various applications: given an undirected graph G, can we infer efficiently the latent vertex features which explain the observed network structure under the assumption of a generative model that exhibits homophily?
104	The K-clique Densest Subgraph Problem	Charalampos Tsourakakis	In this work, we introduce the k-clique densest subgraph problem, k ≥ 2.
105	GERBIL: General Entity Annotator Benchmarking Framework	Ricardo Usbeck, Michael Röder, Axel-Cyrille Ngonga Ngomo, Ciro Baron, Andreas Both, Martin Brümmer, Diego Ceccarelli, Marco Cornolti, Didier Cherix, Bernd Eickmann, Paolo Ferragina, Christiane Lemke, Andrea Moro, Roberto Navigli, Francesco Piccinno, Giuseppe Rizzo, Harald Sack, René Speck, Raphaël Troncy, Jörg Waitelonis, Lars Wesemann	We present GERBIL, an evaluation framework for semantic entity annotation.
106	An Optimization Framework for Weighting Implicit Relevance Labels for Personalized Web Search	Yury Ustinovskiy, Gleb Gusev, Pavel Serdyukov	In this paper we develop a framework for automatic reweighting of these labels.
107	A First Look at Tribal Web Traffic	Morgan Vigil, Matthew Rantanen, Elizabeth Belding	In this paper, we present the characterization of the Tribal Digital Village (TDV) network, a multi-hop wireless network currently connecting 13 reservations in San Diego county.
108	A Weighted Correlation Index for Rankings with Ties	Sebastiano Vigna	We prove a number of interesting mathematical properties of our generalization and describe an O(n\log n) algorithm for its computation.
109	Gathering Additional Feedback on Search Results by Multi-Armed Bandits with Respect to Production Ranking	Aleksandr Vorobev, Damien Lefortier, Gleb Gusev, Pavel Serdyukov	We improve the most flexible and pragmatic of them to handle some actual practical issues.
110	The E-Commerce Market for "Lemons": Identification and Analysis of Websites Selling Counterfeit Goods	John Wadleigh, Jake Drew, Tyler Moore	We investigate the practice of websites selling counterfeit goods.
111	Concept Expansion Using Web Tables	Chi Wang, Kaushik Chakrabarti, Yeye He, Kris Ganjam, Zhimin Chen, Philip A. Bernstein	In this paper, we propose to leverage the millions of tables on the web for this problem.
112	User Latent Preference Model for Better Downside Management in Recommender Systems	Jian Wang, David Hardtke	The approach we propose is general and can be applied to any scenario or domain where downside management is key to the system.
113	The Role of Data Cap in Optimal Two-part Network Pricing	Xin Wang, Richard T.B. Ma, Yinlong Xu	In this paper, we study the impact of data cap on the optimal two-part pricing schemes for congestion-prone service markets, e.g., broadband or cloud services.
114	Tweeting Cameras for Event Detection	Yuhui Wang, Mohan S. Kankanhalli	To tackle this problem, we propose an innovative multi-layer tweeting camera framework integrating both physical sensors and social sensors to detect various concepts of real-world events.
115	Mining Missing Hyperlinks from Human Navigation Traces: A Case Study of Wikipedia	Robert West, Ashwin Paranjape, Jure Leskovec	Here we propose a novel approach to identifying missing links in Wikipedia.
116	Semantic Annotation of Mobility Data using Social Media	Fei Wu, Zhenhui Li, Wang-Chien Lee, Hongjian Wang, Zhuojie Huang	We propose frequency-based method, Gaussian mixture model, and kernel density estimation (KDE) to tackle this problem.
117	Automatic Web Content Extraction by Combination of Learning and Grouping	Shanchan Wu, Jerry Liu, Jian Fan	We formulate the actual content identifying problem as a DOM tree node selection problem.
118	Executing Provenance-Enabled Queries over Web Data	Marcin Wylot, Philippe Cudre-Mauroux, Paul Groth	In this paper, we tackle the problem of efficiently executing provenance-enabled queries over RDF data.
119	Understanding Malvertising Through Ad-Injecting Browser Extensions	Xinyu Xing, Wei Meng, Byoungyoung Lee, Udi Weinsberg, Anmol Sheth, Roberto Perdisci, Wenke Lee	In this paper, we show that browser extensions that use ads as their monetization strategy often facilitate the deployment of malvertising.
120	E-commerce Reputation Manipulation: The Emergence of Reputation-Escalation-as-a-Service	Haitao Xu, Daiping Liu, Haining Wang, Angelos Stavrou	In this paper, 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.
121	Effective Techniques for Message Reduction and Load Balancing in Distributed Graph Computation	Da Yan, James Cheng, Yi Lu, Wilfred Ng	In this paper, we propose two effective message reduction techniques: (1)vertex mirroring with message combining, and (2)an additional request-respond API.
122	Tackling the Achilles Heel of Social Networks: Influence Propagation based Language Model Smoothing	Rui Yan, Ian E.H. Yen, Cheng-Te Li, Shiqi Zhao, Xiaohua Hu	In this paper we propose to tackle the Achilles Heel of social networks by smoothing the language model via influence propagation.
123	A Game Theoretic Model for the Formation of Navigable Small-World Networks	Zhi Yang, Wei Chen	In this paper, we present a game theoretic model for the formation of navigable small world networks.
124	A Scalable Asynchronous Distributed Algorithm for Topic Modeling	Hsiang-Fu Yu, Cho-Jui Hsieh, Hyokun Yun, S.V.N. Vishwanathan, Inderjit S. Dhillon	In this paper, we present a novel algorithm F+Nomad LDA which simultaneously tackles both these problems.
125	LightLDA: Big Topic Models on Modest Computer Clusters	Jinhui Yuan, Fei Gao, Qirong Ho, Wei Dai, Jinliang Wei, Xun Zheng, Eric Po Xing, Tie-Yan Liu, Wei-Ying Ma	Our major contributions include: 1) a new, highly-efficient O(1) Metropolis-Hastings sampling algorithm, whose running cost is (surprisingly) agnostic of model size, and empirically converges nearly an order of magnitude more quickly than current state-of-the-art Gibbs samplers; 2) a model-scheduling scheme to handle the big model challenge, where each worker machine schedules the fetch/use of sub-models as needed, resulting in a frugal use of limited memory capacity and network bandwidth; 3) a differential data-structure for model storage, which uses separate data structures for high- and low-frequency words to allow extremely large models to fit in memory, while maintaining high inference speed.
126	A Novelty-Seeking based Dining Recommender System	Fuzheng Zhang, Kai Zheng, Nicholas Jing Yuan, Xing Xie, Enhong Chen, Xiaofang Zhou	In this paper, by leveraging users’ historical dining pattern, socio-demographic characteristics and restaurants’ attributes, we aim at generating the top-K restaurants for a user’s next dining.
127	Daily-Aware Personalized Recommendation based on Feature-Level Time Series Analysis	Yongfeng Zhang, Min Zhang, Yi Zhang, Guokun Lai, Yiqun Liu, Honghui Zhang, Shaoping Ma	In this paper, we make use of the large volume of textual reviews for the automatic extraction of domain knowledge, namely, the explicit features/aspects in a specific product domain.
128	Automatic Detection of Information Leakage Vulnerabilities in Browser Extensions	Rui Zhao, Chuan Yue, Qing Yi	In this paper, we present a framework, LvDetector, that combines static and dynamic program analysis techniques for automatic detection of information leakage vulnerabilities in legitimate browser extensions.
129	Enquiring Minds: Early Detection of Rumors in Social Media from Enquiry Posts	Zhe Zhao, Paul Resnick, Qiaozhu Mei	We present a technique to identify trending rumors, which we define as topics that include disputed factual claims.
130	Improving User Topic Interest Profiles by Behavior Factorization	Zhe Zhao, Zhiyuan Cheng, Lichan Hong, Ed H. Chi	Here we propose to separately model users’ topical interests that come from these various behavioral signals in order to construct better user profiles.
131	Predicting Pinterest: Automating a Distributed Human Computation	Changtao Zhong, Dmytro Karamshuk, Nishanth Sastry	This paper seeks to understand Pinterest as a distributed human computation that categorises images from around the Web.