OpenRaiser/Intern-Atlas huggingface dataset:
Circularized Visualisation of Genetic Interactions
Authors: Irina Kuznetsova, Aleksandra Filipovska, Oliver Rackham, Artur Lugmayr, Andreas Holzinger
Venue: WWW
Year: 2017

Next Generation Sequencing (NGS) has been a powerful tool to investigate gene networks in biological sciences [1]. Visualisation of data produced by NGS is essential for the interpretation of the findings by biological scientists. Here we describe a workflow to image findings from a NGS sequencing methodology to investigate gene expression that can be visualised with Circus software [2]. Visualisation of these processes has provided biological scientists with valuable interpretation of high throughput data and identification of new transcripts.
Web Application Migration with Closure Reconstruction
Authors: Jin-woo Kwon, Soo-Mook Moon
Venue: WWW
Year: 2017

Due to its high portability and simplicity, web application (app) based on HTML/JavaScript/CSS has been widely used for various smart-device platforms. To take advantage of its wide platform pool, a new idea called app migration has been proposed for the web platform. Web app migration is a framework to serialize a web app running on a device and restore it in another device to continue its execution. In JavaScript semantics, one of the language features that does not allow easy app migration is a closure. A JavaScript function can access variables defined in its outer function even if the execution of the outer function is terminated. It is allowed because the inner function is created as a closure such that it contains the outer function's environment. This feature is widely used in web app development because it is the most common way to implement data encapsulation in web programming. Closures are not easy to serialize because environments can be shared by a number of closures and environments can be created in a nested way. In this paper, we propose a novel approach to fully serialize closures. We created mechanisms to extract information from a closure's environment through the JavaScript engine and to serialize the information in a proper order so that the original relationship between closures and environments can be restored properly. We implemented our mechanism on the WebKit browser and successfully migrated Octane benchmarks and seven real web apps which heavily exploit closures. We also show that our mechanism works correctly even for some extreme, closure-heavy cases.
SANE: System for Fine Grained Named Entity Typing on Textual Data
Authors: Anurag Lal, Apoorve Tomer, C. Ravindranath Chowdary
Venue: WWW
Year: 2017

Assignment of fine-grained types to named entities is gaining popularity as one of the major Information Extraction tasks due to its applications in several areas of Natural Language Processing. Existing systems use huge knowledge bases to improve the accuracy of the fine-grained types. We designed and developed SANE, a system that uses Wikipedia categories to fine grain the type of the named entities recognized in the textual data.
Improving the Transparency of the Sharing Economy
Authors: Mathias Lécuyer, Max Tucker, Augustin Chaintreau
Venue: WWW
Year: 2017

The idealistic beginnings of the sharing economy made ways to an entrenched battle to win over the public opinion and for law makers to appreciate its benefits and its risks. The stakes are high as the success of services like Airbnb reveals that under-utilized assets (e.g. spare rooms or apartments left vacant) can be efficiently matched to individual demands to generate a significant surplus to their owners. Rules and regulation, which are increasingly felt as necessary by many communities, also create friction over the best way to leverage these opportunities for growth. To make things worse, the sharing economy is complex and poorly documented: Three recent reports from public institutions and lobbying groups arrived at opposite conclusions with seemingly contradictory facts about the occupancy distribution.
On Detecting Frauds in Comparison-Shopping Services
Authors: Sang-Chul Lee, Christos Faloutsos, Dong-Kyu Chae, Sang-Wook Kim
Venue: WWW
Year: 2017

In comparison-shopping services (CSS), there exist frauds who perform excessive clicks on a target item in order to boost the popularity of it. In this paper, we introduce the problem of detecting frauds in CSS and propose three anomaly scores designed based on click behaviors of users in CSS.
On Analyzing User Topic-Specific Platform Preferences Across Multiple Social Media Sites
Authors: Roy Ka-Wei Lee, Tuan-Anh Hoang, Ee-Peng Lim
Venue: WWW
Year: 2017

Topic modeling has traditionally been studied for single text collections and applied to social media data represented in the form of text documents. With the emergence of many social media platforms, users find themselves using different social media for posting content and for social interaction. While many topics may be shared across social media platforms, users typically show preferences of certain social media platform(s) over others for certain topics. Such platform preferences may even be found at the individual level. To model social media topics as well as platform preferences of users, we propose a new topic model known as MultiPlatform-LDA (MultiLDA). Instead of just merging all posts from different social media platforms into a single text collection, MultiLDA keeps one text collection for each social media platform but allowing these platforms to share a common set of topics. MultiLDA further learns the user-specific platform preferences for each topic. We evaluate MultiLDA against TwitterLDA, the state-of-the-art method for social media content modeling, on two aspects: (i) the effectiveness in modeling topics across social media platforms, and (ii) the ability to predict platform choices for each post. We conduct experiments on three real-world datasets from Twitter, Instagram and Tumblr sharing a set of common users. Our experiments results show that the MultiLDA outperforms in both topic modeling and platform choice prediction tasks. We also show empirically that among the three social media platforms, "Daily matters" and "Relationship matters" are dominant topics in Twitter, "Social gathering", "Outing" and "Fashion" are dominant topics in Instagram, and "Music", "Entertainment" and "Fashion" are dominant topics in Tumblr.
A Single-Step Approach to Recommendation Diversification
Authors: Sang-Chul Lee, Sang-Wook Kim, Sunju Park, Dong-Kyu Chae
Venue: WWW
Year: 2017

This paper addresses recommendation diversification. Existing diversification methods have a difficulty in dealing with the accuracy-diversity tradeoff. We propose a novel method to simultaneously optimize the user preference and diversity of k-items to be recommended.
Adverse Drug Event Detection in Tweets with Semi-Supervised Convolutional Neural Networks
Authors: Kathy Lee, Ashequl Qadir, Sadid A. Hasan, Vivek V. Datla, Aaditya Prakash, Joey Liu, Oladimeji Farri
Venue: WWW
Year: 2017

Current Adverse Drug Events (ADE) surveillance systems are often associated with a sizable time lag before such events are published. Online social media such as Twitter could describe adverse drug events in real-time, prior to official reporting. Deep learning has significantly improved text classification performance in recent years and can potentially enhance ADE classification in tweets. However, these models typically require large corpora with human expert-derived labels, and such resources are very expensive to generate and are hardly available. Semi-supervised deep learning models, which offer a plausible alternative to fully supervised models, involve the use of a small set of labeled data and a relatively larger collection of unlabeled data for training. Traditionally, these models are trained on labeled and unlabeled data from similar topics or domains. In reality, millions of tweets generated daily often focus on disparate topics, and this could present a challenge for building deep learning models for ADE classification with random Twitter stream as unlabeled training data. In this work, we build several semi-supervised convolutional neural network (CNN) models for ADE classification in tweets, specifically leveraging different types of unlabeled data in developing the models to address the problem. We demonstrate that, with the selective use of a variety of unlabeled data, our semi-supervised CNN models outperform a strong state-of-the-art supervised classification model by +9.9% F1-score. We evaluated our models on the Twitter data set used in the PSB 2016 Social Media Shared Task. Our results present the new state-of-the-art for this data set.
An Observation of Research Complexity in Top Universities Based on Research Publications
Authors: Ivan Lee, Feng Xia, Göran Roos
Venue: WWW
Year: 2017

This paper investigates research specialisation of top ranked universities around the world. The revealed comparative advantage in different research fields are determined according to the number of research articles published. Subsequently, measures of research ubiquity and diversity, and research complexity index of each university, are obtained and discussed. The study is conducted on top-ranked universities according to Shanghai Jiao Tong Academic Ranking of World Universities, with bibliographical details extracted Microsoft Academic Graph data set and research fields of journals labelled with SCImago Journal Classification. Diversity-ubiquity distributions, relevance of RCI and university ranks, and geographical RCI distributions are examined in this paper.
LDOW2017: 10th Workshop on Linked Data on the Web
Authors: Jens Lehmann, Sören Auer, Sarven Capadisli, Krzysztof Janowicz, Christian Bizer, Tom Heath, Aidan Hogan, Tim Berners-Lee
Venue: WWW
Year: 2017

The 10th Linked Data on the Web workshop (LDOW2017) was held in Perth, Western Australia on April 3, 2017, co-located with the 26th International World Wide Web Conference (WWW2017). In its 10th anniversary edition, the LDOW workshop aims to stimulate discussion and further research into the challenges of publishing, consuming, and integrating structured data on the Web as well as mining knowledge from said data.
Preserving the Archival Bond in Distributed Ledgers: A Data Model and Syntax
Authors: Victoria L. Lemieux, Manu Sporny
Venue: WWW
Year: 2017

Distributed cryptographic ledgers, such as the blockchain, are now being used in recordkeeping. However, they lack a key feature of more traditional recordkeeping systems needed to establish the authenticity of records and enable reliance on them for trustworthy recordkeeping. The missing feature is known in archival science as the archival bond -- the mutual relationship that exists among documents by virtue of the actions in which they participate. In this paper, we propose a novel data model and syntax using core web principles that can be used to address this shortcoming in distributed ledgers as recordkeeping systems.
Overview of Influence Maximization in Social Media Data Analytics
Authors: Jianxin Li
Venue: WWW
Year: 2017

Social media has become a new and main platform for organizations to broadcast their policies, for companies to advertise their products, and for people to propagate their opinions. Therefore, social media data analytics has become a timely and significant research topic in recent years. In this talk, Dr. Li will first go through the social media background and the data representation of social media data. And then, he will briefly discuss the main stream research in social data mining and social computing. After that, Dr. Li will mainly introduce how the two most popular influence models are defined in social computing, what the influence maximization problem is defined, how its variants are defined. Finally, Dr. Li will introduce his current social media research project and discuss the interesting collaboration with attendees.
Beyond Understanding and Prediction: Data Mining for Action
Authors: Jiuyong Li
Venue: WWW
Year: 2017

Association analysis and prediction are two major tasks in data mining, and they represent two foremost objectives: data exploration for understanding and model construction for prediction. Data mining is known as a process to convert raw data to useful information --- knowledge. However, what do we do with the knowledge discovered from data? We will need knowledge to enable actions, such as preventing diseases in health care, taking actions to retain customers, and etc.
nTD: Noise-Profile Adaptive Tensor Decomposition
Authors: Xinsheng Li, K. Selçuk Candan, Maria Luisa Sapino
Venue: WWW
Year: 2017

Tensor decomposition is used for many web and user data analysis operations from clustering, trend detection, anomaly detection, to correlation analysis. However, many of the tensor decomposition schemes are sensitive to noisy data, an inevitable problem in the real world that can lead to false conclusions. The problem is compounded by over-fitting when the user data is sparse. Recent research has shown that it is possible to avoid over-fitting by relying on probabilistic techniques. However, these have two major deficiencies: (a) firstly, they assume that all the data and intermediary results can fit in the main memory, and (b) they treat the entire tensor uniformly, ignoring potential non-uniformities in the noise distribution. In this paper, we propose a Noise-Profile Adaptive Tensor Decomposition (nTD) method, which aims to tackle both of these challenges. In particular, nTD leverages a grid-based two-phase decomposition strategy for two complementary purposes: firstly, the grid partitioning helps ensure that the memory footprint of the decomposition is kept low; secondly (and perhaps more importantly) any a priori knowledge about the noise profiles of the grid partitions enable us to develop a sample assignment strategy (or s-strategy) that best suits the noise distribution of the given tensor. Experiments show that nTD's performance is significantly better than conventional CP decomposition techniques on noisy user data tensors.
Exploring Query Auto-Completion and Click Logs for Contextual-Aware Web Search and Query Suggestion
Authors: Liangda Li, Hongbo Deng, Anlei Dong, Yi Chang, Ricardo Baeza-Yates, Hongyuan Zha
Venue: WWW
Year: 2017

Contextual data plays an important role in modeling search engine users' behaviors on both query auto-completion (QAC) log and normal query (click) log. User's recent search history on each log has been widely studied individually as the context to benefit the modeling of users' behaviors on that log. However, there is no existing work that explores or incorporates both logs together for contextual data. As QAC and click logs actually record users' sequential behaviors while interacting with a search engine, the available context of a user's current behavior based on the same type of log can be strengthened from the user's recent search history shown on the other type of log. Our paper proposes to model users' behaviors on both QAC and click logs simultaneously by utilizing both logs as the contextual data of each other. The key idea is to capture the correlation between users' behavior patterns on both logs. We model such correlation through a novel probabilistic model based on the Latent Dirichlet allocation (LDA) model. The learned users' behavior patterns on both logs are utilized to address not only the application of query auto-completion on QAC logs, but also the click prediction and relevance ranking of web documents on click logs. Experiments on real-world logs demonstrate the effectiveness of the proposed model on both applications.
iTopic: Influential Topic Discovery from Information Networks via Keyword Query
Authors: Jianxin Li, Chengfei Liu, Lu Chen, Zhenying He, Amitava Datta, Feng Xia
Venue: WWW
Year: 2017

The rapid growth of information networks provides a significant opportunity for people to learn the world and find useful information for decision making. To find influential topics in a given context, instead of searching widely over the whole information network, normally it is wise to find the related communities first and then identify the influential topics in those communities. In this demonstration, we present a novel framework to compute the correlated sub-networks from a large information network such as CiteSeerX based on a user's keyword query, and to extract the influential topics from each correlated network. To help users understand the influential topics as a whole, we utilize a word cloud to represent the discovered topics for each correlated network. As such, multiple word clouds can be generated for different correlated networks, by which users can easily pick up their interested ones by reading the visualized topic descriptions over word clouds. To determine the sizes of different terms in a word cloud, we introduce a scoring scheme for assessing the influence of these terms in the corresponding networks. We demonstrate the functionality of our influential topic system, called iTopic, using the CiteSeerX information network data.
Identifying Value in Crowdsourced Wireless Signal Measurements
Authors: Zhijing Li, Ana Nika, Xinyi Zhang, Yanzi Zhu, Yuanshun Yao, Ben Y. Zhao, Haitao Zheng
Venue: WWW
Year: 2017

While crowdsourcing is an attractive approach to collect large-scale wireless measurements, understanding the quality and variance of the resulting data is difficult. Our work analyzes the quality of crowdsourced cellular signal measurements in the context of basestation localization, using large international public datasets (419M signal measurements and 1M cells) and corresponding ground truth values. Performing localization using raw received signal strength (RSS) data produces poor results and very high variance. Applying supervised learning improves results moderately, but variance remains high. Instead, we propose feature clustering, a novel application of unsupervised learning to detect hidden correlation between measurement instances, their features, and localization accuracy. Our results identify RSS standard deviation and RSS-weighted dispersion mean as key features that correlate with highly predictive measurement samples for both sparse and dense measurements respectively. Finally, we show how optimizing crowdsourcing measurements for these two features dramatically improves localization accuracy and reduces variance.
Understanding the User Display Names across Social Networks
Authors: Yongjun Li, You Peng, Zhen Zhang, Quanqing Xu, Hongzhi Yin
Venue: WWW
Year: 2017

The display names that an individual uses in various online social networks always contain some redundant information because some people tend to use the similar names across different networks to make them easier to remember or to build their online reputation. In this paper, we aim to measure the redundant information between different display names of the same individual. Based on the crosssite linking function, we first develop a specific distributed crawler to extract the display names that individuals select for different social networks, and we give an overview on the display names we extracted. Then we measure and analyze the redundant information in three ways: length similarity, character similarity and letter distribution similarity, comparing with display names of different individuals. We also analyze the evolution of redundant information over time. We find 45% of users tend to use the same display name across OSNs. Our findings also demonstrate that display names of the same individual show high similarity. The evolution analysis results show that redundant information is time-independent. Awareness of the redundant information between the display names can benefit many applications, such as user identification across social networks.
PaRE: A System for Personalized Route Guidance
Authors: Yaguang Li, Han Su, Ugur Demiryurek, Bolong Zheng, Tieke He, Cyrus Shahabi
Venue: WWW
Year: 2017

The turn-by-turn directions provided in existing navigation applications are exclusively derived from underlying road network topology information, i.e., the connectivity of edges to each other. Therefore, the turn-by-turn directions are simplified as metric translation of physical world (e.g. distance/time to turn) to spoken language. Such translation - that ignores human cognition of the geographic space - is often verbose and redundant for the drivers who have knowledge about the geographical areas. In this paper, we study a Personalized RoutE Guidance System dubbed PaRE - with which the goal is to generate more customized and intuitive directions based on user generated content. PaRE utilizes a wealth of user generated historical trajectory data to extract namely "landmarks" (e.g., point of interests or intersections) and frequently visited routes between them from the road network. The extracted information is used to obtain cognitive customized directions for each user. We formalize this task as a problem of finding the optimal partition for a given route that maximizes the familiarity while minimizing the number of segments in the partition, and propose two efficient algorithms to solve it. For empirical study, we apply our solution to both real and synthetic trajectory datasets to evaluate the performance and effectiveness of PaRE.
Semi-supervised Clustering in Attributed Heterogeneous Information Networks
Authors: Xiang Li, Yao Wu, Martin Ester, Ben Kao, Xin Wang, Yudian Zheng
Venue: WWW
Year: 2017

A heterogeneous information network (HIN) is one whose nodes model objects of different types and whose links model objects' relationships. In many applications, such as social networks and RDF-based knowledge bases, information can be modeled as HINs. To enrich its information content, objects (as represented by nodes) in an HIN are typically associated with additional attributes. We call such an HIN an Attributed HIN or AHIN. We study the problem of clustering objects in an AHIN, taking into account objects' similarities with respect to both object attribute values and their structural connectedness in the network. We show how supervision signal, expressed in the form of a must-link set and a cannot-link set, can be leveraged to improve clustering results. We put forward the SCHAIN algorithm to solve the clustering problem. We conduct extensive experiments comparing SCHAIN with other state-of-the-art clustering algorithms and show that SCHAIN outperforms the others in clustering quality.
Multimodal Question Answering over Structured Data with Ambiguous Entities
Authors: Huadong Li, Yafang Wang, Gerard de Melo, Changhe Tu, Baoquan Chen
Venue: WWW
Year: 2017

In recent years, we have witnessed profound changes in the way people satisfy their information needs. For instance, with the ubiquitous 24/7 availability of mobile devices, the number of search engine queries on mobile devices has reportedly overtaken that of queries on regular personal computers. In this paper, we consider the task of multimodal question answering over structured data, in which a user supplies not just a natural language query but also an image. Our system addresses this by optimizing a non-convex objective function capturing multimodal constraints. Our experiments show that this enables it to answer even very challenging ambiguous entity queries with high accuracy.
Effective Strategies on Representing Information Networks
Authors: Hang Li, Haozheng Wang, Zhenglu Yang, Jin-Mao Wei, Masato Odagaki
Venue: WWW
Year: 2017

Network representation is the basis of many applications and of extensive interest in various fields such as information retrieval, social network analysis, and recommendation systems. Majority of previous methods on network representation only considered incomplete aspects of the problem, such as link structure, node information, or partial integration. The present paper proposes a comprehensive network representation model, which seamlessly integrates the text information, node label, and first-order and second-order proximity of a network. The effectiveness of the introduced strategies is experimentally evaluated. Results demonstrate that our method is better than state-of-the-art techniques.