OpenRaiser/Intern-Atlas huggingface dataset:
Vertex collocation profiles: subgraph counting for link analysis and prediction
Authors: Ryan Lichtenwalter, Nitesh V. Chawla
Venue: WWW
Year: 2012

We introduce the concept of a vertex collocation profile (VCP) for the purpose of topological link analysis and prediction. VCPs provide nearly complete information about the surrounding local structure of embedded vertex pairs. The VCP approach offers a new tool for domain experts to understand the underlying growth mechanisms in their networks and to analyze link formation mechanisms in the appropriate sociological, biological, physical, or other context. The same resolution that gives VCP its analytical power also enables it to perform well when used in supervised models to discriminate potential new links. We first develop the theory, mathematics, and algorithms underlying VCPs. Then we demonstrate VCP methods performing link prediction competitively with unsupervised and supervised methods across several different network families. We conclude with timing results that introduce the comparative performance of several existing algorithms and the practicability of VCP computations on large networks.
Community detection in incomplete information networks
Authors: Wangqun Lin, Xiangnan Kong, Philip S. Yu, Quanyuan Wu, Yan Jia, Chuan Li
Venue: WWW
Year: 2012

With the recent advances in information networks, the problem of community detection has attracted much attention in the last decade. While network community detection has been ubiquitous, the task of collecting complete network data remains challenging in many real-world applications. Usually the collected network is incomplete with most of the edges missing. Commonly, in such networks, all nodes with attributes are available while only the edges within a few local regions of the network can be observed. In this paper, we study the problem of detecting communities in incomplete information networks with missing edges. We first learn a distance metric to reproduce the link-based distance between nodes from the observed edges in the local information regions. We then use the learned distance metric to estimate the distance between any pair of nodes in the network. A hierarchical clustering approach is proposed to detect communities within the incomplete information networks. Empirical studies on real-world information networks demonstrate that our proposed method can effectively detect community structures within incomplete information networks.
Sentiment classification via integrating multiple feature presentations
Authors: Yuming Lin, Jingwei Zhang, Xiaoling Wang, Aoying Zhou
Venue: WWW
Year: 2012

In the bag of words framework, documents are often converted into vectors according to predefined features together with weighting mechanisms. Since each feature presentation has its character, it is difficult to determine which one should be chosen for a specific domain, especially for the users who are not familiar with the domain. This paper explores the integration of various feature presentations to improve the classification accuracy. A general two phases framework is proposed. In the first phase, we train multiple base classifiers with various vector spaces and use these classifiers to predict the class of testing samples respectively. In the second phase, the previous predicted results are integrated into the ultimate class via stacking with SVM. The experimental results demonstrate the effectiveness of our method.
"It's simply integral to what I do": enquiries into how the web is weaved into everyday life
Authors: Siân E. Lindley, Sam Meek, Abigail Sellen, Richard H. R. Harper
Venue: WWW
Year: 2012

This paper presents findings from a field study of 24 individuals who kept diaries of their web use, across device and location, for a period of four days. Our focus was on how the web was used for non-work purposes, with a view to understanding how this is intertwined with everyday life. While our initial aim was to update existing frameworks of 'web activities', such as those described by Sellen et al. [25] and Kellar et al. [14], our data lead us to suggest that the notion of 'web activity' is only partially useful for an analytic understanding of what it is that people do when they go online. Instead, our analysis leads us to present five modes of web use, which can be used to frame and enrich interpretations of 'activity'. These are respite, orienting, opportunistic use, purposeful use and lean-back internet. We then consider two properties of the web that enable it to be tailored to these different modes, persistence and temporality, and close by suggesting ways of drawing upon these qualities in order to inform design.
Simplifying friendlist management
Authors: Yabing Liu, Bimal Viswanath, Mainack Mondal, P. Krishna Gummadi, Alan Mislove
Venue: WWW
Year: 2012

Online social networks like Facebook allow users to connect, communicate, and share content. The popularity of these services has lead to an information overload for their users; the task of simply keeping track of different interactions has become daunting. To reduce this burden, sites like Facebook allows the user to group friends into specific lists, known as friendlists, aggregating the interactions and content from all friends in each friendlist. While this approach greatly reduces the burden on the user, it still forces the user to create and populate the friendlists themselves and, worse, makes the user responsible for maintaining the membership of their friendlists over time. We show that friendlists often have a strong correspondence to the structure of the social network, implying that friendlists may be automatically inferred by leveraging the social network structure. We present a demonstration of Friendlist Manager, a Facebook application that proposes friendlists to the user based on the structure of their local social network, allows the user to tweak the proposed friendlists, and then automatically creates the friendlists for the user.
Tuning parameters of the expected reciprocal rank
Authors: Yury Logachev, Lidia Grauer, Pavel Serdyukov
Venue: WWW
Year: 2012

There are several popular IR metrics based on an underlying user model. Most of them are parameterized. Usually parameters of these metrics are chosen on the basis of general considerations and not validated by experiments with real users. Particularly, the parameters of the Expected Reciprocal Rank measure are the normalized parameters of the DCG metric, and the latter are chosen in an ad-hoc manner. We suggest two approaches for adjusting parameters of the ERR model by analyzing real users behaviour: one based on a controlled experiment and another relying on search log analysis. We show that our approaches generate parameters that are largely different from the commonly used parameters of the ERR model.
Distributed graph pattern matching
Authors: Shuai Ma, Yang Cao, Jinpeng Huai, Tianyu Wo
Venue: WWW
Year: 2012

Graph simulation has been adopted for pattern matching to reduce the complexity and capture the need of novel applications. With the rapid development of the Web and social networks, data is typically distributed over multiple machines. Hence a natural question raised is how to evaluate graph simulation on distributed data. To our knowledge, no such distributed algorithms are in place yet. This paper settles this question by providing evaluation algorithms and optimizations for graph simulation in a distributed setting. (1) We study the impacts of components and data locality on the evaluation of graph simulation. (2) We give an analysis of a large class of distributed algorithms, captured by a message-passing model, for graph simulation. We also identify three complexity measures: visit times, makespan and data shipment, for analyzing the distributed algorithms, and show that these measures are essentially controversial with each other. (3) We propose distributed algorithms and optimization techniques that exploit the properties of graph simulation and the analyses of distributed algorithms. (4) We experimentally verify the effectiveness and efficiency of these algorithms, using both real-life and synthetic data.
A habit mining approach for discovering similar mobile users
Authors: Haiping Ma, Huanhuan Cao, Qiang Yang, Enhong Chen, Jilei Tian
Venue: WWW
Year: 2012

Discovering similar users with respect to their habits plays an important role in a wide range of applications, such as collaborative filtering for recommendation, user segmentation for market analysis, etc. Recently, the progressing ability to sense user contexts of smart mobile devices makes it possible to discover mobile users with similar habits by mining their habits from their mobile devices. However, though some researchers have proposed effective methods for mining user habits such as behavior pattern mining, how to leverage the mined results for discovering similar users remains less explored. To this end, we propose a novel approach for conquering the sparseness of behavior pattern space and thus make it possible to discover similar mobile users with respect to their habits by leveraging behavior pattern mining. To be specific, first, we normalize the raw context log of each user by transforming the location-based context data and user interaction records to more general representations. Second, we take advantage of a constraint-based Bayesian Matrix Factorization model for extracting the latent common habits among behavior patterns and then transforming behavior pattern vectors to the vectors of mined common habits which are in a much more dense space. The experiments conducted on real data sets show that our approach outperforms three baselines in terms of the effectiveness of discovering similar mobile users with respect to their habits.
Secure querying of recursive XML views: a standard xpath-based technique
Authors: Houari Mahfoud, Abdessamad Imine
Venue: WWW
Year: 2012

Most state-of-the art approaches for securing XML documents allow users to access data only through authorized views defined by annotating an XML grammar (e.g. DTD) with a collection of XPath expressions. To prevent improperdisclosure of confidential information, user queries posed on these views need to be rewritten into equivalent queries on the underlying documents, which enables us to avoid the overhead of view materialization and maintenance. A major concern here is that XPath query rewriting for recursive XML views is still an open problem. To overcome this problem, some authors have proposed rewriting approaches based on the non-standard language, "Regular XPath", which is more expressive than XPath and makes rewriting possible under recursion. However, query rewriting under Regular XPath can be of exponential size as it relies on automaton model. Most importantly, Regular XPath remains a theoretical achievement. Indeed, it is not commonly used in practice as translation and evaluation tools are not available. In this work, we show that query rewriting is always possible for recursive XML views using only the expressive power of the standard XPath. We propose a general approach for securely querying of XML data under arbitrary security views (recursive or not) and for a significant fragment of XPath. We provide a linear rewriting algorithm that is efficient and scales well.
GoThere: travel suggestions using geotagged photos
Authors: Abdul Majid, Ling Chen, Gencai Chen, Hamid Turab Mirza, Ibrar Hussain
Venue: WWW
Year: 2012

We propose a context and preference aware travel guide that suggests significant tourist destinations to users based on their preferences and current surrounding context using contextualized user-generated contents from the social media repository, i.e., Flickr.
Extracting client-side web application code
Authors: Josip Maras, Jan Carlson, Ivica Crnkovic
Venue: WWW
Year: 2012

The web application domain is one of the fastest growing and most wide-spread application domains today. By utilizing fast, modern web browsers and advanced scripting techniques, web developers are developing highly interactive applications that can, in terms of user-experience and responsiveness, compete with standard desktop applications. A web application is composed of two equally important parts: the server-side and the client-side. The client-side acts as a user-interface to the application, and can be viewed as a collection of behaviors. Similar behaviors are often used in a large number of applications, and facilitating their reuse offers considerable benefits. However, due to client-side specifics, such as multi-language implementation and extreme dynamicity, identifying and extracting code responsible for a certain behavior is difficult. In this paper we present a semi-automatic method for extracting client-side web application code implementing a certain behavior. We show how by analyzing the execution of a usage scenario, code responsible for a certain behavior can be identified, how dependencies between different parts of the application can be tracked, and how in the end only the code responsible for a certain behavior can be extracted. Our evaluation shows that the method is capable of extracting stand-alone behaviors, while achieving considerable savings in terms of code size and application performance.
Mining conversations of geographically changing users
Authors: Liam McNamara, Christian Rohner
Venue: WWW
Year: 2012

In recent disaster events, social media has proven to be an effective communication tool for affected people. The corpus of generated messages contains valuable information about the situation, needs, and locations of victims. We propose an approach to extract significant aspects of user discussions to better inform responders and enable an appropriate response. The methodology combines location based division of users together with standard text mining (term frequency inverse document frequency) to identify important topics of conversation in a dynamic geographic network. We further suggest that both topics and movement patterns change during a disaster, which requires identification of new trends. When applied to an area that has suffered a disaster, this approach can provide 'sensemaking' through insights into where people are located, where they are going and what they communicate when moving.
SocialEMIS: improving emergency preparedness through collaboration
Authors: Ouejdane Mejri, Pierluigi Plebani
Venue: WWW
Year: 2012

The definition of the contingency plan during the preparedness phase holds a crucial role in emergency management. A proper emergency response, indeed, requires the implementation of a contingency plan that can be accurate only if different people with different skills are involved. The goal of this paper is to introduce SocialEMIS, a first prototype of a tool that supports the collaborative definition of contingency plans. Although the current implementation is now focused on the role of the emergency operators, the accuracy of the plan will also take advantage of information coming from the citizens in future releases. Moreover, the contingency plans defined with SocialEMIS represent a knowledge base for defining other contingency plans.
CloudGenius: decision support for web server cloud migration
Authors: Michael Menzel, Rajiv Ranjan
Venue: WWW
Year: 2012

Cloud computing is the latest computing paradigm that delivers hardware and software resources as virtualized services in which users are free from the burden of worrying about the low-level system administration details. Migrating Web applications to Cloud services and integrating Cloud services into existing computing infrastructures is non-trivial. It leads to new challenges that often require innovation of paradigms and practices at all levels: technical, cultural, legal, regulatory, and social. The key problem in mapping Web applications to virtualized Cloud services is selecting the best and compatible mix of software images (e.g., Web server image) and infrastructure services to ensure that Quality of Service (QoS) targets of an application are achieved. The fact that, when selecting Cloud services, engineers must consider heterogeneous sets of criteria and complex dependencies between infrastructure services and software images, which are impossible to resolve manually, is a critical issue. To overcome these challenges, we present a framework (called CloudGenius) which automates the decision-making process based on a model and factors specifically for Web server migration to the Cloud. CloudGenius leverages a well known multi-criteria decision making technique, called Analytic Hierarchy Process, to automate the selection process based on a model, factors, and QoS parameters related to an application. An example application demonstrates the applicability of the theoretical CloudGenius approach. Moreover, we present an implementation of CloudGenius that has been validated through experiments.
A user profile modelling using social annotations: a survey
Authors: Manel Mezghani, Corinne Amel Zayani, Ikram Amous, Faïez Gargouri
Venue: WWW
Year: 2012

As social networks are growing in terms of the number of users, resources and interactions; the user may be lost or unable to find useful information. Social elements could avoid this disorientation like the social annotations (tags) which become more and more popular and contribute to avoid the disorientation of the user. Representing a user based on these social annotations has showed their utility in reflecting an accurate user profile which could be used for a recommendation purpose. In this paper, we give a state of the art of characteristics of social user and techniques which model and update a tag-based profile. We show how to treat social annotations and the utility of modelling tag-based profiles for recommendation purposes.
Understanding task-driven information flow in collaborative networks
Authors: Gengxin Miao, Shu Tao, Winnie Cheng, Randy Moulic, Louise E. Moser, David Lo, Xifeng Yan
Venue: WWW
Year: 2012

Collaborative networks are a special type of social network formed by members who collectively achieve specific goals, such as fixing software bugs and resolving customers' problems. In such networks, information flow among members is driven by the tasks assigned to the network, and by the expertise of its members to complete those tasks. In this work, we analyze real-life collaborative networks to understand their common characteristics and how information is routed in these networks. Our study shows that collaborative networks exhibit significantly different properties compared with other complex networks. Collaborative networks have truncated power-law node degree distributions and other organizational constraints. Furthermore, the number of steps along which information is routed follows a truncated power-law distribution. Based on these observations, we developed a network model that can generate synthetic collaborative networks subject to certain structure constraints. Moreover, we developed a routing model that emulates task-driven information routing conducted by human beings in a collaborative network. Together, these two models can be used to study the efficiency of information routing for different types of collaborative networks -- a problem that is important in practice yet difficult to solve without the method proposed in this paper.
Evaluating the impact of incorporating information from social media streams in disaster relief routing
Authors: Ashlea Bennett Milburn, Clarence L. Wardell
Venue: WWW
Year: 2012

In this paper, we describe a model that can be used to evaluate the impact of using imperfect information when routing supplies for disaster relief. Using two objectives, maximizing the population supported, and minimizing response time, we explore the potential tradeoffs (e.g. more information, but possibly less accurate) of using information from social media streams to inform routing and resource allocation decisions immediately after a disaster.
Semi-supervised correction of biased comment ratings
Authors: Abhinav Mishra, Rajeev Rastogi
Venue: WWW
Year: 2012

In many instances, offensive comments on the internet attract a disproportionate number of positive ratings from highly biased users. This results in an undesirable scenario where these offensive comments are the top rated ones. In this paper, we develop semi-supervised learning techniques to correct the bias in user ratings of comments. Our scheme uses a small number of comment labels in conjunction with user rating information to iteratively compute user bias and unbiased ratings for unlabeled comments. We show that the running time of each iteration is linear in the number of ratings, and the system converges to a unique fixed point. To select the comments to label, we devise an active learning algorithm based on empirical risk minimization. Our active learning method incrementally updates the risk for neighboring comments each time a comment is labeled, and thus can easily scale to large comment datasets. On real-life comments from Yahoo! News, our semi-supervised and active learning algorithms achieve higher accuracy than simple baselines, with few labeled examples.
Semi-automatic semantic moderation of web annotations
Authors: Elaheh Momeni
Venue: WWW
Year: 2012

Many social media portals are featuring annotation functionality in order to integrate the end users' knowledge with existing digital curation processes. This facilitates extending existing metadata about digital resources. However, due to various levels of annotators' expertise, the quality of annotations can vary from excellent to vague. The evaluation and moderation of annotations (be they troll, vague, or helpful) have not been sufficiently analyzed automatically. Available approaches mostly attempt to solve the problem by using distributed moderation systems, which are influenced by factors affecting accuracy (such as imbalance voting). Despite this, we hypothesize that analyzing and exploiting both content and context dimensions of annotations may assist the automatic moderation process. In this research, we focus on leveraging the context and content features of social web annotations for semi-automatic semantic moderation. This paper describes the vision of our research, proposes an approach for semi-automatic semantic moderation, introduces an ongoing effort from which we collect data that can serve as a basis for evaluating our assumption, and report on lessons learned so far.
The RaiNewsbook: browsing worldwide multimodal news stories by facts, entities and dates
Authors: Maurizio Montagnuolo, Alberto Messina
Venue: WWW
Year: 2012

This paper presents a novel framework for multimodal news data aggregation, retrieval and browsing. News aggregations are contextualised within automatically extracted information such as entities (i.e. persons, places and organisations), temporal span, categorical topics, social networks popularity and audience scores. Further resources coming from professional repositories, and related to the aggregation topics, can be accessed as well. The system is accessible through a Web interface supporting interactive navigation and exploration of large-scale collections of news stories at the topic and context levels. Users can select news topics and sub-topics interactively, building their personal paths towards worldwide events, main characters, dates and contents.