OpenRaiser/Intern-Atlas huggingface dataset:
A tool for collecting provenance data in social media
Authors: Pritam Gundecha, Suhas Ranganath, Zhuo Feng, Huan Liu
Venue: KDD
Year: 2013

In recent years, social media sites have provided a large amount of information. Recipients of such information need mechanisms to know more about the received information, including the provenance. Previous research has shown that some attributes related to the received information provide additional context, so that a recipient can assess the amount of value, trust, and validity to be placed in the received information. Personal attributes of a user, including name, location, education, ethnicity, gender, and political and religious affiliations, can be found in social media sites. In this paper, we present a novel web-based tool for collecting the attributes of interest associated with a particular social media user related to the received information. This tool provides a way to combine different attributes available at different social media sites into a single user profile. Using different types of Twitter users, we also evaluate the performance of the tool in terms of number of attribute values collected, validity of these values, and total amount of retrieval time.
Soft-CsGDT: soft cost-sensitive Gaussian decision tree for cost-sensitive classification of data streams
Authors: Ning Guo, Yanhua Yu, Meina Song, Junde Song, Yu Fu
Venue: KDD
Year: 2013

Nowadays in many real-world scenarios, high speed data streams are usually with non-uniform misclassification costs and thus call for cost-sensitive classification algorithms of data streams. However, only little literature focuses on this issue. On the other hand, the existing algorithms for cost-sensitive classification can achieve excellent performance in the metric of total misclassification costs, but always lead to obvious reduction of accuracy, which restrains the practical application greatly. In this paper, we present an improved folk theorem. Based on the new theorem, the existing accuracy-based classification algorithm can be converted into soft cost-sensitive one immediately, which allows us to take both accuracy and cost into account. Following the idea of this theorem, the soft-CsGDT algorithm is proposed to process the data streams with non-uniform misclassification costs, which is an expansion of GDT. With both synthetic and real-world datasets, the experimental results show that compared with the cost-sensitive algorithm, the accuracy in our soft-CsGDT is significantly improved, while the total misclassification costs are approximately the same.
Empirical bayes model to combine signals of adverse drug reactions
Authors: Rave Harpaz, William DuMouchel, Paea LePendu, Nigam H. Shah
Venue: KDD
Year: 2013

Data mining is a crucial tool for identifying risk signals of potential adverse drug reactions (ADRs). However, mining of ADR signals is currently limited to leveraging a single data source at a time. It is widely believed that combining ADR evidence from multiple data sources will result in a more accurate risk identification system. We present a methodology based on empirical Bayes modeling to combine ADR signals mined from ~5 million adverse event reports collected by the FDA, and healthcare data corresponding to 46 million patients' the main two types of information sources currently employed for signal detection. Based on four sets of test cases (gold standard), we demonstrate that our method leads to a statistically significant and substantial improvement in signal detection accuracy, averaging 40% over the use of each source independently, and an area under the ROC curve of 0.87. We also compare the method with alternative supervised learning approaches, and argue that our approach is preferable as it does not require labeled (training) samples whose availability is currently limited. To our knowledge, this is the first effort to combine signals from these two complementary data sources, and to demonstrate the benefits of a computationally integrative strategy for drug safety surveillance.
The business impact of deep learning
Authors: Jeremy Howard
Venue: KDD
Year: 2013

In the last year deep learning has gone from being a special purpose machine learning technique used mainly for image and speech recognition, to becoming a general purpose machine learning tool. This has broad implications for all organizations that rely on data analysis. It represents the latest development in a general trend towards more automated algorithms, and away from domain specific knowledge. For organizations that rely on domain expertise for their competitive advantage, this trend could be extremely disruptive. For start-ups interested in entering established markets, this trend could be a major opportunity. This talk will be a non-technical introduction to general-purpose deep learning, and its potential business impact.
Forecasting building occupancy using sensor network data
Authors: James Howard, William A. Hoff
Venue: KDD
Year: 2013

Forecasting the occupancy of buildings can lead to significant improvement of smart heating and cooling systems. Using a sensor network of simple passive infrared motion sensors densely placed throughout a building, we perform data mining to forecast occupancy a short time (i.e., up to 60 minutes) into the future. Our approach is to train a set of standard forecasting models to our time series data. Each model then forecasts occupancy a various horizons into the future. We combine these forecasts using a modified Bayesian combined forecasting approach. The method is demonstrated on two large building occupancy datasets, and shows promising results for forecasting horizons of up to 60 minutes. Because the two datasets have such different occupancy profiles, we compare our algorithms on each dataset to evaluate the performance of the forecasting algorithm for the different conditions.
A space efficient streaming algorithm for triangle counting using the birthday paradox
Authors: Madhav Jha, C. Seshadhri, Ali Pinar
Venue: KDD
Year: 2013

We design a space efficient algorithm that approximates the transitivity (global clustering coefficient) and total triangle count with only a single pass through a graph given as a stream of edges. Our procedure is based on the classic probabilistic result, the birthday paradox. When the transitivity is constant and there are more edges than wedges (common properties for social networks), we can prove that our algorithm requires O(√n) space (n is the number of vertices) to provide accurate estimates. We run a detailed set of experiments on a variety of real graphs and demonstrate that the memory requirement of the algorithm is a tiny fraction of the graph. For example, even for a graph with 200 million edges, our algorithm stores just 60,000 edges to give accurate results. Being a single pass streaming algorithm, our procedure also maintains a real-time estimate of the transitivity/number of triangles of a graph, by storing a miniscule fraction of edges.
Solving combinatorial optimization problems using relaxed linear programming: a high performance computing perspective
Authors: Chen Jin, Qiang Fu, Huahua Wang, Ankit Agrawal, William Hendrix, Wei-keng Liao, Md. Mostofa Ali Patwary, Arindam Banerjee, Alok N. Choudhary
Venue: KDD
Year: 2013

Several important combinatorial optimization problems can be formulated as maximum a posteriori (MAP) inference in discrete graphical models. We adopt the recently proposed parallel MAP inference algorithm Bethe-ADMM and implement it using message passing interface (MPI) to fully utilize the computing power provided by the modern supercomputers with thousands of cores. The empirical results show that our parallel implementation scales almost linearly even with thousands of cores.
Forex-foreteller: currency trend modeling using news articles
Authors: Fang Jin, Nathan Self, Parang Saraf, Patrick Butler, Wei Wang, Naren Ramakrishnan
Venue: KDD
Year: 2013

Financial markets are quite sensitive to unanticipated news and events. Identifying the effect of news on the market is a challenging task. In this demo, we present Forex-foreteller (FF) which mines news articles and makes forecasts about the movement of foreign currency markets. The system uses a combination of language models, topic clustering, and sentiment analysis to identify relevant news articles. These articles along with the historical stock index and currency exchange values are used in a linear regression model to make forecasts. The system has an interactive visualizer designed specifically for touch-sensitive devices which depicts forecasts along with the chronological news events and financial data used for making the forecasts.
Efficiently rewriting large multimedia application execution traces with few event sequences
Authors: Christiane Kamdem Kengne, Léon Constantin Fopa, Alexandre Termier, Noha Ibrahim, Marie-Christine Rousset, Takashi Washio, Miguel Santana
Venue: KDD
Year: 2013

Session Industrial and government emerging (Security & Privacy)
Analysis of advanced meter infrastructure data of water consumption in apartment buildings
Authors: Einat Kermany, Hanna Mazzawi, Dorit Baras, Yehuda Naveh, Hagai Michaelis
Venue: KDD
Year: 2013

We present our experience of using machine learning techniques over data originating from advanced meter infrastructure (AMI) systems for water consumption in a medium-size city. We focus on two new use cases that are of special importance to city authorities. One use case is the automatic identification of malfunctioning meters, with a focus on distinguishing them from legitimate non-consumption such as during periods when the household residents are on vacation. The other use case is the identification of leaks or theft in the unmetered common areas of apartment buildings. These two use cases are highly important to city authorities both because of the lost revenue they imply and because of the hassle to the residents in cases of delayed identification. Both cases are inherently complex to analyze and require advanced data mining techniques in order to achieve high levels of correct identification. Our results provide for faster and more accurate detection of malfunctioning meters as well as leaks in the common areas. This results in significant tangible value to the authorities in terms of increase in technician efficiency and a decrease in the amount of wasted, non-revenue, water.
Multi-label classification by mining label and instance correlations from heterogeneous information networks
Authors: Xiangnan Kong, Bokai Cao, Philip S. Yu
Venue: KDD
Year: 2013

Multi-label classification is prevalent in many real-world applications, where each example can be associated with a set of multiple labels simultaneously. The key challenge of multi-label classification comes from the large space of all possible label sets, which is exponential to the number of candidate labels. Most previous work focuses on exploiting correlations among different labels to facilitate the learning process. It is usually assumed that the label correlations are given beforehand or can be derived directly from data samples by counting their label co-occurrences. However, in many real-world multi-label classification tasks, the label correlations are not given and can be hard to learn directly from data samples within a moderate-sized training set. Heterogeneous information networks can provide abundant knowledge about relationships among different types of entities including data samples and class labels. In this paper, we propose to use heterogeneous information networks to facilitate the multi-label classification process. By mining the linkage structure of heterogeneous information networks, multiple types of relationships among different class labels and data samples can be extracted. Then we can use these relationships to effectively infer the correlations among different class labels in general, as well as the dependencies among the label sets of data examples inter-connected in the network. Empirical studies on real-world tasks demonstrate that the performance of multi-label classification can be effectively boosted using heterogeneous information net- works.
TV predictor: personalized program recommendations to be displayed on SmartTVs
Authors: Christopher Krauss, Lars George, Stefan Arbanowski
Venue: KDD
Year: 2013

Switching through the variety of available TV channels to find the most acceptable program at the current time can be very time-consuming. Especially at the prime time when there are lots of different channels offering quality content it is hard to find the best fitting channel.
Unsupervised link prediction using aggregative statistics on heterogeneous social networks
Authors: Tsung-Ting Kuo, Rui Yan, Yu-Yang Huang, Perng-Hwa Kung, Shou-De Lin
Venue: KDD
Year: 2013

The concern of privacy has become an important issue for online social networks. In services such as Foursquare.com, whether a person likes an article is considered private and therefore not disclosed; only the aggregative statistics of articles (i.e., how many people like this article) is revealed. This paper tries to answer a question: can we predict the opinion holder in a heterogeneous social network without any labeled data? This question can be generalized to a link prediction with aggregative statistics problem. This paper devises a novel unsupervised framework to solve this problem, including two main components: (1) a three-layer factor graph model and three types of potential functions; (2) a ranked-margin learning and inference algorithm. Finally, we evaluate our method on four diverse prediction scenarios using four datasets: preference (Foursquare), repost (Twitter), response (Plurk), and citation (DBLP). We further exploit nine unsupervised models to solve this problem as baselines. Our approach not only wins out in all scenarios, but on the average achieves 9.90% AUC and 12.59% NDCG improvement over the best competitors. The resources are available at http://www.csie.ntu.edu.tw/~d97944007/aggregative/
STRIP: stream learning of influence probabilities
Authors: Konstantin Kutzkov, Albert Bifet, Francesco Bonchi, Aristides Gionis
Venue: KDD
Year: 2013

Influence-driven diffusion of information is a fundamental process in social networks. Learning the latent variables of such process, i.e., the influence strength along each link, is a central question towards understanding the structure and function of complex networks, modeling information cascades, and developing applications such as viral marketing. Motivated by modern microblogging platforms, such as twitter, in this paper we study the problem of learning influence probabilities in a data-stream scenario, in which the network topology is relatively stable and the challenge of a learning algorithm is to keep up with a continuous stream of tweets using a small amount of time and memory. Our contribution is a number of randomized approximation algorithms, categorized according to the available space (superlinear, linear, and sublinear in the number of nodes n) and according to different models (landmark and sliding window). Among several results, we show that we can learn influence probabilities with one pass over the data, using O(nlog n) space, in both the landmark model and the sliding-window model, and we further show that our algorithm is within a logarithmic factor of optimal. For truly large graphs, when one needs to operate with sublinear space, we show that we can still learn influence probabilities in one pass, assuming that we restrict our attention to the most active users. Our thorough experimental evaluation on large social graph demonstrates that the empirical performance of our algorithms agrees with that predicted by the theory.
Real-time disease surveillance using Twitter data: demonstration on flu and cancer
Authors: Kathy Lee, Ankit Agrawal, Alok N. Choudhary
Venue: KDD
Year: 2013

Social media is producing massive amounts of data on an unprecedented scale. Here people share their experiences and opinions on various topics, including personal health issues, symptoms, treatments, side-effects, and so on. This makes publicly available social media data an invaluable resource for mining interesting and actionable healthcare insights. In this paper, we describe a novel real-time flu and cancer surveillance system that uses spatial, temporal, and text mining on Twitter data. The real-time analysis results are reported visually in terms of US disease surveillance maps, distribution and timelines of disease types, symptoms, and treatments, in addition to overall disease activity timelines on our project website. Our surveillance system can be very useful not only for early prediction of seasonal disease outbreaks such as flu, but also for monitoring distribution of cancer patients with different cancer types and symptoms in each state and the popularity of treatments used. The resulting insights are expected to help facilitate faster response to and preparation for epidemics and also be very useful for both patients and doctors to make more informed decisions.
KeySee: supporting keyword search on evolving events in social streams
Authors: Pei Lee, Laks V. S. Lakshmanan, Evangelos E. Milios
Venue: KDD
Year: 2013

Online social streams such as Twitter/Facebook timelines and forum discussions have emerged as prevalent channels for information dissemination. As these social streams surge quickly, information overload has become a huge problem. Existing keyword search engines on social streams like Twitter Search are not successful in overcoming the problem, because they merely return an overwhelming list of posts, with little aggregation or semantics. In this demo, we provide a new solution called \keysee by grouping posts into events, and track the evolution patterns of events as new posts stream in and old posts fade out. Noise and redundancy problems are effectively addressed in our system. Our demo supports refined keyword query on evolving events by allowing users to specify the time span and designated evolution pattern. For each event result, we provide various analytic views such as frequency curves, word clouds and GPS distributions. We deploy \keysee on real Twitter streams and the results show that our demo outperforms existing keyword search engines on both quality and usability.
Feature engineering and tree modeling for author-paper identification challenge
Authors: Jiefei Li, Xiaocong Liang, Weijie Ding, Weidong Yang, Rong Pan
Venue: KDD
Year: 2013

The ability to search literature and collect/aggregate metrics around publications is a central tool for modern research. Both academic and industry researchers across hundreds of scientific disciplines, from astronomy to zoology, increasingly rely on search to understand what has been published and by whom. Microsoft Academic Search is an open platform, which provides a variety of metrics and experiences for the research community, in addition to literature search. As the covering data came from many sources, the profile of an author with an ambiguous name tends to contain noise, resulting in papers that are incorrectly assigned to others. KDD Cup 2013 Track 1 challenges participants to determine which papers in an author profile were truly written by the given author.
Combination of feature engineering and ranking models for paper-author identification in KDD Cup 2013
Authors: Chun-Liang Li, Yu-Chuan Su, Ting-Wei Lin, Cheng-Hao Tsai, Wei-Cheng Chang, Kuan-Hao Huang, Tzu-Ming Kuo, Shan-Wei Lin, Young-San Lin, Yu-Chen Lu, Chun-Pai Yang, Cheng-Xia Chang, Wei-Sheng Chin, Yu-Chin Juan, Hsiao-Yu Tung, Jui-Pin Wang, Cheng-Kuang Wei, Felix Wu, Tu-Chun Yin, Tong Yu, Yong Zhuang, Shou-de Lin, Hsuan-Tien Lin, Chih-Jen Lin
Venue: KDD
Year: 2013

The track 1 problem in KDD Cup 2013 is to discriminate between papers confirmed by the given authors from the other deleted papers. This paper describes the winning solution of team National Taiwan University for track 1 of KDD Cup 2013. First, we conduct the feature engineering to transform the various provided text information into 97 features. Second, we train classification and ranking models using these features. Last, we combine our individual models to boost the performance by using results on the internal validation set and the official Valid set. Some effective post-processing techniques have also been proposed. Our solution achieves 0.98259 MAP score and ranks the first place on the private leaderboard of Test set.
Location-aware publish/subscribe
Authors: Guoliang Li, Yang Wang, Ting Wang, Jianhua Feng
Venue: KDD
Year: 2013

Location-based services have become widely available on mobile devices. Existing methods employ a pull model or user-initiated model, where a user issues a query to a server which replies with location-aware answers. To provide users with instant replies, a push model or server-initiated model is becoming an inevitable computing model in the next-generation location-based services. In the push model, subscribers register spatio-textual subscriptions to capture their interests, and publishers post spatio-textual messages. This calls for a high-performance location-aware publish/subscribe system to deliver publishers' messages to relevant subscribers.In this paper, we address the research challenges that arise in designing a location-aware publish/subscribe system. We propose an rtree based index structure by integrating textual descriptions into rtree nodes. We devise efficient filtering algorithms and develop effective pruning techniques to improve filtering efficiency. Experimental results show that our method achieves high performance. For example, our method can filter 500 tweets in a second for 10 million registered subscriptions on a commodity computer.
Summarizing probabilistic frequent patterns: a fast approach
Authors: Chunyang Liu, Ling Chen, Chengqi Zhang
Venue: KDD
Year: 2013

Mining probabilistic frequent patterns from uncertain data has received a great deal of attention in recent years due to the wide applications. However, probabilistic frequent pattern mining suffers from the problem that an exponential number of result patterns are generated, which seriously hinders further evaluation and analysis. In this paper, we focus on the problem of mining probabilistic representative frequent patterns (P-RFP), which is the minimal set of patterns with adequately high probability to represent all frequent patterns. Observing the bottleneck in checking whether a pattern can probabilistically represent another, which involves the computation of a joint probability of the supports of two patterns, we introduce a novel approximation of the joint probability with both theoretical and empirical proofs. Based on the approximation, we propose an Approximate P-RFP Mining (APM) algorithm, which effectively and efficiently compresses the set of probabilistic frequent patterns. To our knowledge, this is the first attempt to analyze the relationship between two probabilistic frequent patterns through an approximate approach. Our experiments on both synthetic and real-world datasets demonstrate that the APM algorithm accelerates P-RFP mining dramatically, orders of magnitudes faster than an exact solution. Moreover, the error rate of APM is guaranteed to be very small when the database contains hundreds transactions, which further affirms APM is a practical solution for summarizing probabilistic frequent patterns.