OpenRaiser/Intern-Atlas huggingface dataset:
Network mining and analysis for social applications
Authors: Feida Zhu, Huan Sun, Xifeng Yan
Venue: KDD
Year: 2014

The recent blossom of social network and communication services in both public and corporate settings have generated a staggering amount of network data of all kinds. Unlike the bio-networks and the chemical compound graph data often used in traditional network mining and analysis, the new network data grown out of the social applications are characterized by their rich attributes, high heterogeneity, enormous sizes and complex patterns of various semantic meanings, all of which have posed significant research challenges to the graph/network mining community. In this tutorial, we aim to examine some recent advances in network mining and analysis for social applications, covering a diverse collection of methodologies and applications from the perspectives of event, relationship, collaboration, and network pattern. We would present the problem settings, the challenges, the recent research advances and some future directions for each perspective. Topics include but are not limited to correlation mining, iceberg finding, anomaly detection, relationship discovery, information flow, task routing, and pattern mining.
Towards scalable critical alert mining
Authors: Bo Zong, Yinghui Wu, Jie Song, Ambuj K. Singh, Hasan Çam, Jiawei Han, Xifeng Yan
Venue: KDD
Year: 2014

Performance monitor software for data centers typically generates a great number of alert sequences. These alert sequences indicate abnormal network events. Given a set of observed alert sequences, it is important to identify the most critical alerts that are potentially the causes of others. While the need for mining critical alerts over large scale alert sequences is evident, most alert analysis techniques stop at modeling and mining the causal relations among the alerts. This paper studies the critical alert mining problem: Given a set of alert sequences, we aim to find a set of k critical alerts such that the number of alerts potentially triggered by them is maximized. We show that the problem is intractable; therefore, we resort to approximation and heuristic algorithms. First, we develop an approximation algorithm that obtains a near-optimal alert set in quadratic time, and propose pruning techniques to improve its runtime performance. Moreover, we show a faster approximation exists, when the alerts follow certain causal structure. Second, we propose two fast heuristic algorithms based on tree sampling techniques. On real-life data, these algorithms identify a critical alert from up to 270,000 mined causal relations in 5 seconds; meanwhile, they preserve more than 80% of solution quality, and are up to 5,000 times faster than their approximation counterparts.
Representative clustering of uncertain data
Authors: Andreas Züfle, Tobias Emrich, Klaus Arthur Schmid, Nikos Mamoulis, Arthur Zimek, Matthias Renz
Venue: KDD
Year: 2014

This paper targets the problem of computing meaningful clusterings from uncertain data sets. Existing methods for clustering uncertain data compute a single clustering without any indication of its quality and reliability; thus, decisions based on their results are questionable. In this paper, we describe a framework, based on possible-worlds semantics; when applied on an uncertain dataset, it computes a set of representative clusterings, each of which has a probabilistic guarantee not to exceed some maximum distance to the ground truth clustering, i.e., the clustering of the actual (but unknown) data. Our framework can be combined with any existing clustering algorithm and it is the first to provide quality guarantees about its result. In addition, our experimental evaluation shows that our representative clusterings have a much smaller deviation from the ground truth clustering than existing approaches, thus reducing the effect of uncertainty.
Scaling Machine Learning and Statistics for Web Applications
Authors: Deepak Agarwal
Venue: KDD
Year: 2015

Scaling web applications like recommendation systems, search and computational advertising is challenging. Such systems have to make astronomical number of decisions every day on what to serve users when they are visiting the website and/or using the mobile app. Machine learning and statistical modeling approaches that can obtain insights by continuously processing large amounts of data emitted at very high frequency by these applications have emerged as the method of choice. However, there are three challenges to scale such methods : a) scientific b) infrastructure and c) organizational. I will provide an overview of these challenges and the strategies we have adopted at LinkedIn to address those. Throughout, I will illustrate with examples from real-world applications at LinkedIn.
Whither Social Networks for Web Search?
Authors: Rakesh Agrawal, Behzad Golshan, Evangelos E. Papalexakis
Venue: KDD
Year: 2015

Access to diverse perspectives nurtures an informed citizenry. Google and Bing have emerged as the duopoly that largely arbitrates which English language documents are seen by web searchers. A recent study shows that there is now a large overlap in the top organic search results produced by them. Thus, citizens may no longer be able to gain different perspectives by using different search engines.
TimeMachine: Timeline Generation for Knowledge-Base Entities
Authors: Tim Althoff, Xin Luna Dong, Kevin Murphy, Safa Alai, Van Dang, Wei Zhang
Venue: KDD
Year: 2015

We present a method called TIMEMACHINE to generate a timeline of events and relations for entities in a knowledge base. For example for an actor, such a timeline should show the most important professional and personal milestones and relationships such as works, awards, collaborations, and family relationships. We develop three orthogonal timeline quality criteria that an ideal timeline should satisfy: (1) it shows events that are relevant to the entity; (2) it shows events that are temporally diverse, so they distribute along the time axis, avoiding visual crowding and allowing for easy user interaction, such as zooming in and out; and (3) it shows events that are content diverse, so they contain many different types of events (e.g., for an actor, it should show movies and marriages and awards, not just movies). We present an algorithm to generate such timelines for a given time period and screen size, based on submodular optimization and web-co-occurrence statistics with provable performance guarantees. A series of user studies using Mechanical Turk shows that all three quality criteria are crucial to produce quality timelines and that our algorithm significantly outperforms various baseline and state-of-the-art methods.
Exploiting Data Mining for Authenticity Assessment and Protection of High-Quality Italian Wines from Piedmont
Authors: Marco Arlorio, Jean Daniel Coïsson, Giorgio Leonardi, Monica Locatelli, Luigi Portinale
Venue: KDD
Year: 2015

This paper discusses the data mining approach followed in a project called TRAQUASwine, aimed at the definition of methods for data analytical assessment of the authenticity and protection, against fake versions, of some of the highest value Nebbiolo-based wines from Piedmont region in Italy. This is a big issue in the wine market, where commercial frauds related to such a kind of products are estimated to be worth millions of Euros. The objective is twofold: to show that the problem can be addressed without expensive and hyper-specialized wine analyses, and to demonstrate the actual usefulness of classification algorithms for data mining on the resulting chemical profiles. Following Wagstaff's proposal for practical exploitation of machine learning (and data mining) approaches, we describe how data have been collected and prepared for the production of different datasets, how suitable classification models have been identified and how the interpretation of the results suggests the emergence of an active role of classification techniques, based on standard chemical profiling, for the assesment of the authenticity of the wines target of the study.
Hadoop's Impact on the Future of Data Management
Authors: Amr Awadallah
Venue: KDD
Year: 2015

As Hadoop and the surrounding projects & vendors mature, their impact on the data management sector is growing. Amr will talk about his views on how that impact will change over the next five years. How central will Hadoop be to the data center of 2020? What industries will benefit most? Which technologies are at risk of displacement or encroachment?
Predictive Approaches for Low-Cost Preventive Medicine Program in Developing Countries
Authors: Yukino Baba, Hisashi Kashima, Yasunobu Nohara, Eiko Kai, Partha Pratim Ghosh, Rafiqul Islam Maruf, Ashir Ahmed, Masahiro Kuroda, Sozo Inoue, Tatsuo Hiramatsu, Michio Kimura, Shuji Shimizu, Kunihisa Kobayashi, Koji Tsuda, Masashi Sugiyama, Mathieu Blondel, Naonori Ueda, Masaru Kitsuregawa, Naoki Nakashima
Venue: KDD
Year: 2015

Non-communicable diseases (NCDs) are no longer just a problem for high-income countries, but they are also a problem that affects developing countries. Preventive medicine is definitely the key to combat NCDs; however, the cost of preventive programs is a critical issue affecting the popularization of these medicine programs in developing countries. In this study, we investigate predictive modeling for providing a low-cost preventive medicine program. In our two-year-long field study in Bangladesh, we collected the health checkup results of 15,075 subjects, the data of 6,607 prescriptions, and the follow-up examination results of 2,109 subjects. We address three prediction problems, namely subject risk prediction, drug recommendation, and future risk prediction, by using machine learning techniques; our multiple-classifier approach successfully reduced the costs of health checkups, a multi-task learning method provided accurate recommendation for specific types of drugs, and an active learning method achieved an efficient assignment of healthcare workers for the follow-up care of subjects.
Dynamically Modeling Patient's Health State from Electronic Medical Records: A Time Series Approach
Authors: Karla L. Caballero Barajas, Ram Akella
Venue: KDD
Year: 2015

In this paper, we present a method to dynamically estimate the probability of mortality inside the Intensive Care Unit (ICU) by combining heterogeneous data. We propose a method based on Generalized Linear Dynamic Models that models the probability of mortality as a latent state that evolves over time. This framework allows us to combine different types of features (lab results, vital signs readings, doctor and nurse notes, etc) into a single state, which is updated each time new patient data is observed. In addition, we include the use of text features, based on medical noun phrase extraction and Statistical Topic Models. These features provide context about the patient that cannot be captured when only numerical features are used. We fill out the missing values using a Regularized Expectation Maximization based method assuming temporal data. We test our proposed approach using 15,000 Electronic Medical Records (EMRs) obtained from the MIMIC II public dataset. Experimental results show that the proposed model allows us to detect an increase in the probability of mortality before it occurs. We report an AUC 0.8657. Our proposed model clearly outperforms other methods of the literature in terms of sensitivity with 0.7885 compared to 0.6559 of Naive Bayes and F-score with 0.5929 compared to 0.4662 of Apache III score after 24 hours.
Web Personalization and Recommender Systems
Authors: Shlomo Berkovsky, Jill Freyne
Venue: KDD
Year: 2015

The quantity of accessible information has been growing rapidly and far exceeded human processing capabilities. The sheer abundance of information often prevents users from discovering the desired information, or aggravates making informed and correct choices. This highlights the pressing need for intelligent personalized applications that simplify information access and discovery by taking into account users' preferences and needs. One type of personalized application that has recently become tremendously popular in research and industry is recommender systems. These provide to users personalized recommendations about information and products they may be interested to examine or purchase. Extensive research into recommender systems has yielded a variety of techniques, which have been published at a variety of conferences and adopted by numerous Web-sites. This tutorial will provide the participants with broad overview and thorough understanding of algorithms and practically deployed Web and mobile applications of personalized technologies.
Graph-Based User Behavior Modeling: From Prediction to Fraud Detection
Authors: Alex Beutel, Leman Akoglu, Christos Faloutsos
Venue: KDD
Year: 2015

How can we model users' preferences? How do anomalies, fraud, and spam effect our models of normal users? How can we modify our models to catch fraudsters? In this tutorial we will answer these questions - connecting graph analysis tools for user behavior modeling to anomaly and fraud detection. In particular, we will focus on the application of subgraph analysis, label propagation, and latent factor models to static, evolving, and attributed graphs. For each of these techniques we will give a brief explanation of the algorithms and the intuition behind them. We will then give examples of recent research using the techniques to model, understand and predict normal behavior. With this intuition for how these methods are applied to graphs and user behavior, we will focus on state-of-the-art research showing how the outcomes of these methods are effected by fraud, and how they have been used to catch fraudsters.
Multi-View Incident Ticket Clustering for Optimal Ticket Dispatching
Authors: Mirela Madalina Botezatu, Jasmina Bogojeska, Ioana Giurgiu, Hagen Völzer, Dorothea Wiesmann
Venue: KDD
Year: 2015

We present a novel technique that optimizes the dispatching of incident tickets to the agents in an IT Service Support Environment. Unlike the common skill-based dispatching, our approach also takes empirical evidence on the agent's speed from historical data into account. Our solution consists of two parts. First, a novel technique clusters historic tickets into incident categories that are discriminative in terms of agent's performance. Second, a dispatching policy selects, for an incoming ticket, the fastest available agent according to the target cluster. We show that, for ticket data collected from several Service Delivery Units, our new dispatching technique can reduce service time between $35\%$ and $44\%$.
BatchRank: A Novel Batch Mode Active Learning Framework for Hierarchical Classification
Authors: Shayok Chakraborty, Vineeth Nallure Balasubramanian, Adepu Ravi Sankar, Sethuraman Panchanathan, Jieping Ye
Venue: KDD
Year: 2015

Active learning algorithms automatically identify the salient and exemplar instances from large amounts of unlabeled data and thus reduce human annotation effort in inducing a classification model. More recently, Batch Mode Active Learning (BMAL) techniques have been proposed, where a batch of data samples is selected simultaneously from an unlabeled set. Most active learning algorithms assume a flat label space, that is, they consider the class labels to be independent. However, in many applications, the set of class labels are organized in a hierarchical tree structure, with the leaf nodes as outputs and the internal nodes as clusters of outputs at multiple levels of granularity. In this paper, we propose a novel BMAL algorithm (BatchRank) for hierarchical classification. The sample selection is posed as an NP-hard integer quadratic programming problem and a convex relaxation (based on linear programming) is derived, whose solution is further improved by an iterative truncated power method. Finally, a deterministic bound is established on the quality of the solution. Our empirical results on several challenging, real-world datasets from multiple domains, corroborate the potential of the proposed framework for real-world hierarchical classification applications.
Heterogeneous Network Embedding via Deep Architectures
Authors: Shiyu Chang, Wei Han, Jiliang Tang, Guo-Jun Qi, Charu C. Aggarwal, Thomas S. Huang
Venue: KDD
Year: 2015

Data embedding is used in many machine learning applications to create low-dimensional feature representations, which preserves the structure of data points in their original space. In this paper, we examine the scenario of a heterogeneous network with nodes and content of various types. Such networks are notoriously difficult to mine because of the bewildering combination of heterogeneous contents and structures. The creation of a multidimensional embedding of such data opens the door to the use of a wide variety of off-the-shelf mining techniques for multidimensional data. Despite the importance of this problem, limited efforts have been made on embedding a network of scalable, dynamic and heterogeneous data. In such cases, both the content and linkage structure provide important cues for creating a unified feature representation of the underlying network. In this paper, we design a deep embedding algorithm for networked data. A highly nonlinear multi-layered embedding function is used to capture the complex interactions between the heterogeneous data in a network. Our goal is to create a multi-resolution deep embedding function, that reflects both the local and global network structures, and makes the resulting embedding useful for a variety of data mining tasks. In particular, we demonstrate that the rich content and linkage information in a heterogeneous network can be captured by such an approach, so that similarities among cross-modal data can be measured directly in a common embedding space. Once this goal has been achieved, a wide variety of data mining problems can be solved by applying off-the-shelf algorithms designed for handling vector representations. Our experiments on real-world network datasets show the effectiveness and scalability of the proposed algorithm as compared to the state-of-the-art embedding methods.
Optimal Kernel Group Transformation for Exploratory Regression Analysis and Graphics
Authors: Pan Chao, Qiming Huang, Michael Zhu
Venue: KDD
Year: 2015

The general goal of multivariate regression analysis is to infer about the relationship between a response variable Y and a predictor vector X. Many popularly used regression methods only focus on specific aspects of this relationship. Even though the conditional distribution P(Y|X) of Y given X fully characterizes the relationship between Y and X, estimation of the conditional density is challenging and becomes quickly infeasible when the dimension of X increases. In this article, we propose to use optimal group transformations as a general approach for exploring the relationship between Y and X. This approach can be considered an automatic procedure to identify the best characteristic of P(Y|X) under which the relationship between Y and X can be fully explored. The emphasis on using group transformations allows the approach to recover intrinsic group structures among the predictors. Furthermore, we develop kernel methods for estimating the optimal group transformations based on cross-covariance and conditional covariance operators. The statistical consistency of the estimates has been established. We refer to the proposed framework and approach as the Optimal Kernel Group Transformation (OKGT) method. Simulation study and real data applications show that the OKGT method is flexible and powerful for the general purpose of high dimensional regression analysis.
Differentially Private High-Dimensional Data Publication via Sampling-Based Inference
Authors: Rui Chen, Qian Xiao, Yu Zhang, Jianliang Xu
Venue: KDD
Year: 2015

Releasing high-dimensional data enables a wide spectrum of data mining tasks. Yet, individual privacy has been a major obstacle to data sharing. In this paper, we consider the problem of releasing high-dimensional data with differential privacy guarantees. We propose a novel solution to preserve the joint distribution of a high-dimensional dataset. We first develop a robust sampling-based framework to systematically explore the dependencies among all attributes and subsequently build a dependency graph. This framework is coupled with a generic threshold mechanism to significantly improve accuracy. We then identify a set of marginal tables from the dependency graph to approximate the joint distribution based on the solid inference foundation of the junction tree algorithm while minimizing the resultant error. We prove that selecting the optimal marginals with the goal of minimizing error is NP-hard and, thus, design an approximation algorithm using an integer programming relaxation and the constrained concave-convex procedure. Extensive experiments on real datasets demonstrate that our solution substantially outperforms the state-of-the-art competitors.
Adaptation Algorithm and Theory Based on Generalized Discrepancy
Authors: Corinna Cortes, Mehryar Mohri, Andres Muñoz Medina
Venue: KDD
Year: 2015

We present a new algorithm for domain adaptation improving upon the discrepancy minimization algorithm (DM), which was previously shown to outperform a number of popular algorithms designed for this task. Unlike most previous approaches adopted for domain adaptation, our algorithm does not consist of a fixed reweighting of the losses over the training sample. Instead, it uses a reweighting that depends on the hypothesis considered and is based on the minimization of a new measure of generalized discrepancy. We give a detailed description of our algorithm and show that it can be formulated as a convex optimization problem. We also present a detailed theoretical analysis of its learning guarantees, which helps us select its parameters. Finally, we report the results of experiments demonstrating that it improves upon the DM algorithm in several tasks.
Should You Trust Your Money to a Robot?
Authors: Vasant Dhar
Venue: KDD
Year: 2015

Computers are making more and more decisions for us, and increasingly so in areas that require human judgment. There is a palpable increase in machine intelligence across the touch points of our lives, driven by the proliferation of data feeding into intelligent algorithms capable of learning useful patterns and acting on them. A natural question to ask is how we should be thinking about the role of computers in managing our money. Should we trust our money to a robot? In an era of big data and machines to make sense of it all, do machines have an inherent advantage over humans? There is a surge of interest in Artificial Intelligence for financial prediction. Should we pay attention? Or is this an area where human judgment and input is always essential?