OpenRaiser/Intern-Atlas huggingface dataset:
Inferring distant-time location in low-sampling-rate trajectories
Authors: Meng-Fen Chiang, Yung-Hsiang Lin, Wen-Chih Peng, Philip S. Yu
Venue: KDD
Year: 2013

With the growth of location-based services and social services, low- sampling-rate trajectories from check-in data or photos with geo- tag information becomes ubiquitous. In general, most detailed mov- ing information in low-sampling-rate trajectories are lost. Prior works have elaborated on distant-time location prediction in high- sampling-rate trajectories. However, existing prediction models are pattern-based and thus not applicable due to the sparsity of data points in low-sampling-rate trajectories. To address the sparsity in low-sampling-rate trajectories, we develop a Reachability-based prediction model on Time-constrained Mobility Graph (RTMG) to predict locations for distant-time queries. Specifically, we de- sign an adaptive temporal exploration approach to extract effective supporting trajectories that are temporally close to the query time. Based on the supporting trajectories, a Time-constrained mobility Graph (TG) is constructed to capture mobility information at the given query time. In light of TG, we further derive the reacha- bility probabilities among locations in TG. Thus, a location with maximum reachability from the current location among all possi- ble locations in supporting trajectories is considered as the predic- tion result. To efficiently process queries, we proposed the index structure Sorted Interval-Tree (SOIT) to organize location records. Extensive experiments with real data demonstrated the effective- ness and efficiency of RTMG. First, RTMG with adaptive tempo- ral exploration significantly outperforms the existing pattern-based prediction model HPM [2] over varying data sparsity in terms of higher accuracy and higher coverage. Also, the proposed index structure SOIT can efficiently speedup RTMG in large-scale trajec- tory dataset. In the future, we could extend RTMG by considering more factors (e.g., staying durations in locations, application us- ages in smart phones) to further improve the prediction accuracy.
On community detection in real-world networks and the importance of degree assortativity
Authors: Marek Ciglan, Michal Laclavik, Kjetil Nørvåg
Venue: KDD
Year: 2013

Graph clustering, often addressed as community detection, is a prominent task in the domain of graph data mining with dozens of algorithms proposed in recent years. In this paper, we focus on several popular community detection algorithms with low computational complexity and with decent performance on the artificial benchmarks, and we study their behaviour on real-world networks. Motivated by the observation that there is a class of networks for which the community detection methods fail to deliver good community structure, we examine the assortativity coefficient of ground-truth communities and show that assortativity of a community structure can be very different from the assortativity of the original network. We then examine the possibility of exploiting the latter by weighting edges of a network with the aim to improve the community detection outputs for networks with assortative community structure. The evaluation shows that the proposed weighting can significantly improve the results of community detection methods on networks with assortative community structure.
Cascading outbreak prediction in networks: a data-driven approach
Authors: Peng Cui, Shifei Jin, Linyun Yu, Fei Wang, Wenwu Zhu, Shiqiang Yang
Venue: KDD
Year: 2013

Cascades are ubiquitous in various network environments such as epidemic networks, traffic networks, water distribution networks and social networks. The outbreaks of cascades will often bring bad or even devastating effects. How to accurately predict the cascading outbreaks in early stage is of paramount importance for people to avoid these bad effects. Although there have been some pioneering works on cascading outbreaks detection, how to predict, rather than detect, the cascading outbreaks is still an open problem. In this paper, we attempt harnessing historical cascade data, propose a novel data driven approach to select important nodes as sensors, and predict the outbreaks based on the cascading behaviors of these sensors. In particular, we propose Orthogonal Sparse LOgistic Regression (OSLOR) method to jointly optimize node selection and outbreak prediction, where the prediction loss are combined with an orthogonal regularizer and L1 regularizer to guarantee good prediction accuracy, as well as the sparsity and low-redundancy of selected sensors. We evaluate the proposed method on a real online social network dataset including 182.7 million information cascades. The experimental results show that the proposed OSLOR significantly and consistently outperform topological measure based method and other data driven methods in prediction performances.
AMETHYST: a system for mining and exploring topical hierarchies of heterogeneous data
Authors: Marina Danilevsky, Chi Wang, Fangbo Tao, Son Nguyen, Gong Chen, Nihit Desai, Lidan Wang, Jiawei Han
Venue: KDD
Year: 2013

In this demo we present AMETHYST, a system for exploring and analyzing a topical hierarchy constructed from a heterogeneous information network (HIN). HINs, composed of multiple types of entities and links are very common in the real world. Many have a text component, and thus can benefit from a high quality hierarchical organization of the topics in the network dataset. By organizing the topics into a hierarchy, AMETHYST helps understand search results in the context of an ontology, and explain entity relatedness at different granularities. The automatically constructed topical hierarchy reflects a domain-specific ontology, interacts with multiple types of linked entities, and can be tailored for both free text and OLAP queries.
Learning to question: leveraging user preferences for shopping advice
Authors: Mahashweta Das, Gianmarco De Francisci Morales, Aristides Gionis, Ingmar Weber
Venue: KDD
Year: 2013

We present ShoppingAdvisor, a novel recommender system that helps users in shopping for technical products. ShoppingAdvisor leverages both user preferences and technical product attributes in order to generate its suggestions. The system elicits user preferences via a tree-shaped flowchart, where each node is a question to the user. At each node, ShoppingAdvisor suggests a ranking of products matching the preferences of the user, and that gets progressively refined along the path from the tree's root to one of its leafs. In this paper we show (i) how to learn the structure of the tree, i.e., which questions to ask at each node, and (ii) how to produce a suitable ranking at each node. First, we adapt the classical top-down strategy for building decision trees in order to find the best user attribute to ask at each node. Differently from decision trees, ShoppingAdvisor partitions the user space rather than the product space. Second, we show how to employ a learning-to-rank approach in order to learn, for each node of the tree, a ranking of products appropriate to the users who reach that node. We experiment with two real-world datasets for cars and cameras, and a synthetic one. We use mean reciprocal rank to evaluate ShoppingAdvisor, and show how the performance increases by more than 50% along the path from root to leaf. We also show how collaborative recommendation algorithms such as k-nearest neighbor benefits from feature selection done by the ShoppingAdvisor tree. Our experiments show that ShoppingAdvisor produces good quality interpretable recommendations, while requiring less input from users and being able to handle the cold-start problem.
Network discovery via constrained tensor analysis of fMRI data
Authors: Ian N. Davidson, Sean Gilpin, Owen T. Carmichael, Peter B. Walker
Venue: KDD
Year: 2013

We pose the problem of network discovery which involves simplifying spatio-temporal data into cohesive regions (nodes) and relationships between those regions (edges). Such problems naturally exist in fMRI scans of human subjects. These scans consist of activations of thousands of voxels over time with the aim to simplify them into the underlying cognitive network being used. We propose supervised and semi-supervised variations of this problem and postulate a constrained tensor decomposition formulation and a corresponding alternating least squares solver that is easy to implement. We show this formulation works well in controlled experiments where supervision is incomplete, superfluous and noisy and is able to recover the underlying ground truth network. We then show that for real fMRI data our approach can reproduce well known results in neurology regarding the default mode network in resting-state healthy and Alzheimer affected individuals. Finally, we show that the reconstruction error of the decomposition provides a useful measure of the network strength and is useful at predicting key cognitive scores both by itself and with clinical information.
Estimating building simulation parameters via Bayesian structure learning
Authors: Richard E. Edwards, Joshua R. New, Lynne E. Parker
Venue: KDD
Year: 2013

Many key building design policies are made using sophisticated computer simulations such as EnergyPlus (E+), the DOE flagship whole-building energy simulation engine. E+ and other sophisticated computer simulations have several major problems. The two main issues are 1) gaps between the simulation model and the actual structure, and 2) limitations of the modeling engine's capabilities. Currently, these problems are addressed by having an engineer manually calibrate simulation parameters to real world data or using algorithmic optimization methods to adjust the building parameters. However, some simulations engines, like E+, are computationally expensive, which makes repeatedly evaluating the simulation engine costly. This work explores addressing this issue by automatically discovering the simulation's internal input and output dependencies from ~20 Gigabytes of E+ simulation data, future extensions will use ~200 Terabytes of E+ simulation data. The model is validated by inferring building parameters for E+ simulations with ground truth building parameters. Our results indicate that the model accurately represents parameter means with some deviation from the means, but does not support inferring parameter values that exist on the distribution's tail.
KDD Cup 2013 - author-paper identification challenge: second place team
Authors: Dmitry Efimov, Lucas Silva, Benjamin Solecki
Venue: KDD
Year: 2013

This paper describes our submission to the KDD Cup 2013 Track 1 Challenge: Author-Paper Indentification in the Microsoft Academic Search database. Our approach is based on Gradient Boosting Machine (GBM) of Friedman ([5]) and deep feature engineering. The method was second in the final standings with Mean Average Precision (MAP) of 0.98144, while the winning submission scored 0.98259.
Information cascade at group scale
Authors: Milad Eftekhar, Yashar Ganjali, Nick Koudas
Venue: KDD
Year: 2013

Identifying the k most influential individuals in a social network is a well-studied problem. The objective is to detect k individuals in a (social) network who will influence the maximum number of people, if they are independently convinced of adopting a new strategy (product, idea, etc). There are cases in real life, however, where we aim to instigate groups instead of individuals to trigger network diffusion. Such cases abound, e.g., billboards, TV commercials and newspaper ads are utilized extensively to boost the popularity and raise awareness.
A data mining driven risk profiling method for road asset management
Authors: Daniel Emerson, Justin Weligamage, Richi Nayak
Venue: KDD
Year: 2013

Road surface skid resistance has been shown to have a strong relationship to road crash risk, however, applying the current method of using investigatory levels to identify crash prone roads is problematic as they may fail in identifying risky roads outside of the norm. The proposed method analyses a complex and formerly impenetrable volume of data from roads and crashes using data mining. This method rapidly identifies roads with elevated crash-rate, potentially due to skid resistance deficit, for investigation. A hypothetical skid resistance/crash risk curve is developed for each road segment, driven by the model deployed in a novel regression tree extrapolation method. The method potentially solves the problem of missing skid resistance values which occurs during network-wide crash analysis, and allows risk assessment of the major proportion of roads without skid resistance values.
Repetition-aware content placement in navigational networks
Authors: Dóra Erdös, Vatche Ishakian, Azer Bestavros, Evimaria Terzi
Venue: KDD
Year: 2013

Arguably, the most effective technique to ensure wide adoption of a concept (or product) is by repeatedly exposing individuals to messages that reinforce the concept (or promote the product). Recognizing the role of repeated exposure to a message, in this paper we propose a novel framework for the effective placement of content: Given the navigational patterns of users in a network, e.g., web graph, hyperlinked corpus, or road network, and given a model of the relationship between content-adoption and frequency of exposition, we define the repetition-aware content-placement (RACP) problem as that of identifying the set of B nodes on which content should be placed so that the expected number of users adopting that content is maximized. The key contribution of our work is the introduction of memory into the navigation process, by making user conversion dependent on the number of her exposures to that content. This dependency is captured using a conversion model that is general enough to capture arbitrary dependencies. Our solution to this general problem builds upon the notion of absorbing random walks, which we extend appropriately in order to address the technicalities of our definitions. Although we show the RACP problem to be NP-hard, we propose a general and efficient algorithmic solution. Our experimental results demonstrate the efficacy and the efficiency of our methods in multiple real-world datasets obtained from different application domains.
To buy or not to buy: that is the question
Authors: Oren Etzioni
Venue: KDD
Year: 2013

Shopping can be decomposed into three basic questions: what, where, and when to buy? In this talk, I'll describe how we utilize advanced data-mining and text-mining techniques at Decide.com (and earlier at Farecast) to solve these problems for on-line shoppers. Our algorithms have predicted prices utilizing billions of data points, and ranked products based on millions of reviews.
Heat pump detection from coarse grained smart meter data with positive and unlabeled learning
Authors: Hongliang Fei, Younghun Kim, Sambit Sahu, Milind R. Naphade, Sanjay K. Mamidipalli, John Hutchinson
Venue: KDD
Year: 2013

Recent advances in smart metering technology enable utility companies to have access to tremendous amount of smart meter data, from which the utility companies are eager to gain more insight about their customers. In this paper, we aim to detect electric heat pumps from coarse grained smart meter data for a heat pump marketing campaign. However, appliance detection is a challenging task, especially given a very low granularity and partial labeled even unlabeled data. Traditional methods install either a high granularity smart meter or sensors at every appliance, which is either too expensive or requires technical expertise. We propose a novel approach to detect heat pumps that utilizes low granularity smart meter data, prior sales data and weather data. In particular, motivated by the characteristics of heat pump consumption pattern, we extract novel features that are highly relevant to heat pump usage from smart meter data and weather data. Under the constraint that only a subset of heat pump users are available, we formalize the problem into a positive and unlabeled data classification and apply biased Support Vector Machine (BSVM) to our extracted features. Our empirical study on a real-world data set demonstrates the effectiveness of our method. Furthermore, our method has been deployed in a real-life setting where the partner electric company runs a targeted campaign for 292,496 customers. Based on the initial feedback, our detection algorithm can successfully detect substantial number of non-heat pump users who were identified heat pump users with the prior algorithm the company had used.
Why people hate your app: making sense of user feedback in a mobile app store
Authors: Bin Fu, Jialiu Lin, Lei Li, Christos Faloutsos, Jason I. Hong, Norman M. Sadeh
Venue: KDD
Year: 2013

User review is a crucial component of open mobile app markets such as the Google Play Store. How do we automatically summarize millions of user reviews and make sense out of them? Unfortunately, beyond simple summaries such as histograms of user ratings, there are few analytic tools that can provide insights into user reviews. In this paper, we propose Wiscom, a system that can analyze tens of millions user ratings and comments in mobile app markets at three different levels of detail. Our system is able to (a) discover inconsistencies in reviews; (b) identify reasons why users like or dislike a given app, and provide an interactive, zoomable view of how users' reviews evolve over time; and (c) provide valuable insights into the entire app market, identifying users' major concerns and preferences of different types of apps. Results using our techniques are reported on a 32GB dataset consisting of over 13 million user reviews of 171,493 Android apps in the Google Play Store. We discuss how the techniques presented herein can be deployed to help a mobile app market operator such as Google as well as individual app developers and end-users.
A data-driven method for in-game decision making in MLB: when to pull a starting pitcher
Authors: Gartheeban Ganeshapillai, John V. Guttag
Venue: KDD
Year: 2013

Professional sports is a roughly $500 billion dollar industry that is increasingly data-driven. In this paper we show how machine learning can be applied to generate a model that could lead to better on-field decisions by managers of professional baseball teams. Specifically we show how to use regularized linear regression to learn pitcher-specific predictive models that can be used to help decide when a starting pitcher should be replaced. A key step in the process is our method of converting categorical variables (e.g., the venue in which a game is played) into continuous variables suitable for the regression. Another key step is dealing with situations in which there is an insufficient amount of data to compute measures such as the effectiveness of a pitcher against specific batters.
Adaptive adversaries: building systems to fight fraud and cyber intruders
Authors: Ari Gesher
Venue: KDD
Year: 2013

Statistical machine learning / knowledge discovery techniques tend to fail when faced with an adaptive adversary attempting to evade detection in the data. Humans do an excellent job of correctly spotting adaptive adversaries given a good way to digest the data. On the other hand, humans are glacially slow and error-prone when it comes to moving through very large volumes of data, a task best left to the machines. Fighting complex fraud and cyber-security threats requires a symbiosis between the computers and teams of human analysts. The computers use algorithmic analysis, heuristics, and/or statistical characterization to find interesting 'simple' patterns in the data. These candidate events are then queued for in-depth human analysis in rich, expressive, interactive analysis environments.
Entity resolution for big data
Authors: Lise Getoor, Ashwin Machanavajjhala
Venue: KDD
Year: 2013

Entity resolution (ER), the problem of extracting, matching and resolving entity mentions in structured and unstructured data, is a long-standing challenge in database management, information retrieval, machine learning, natural language processing and statistics. Accurate and fast entity resolution has huge practical implications in a wide variety of commercial, scientific and security domains. Despite the long history of work on entity resolution, there is still a surprising diversity of approaches, and lack of guiding theory. Meanwhile, in the age of big data, the need for high quality entity resolution is growing, as we are inundated with more and more data, all of which needs to be integrated, aligned and matched, before further utility can be extracted. In this tutorial, we bring together perspectives on entity resolution from a variety of fields, including databases, information retrieval, natural language processing and machine learning, to provide, in one setting, a survey of a large body of work. We discuss both the practical aspects and theoretical underpinnings of ER. We describe existing solutions, current challenges and open research problems. In addition to giving attendees a thorough understanding of existing ER models, algorithms and evaluation methods, the tutorial will cover important research topics such as scalable ER, active and lightly supervised ER, and query-driven ER.
Network sampling
Authors: Lise Getoor, Ashwin Machanavajjhala
Venue: KDD
Year: 2013

Network data appears in various domains, including social, communication, and information sciences. Analysis of such data is crucial for making inferences and predictions about these networks, and moreover, for understanding the different processes that drive their evolution. However, a major bottleneck to perform such an analysis is the massive size of real-life networks, which makes modeling and analyzing these networks simply infeasible. Further, many networks, specifically those that belong to social and communication domains, are not visible to the public due to privacy concerns, and other networks, such as the Web, are only accessible via crawling. Therefore, to overcome the above challenges, researchers use network sampling overwhelmingly as a key statistical approach to select a sub-population of interest that can be studied thoroughly.
Targeting and influencing at scale: from presidential elections to social good
Authors: Rayid Ghani
Venue: KDD
Year: 2013

If you're still recovering from the barrage of ads, news, emails, Facebook posts, and newspaper articles that were giving you the latest poll numbers, asking you to volunteer, donate money, and vote, this talk will give you a look behind the scenes on why you were seeing what you were seeing. I will talk about how machine learning and data mining along with randomized experiments were used to target and influence tens of millions of people. Beyond the presidential elections, these methodologies for targeting and influence have the power to solve big problems in education, healthcare, energy, transportation, and related areas. I will talk about some recent work we're doing at the University of Chicago Data Science for Social Good summer fellowship program working with non-profits and government organizations to tackle some of these challenges.