OpenRaiser/Intern-Atlas huggingface dataset:
Large-Scale Learnable Graph Convolutional Networks
Authors: Hongyang Gao, Zhengyang Wang, Shuiwang Ji
Venue: KDD
Year: 2018

Convolutional neural networks (CNNs) have achieved great success on grid-like data such as images, but face tremendous challenges in learning from more generic data such as graphs. In CNNs, the trainable local filters enable the automatic extraction of high-level features. The computation with filters requires a fixed number of ordered units in the receptive fields. However, the number of neighboring units is neither fixed nor are they ordered in generic graphs, thereby hindering the applications of convolutional operations. Here, we address these challenges by proposing the learnable graph convolutional layer (LGCL). LGCL automatically selects a fixed number of neighboring nodes for each feature based on value ranking in order to transform graph data into grid-like structures in 1-D format, thereby enabling the use of regular convolutional operations on generic graphs. To enable model training on large-scale graphs, we propose a sub-graph training method to reduce the excessive memory and computational resource requirements suffered by prior methods on graph convolutions. Our experimental results on node classification tasks in both transductive and inductive learning settings demonstrate that our methods can achieve consistently better performance on the Cora, Citeseer, Pubmed citation network, and protein-protein interaction network datasets. Our results also indicate that the proposed methods using sub-graph training strategy are more efficient as compared to prior approaches.
Accelerating Large-Scale Data Analysis by Offloading to High-Performance Computing Libraries using Alchemist
Authors: Alex Gittens, Kai Rothauge, Shusen Wang, Michael W. Mahoney, Lisa Gerhardt, Prabhat, Jey Kottalam, Michael F. Ringenburg, Kristyn J. Maschhoff
Venue: KDD
Year: 2018

Apache Spark is a popular system aimed at the analysis of large data sets, but recent studies have shown that certain computations---in particular, many linear algebra computations that are the basis for solving common machine learning problems---are significantly slower in Spark than when done using libraries written in a high-performance computing framework such as the Message-Passing Interface (MPI). To remedy this, we introduce Alchemist, a system designed to call MPI-based libraries from Apache Spark. Using Alchemist with Spark helps accelerate linear algebra, machine learning, and related computations, while still retaining the benefits of working within the Spark environment. We discuss the motivation behind the development of Alchemist, and we provide a brief overview of its design and implementation. We also compare the performances of pure Spark implementations with those of Spark implementations that leverage MPI-based codes via Alchemist. To do so, we use data science case studies: a large-scale application of the conjugate gradient method to solve very large linear systems arising in a speech classification problem, where we see an improvement of an order of magnitude; and the truncated singular value decomposition (SVD) of a 400GB three-dimensional ocean temperature data set, where we see a speedup of up to 7.9x. We also illustrate that the truncated SVD computation is easily scalable to terabyte-sized data by applying it to data sets of sizes up to 17.6TB.
When Sentiment Analysis Meets Social Network: A Holistic User Behavior Modeling in Opinionated Data
Authors: Lin Gong, Hongning Wang
Venue: KDD
Year: 2018

User modeling is critical for understanding user intents, while it is also challenging as user intents are so diverse and not directly observable. Most existing works exploit specific types of behavior signals for user modeling, e.g., opinionated data or network structure; but the dependency among different types of user-generated data is neglected. We focus on self-consistence across multiple modalities of user-generated data to model user intents. A probabilistic generative model is developed to integrate two companion learning tasks of opinionated content modeling and social network structure modeling for users. Individual users are modeled as a mixture over the instances of paired learning tasks to realize their behavior heterogeneity, and the tasks are clustered by sharing a global prior distribution to capture the homogeneity among users. Extensive experimental evaluations on large collections of Amazon and Yelp reviews with social network structures confirm the effectiveness of the proposed solution. The learned user models are interpretable and predictive: they enable more accurate sentiment classification and item/friend recommendations than the corresponding baselines that only model a singular type of user behaviors.
LARC: Learning Activity-Regularized Overlapping Communities Across Time
Authors: Alexander Gorovits, Ekta Gujral, Evangelos E. Papalexakis, Petko Bogdanov
Venue: KDD
Year: 2018

Communities are essential building blocks of complex networks enjoying significant research attention in terms of modeling and detection algorithms. Common across models is the premise that node pairs that share communities are likely to interact more strongly. Moreover, in the most general setting a node may be a member of multiple communities, and thus, interact with more than one cohesive group of other nodes. If node interactions are observed over a long period and aggregated into a single static network, the communities may be hard to discern due to their in-network overlap. Alternatively, if interactions are observed over short time periods, the communities may be only partially observable. How can we detect communities at an appropriate temporal resolution that resonates with their natural periods of activity? We propose LARC, a general framework for joint learning of the overlapping community structure and the periods of activity of communities, directly from temporal interaction data. We formulate the problem as an optimization task coupling community fit and smooth temporal activation over time. To the best of our knowledge, the tensor version of LARC is the first tensor-based community detection method to introduce such smoothness constraints. We propose efficient algorithms for the problem, achieving a $2.6x$ quality improvement over all baselines for high temporal resolution datasets, and consistently detecting better-quality communities for different levels of data aggregation and varying community overlap. In addition, LARC elucidates interpretable temporal patterns of community activity corresponding to botnet attacks, transportation change points and public forum interaction trends, while being computationally practical---few minutes on large real datasets. Finally, LARC provides a comprehensive \em unsupervised parameter estimation methodology yielding high accuracy and rendering it easy-to-use for practitioners.
Real-time Personalization using Embeddings for Search Ranking at Airbnb
Authors: Mihajlo Grbovic, Haibin Cheng
Venue: KDD
Year: 2018

Search Ranking and Recommendations are fundamental problems of crucial interest to major Internet companies, including web search engines, content publishing websites and marketplaces. However, despite sharing some common characteristics a one-size-fits-all solution does not exist in this space. Given a large difference in content that needs to be ranked, personalized and recommended, each marketplace has a somewhat unique challenge. Correspondingly, at Airbnb, a short-term rental marketplace, search and recommendation problems are quite unique, being a two-sided marketplace in which one needs to optimize for host and guest preferences, in a world where a user rarely consumes the same item twice and one listing can accept only one guest for a certain set of dates. In this paper we describe Listing and User Embedding techniques we developed and deployed for purposes of Real-time Personalization in Search Ranking and Similar Listing Recommendations, two channels that drive 99% of conversions. The embedding models were specifically tailored for Airbnb marketplace, and are able to capture guest's short-term and long-term interests, delivering effective home listing recommendations. We conducted rigorous offline testing of the embedding models, followed by successful online tests before fully deploying them into production.
New Incremental Learning Algorithm for Semi-Supervised Support Vector Machine
Authors: Bin Gu, Xiao-Tong Yuan, Songcan Chen, Heng Huang
Venue: KDD
Year: 2018

Semi-supervised learning is especially important in data mining applications because it can make use of plentiful unlabeled data to train the high-quality learning models. Semi-Supervised Support Vector Machine (S3VM) is a powerful semi-supervised learning model. However, the high computational cost and non-convexity severely impede the S3VM method in large-scale applications. Although several learning algorithms were proposed for S3VM, scaling up S3VM is still an open problem. To address this challenging problem, in this paper, we propose a new incremental learning algorithm to scale up S3VM (IL-S3VM) based on the path following technique in the framework of Difference of Convex (DC) programming. The traditional DC programming based algorithms need multiple outer loops and are not suitable for incremental learning, and traditional path following algorithms are limited to convex problems. Our new IL-S3VM algorithm based on the path-following technique can directly update the solution of S3VM to converge to a local minimum within one outer loop so that the efficient incremental learning can be achieved. More importantly, we provide the finite convergence analysis for our new algorithm. To the best of our knowledge, our new IL-S3VM algorithm is the first efficient path following algorithm for a non-convex problem (i.e., S3VM) with local minimum convergence guarantee. Experimental results on a variety of benchmark datasets not only confirm the finite convergence of IL-S3VM, but also show a huge reduction of computational time compared with existing batch and incremental learning algorithms, while retaining the similar generalization performance.
R 2 SDH: Robust Rotated Supervised Discrete Hashing
Authors: Jie Gui, Ping Li
Venue: KDD
Year: 2018

Learning-based hashing has recently received considerable attentions due to its capability of supporting efficient storage and retrieval of high-dimensional data such as images, videos, and documents. In this paper, we propose a learning-based hashing algorithm called "Robust Rotated Supervised Discrete Hashing" (R 2 SDH), by extending the previous work on "Supervised Discrete Hashing" (SDH). In R 2 SDH, correntropy is adopted to replace the least square regression (LSR) model in SDH for achieving better robustness. Furthermore, considering the commonly used distance metrics such as cosine and Euclidean distance are invariant to rotational transformation, rotation is integrated into the original zero-one label matrix used in SDH, as additional freedom to promote flexibility without sacrificing accuracy. The rotation matrix is learned through an optimization procedure. Experimental results on three image datasets (MNIST, CIFAR-10, and NUS-WIDE) confirm that R 2 SDH generally outperforms SDH.
Data Science for Financial Applications
Authors: David J. Hand
Venue: KDD
Year: 2018

Financial applications of data science provide a perfect illustration of the power of the shift from subjective decision-making to data- and evidence-driven decision-making. In the space of some fifty years, an entire sector of industry has been totally revolutionised. Such applications come in three broad areas: actuarial and insurance, consumer banking, and investment banking. Actuarial and insurance work was one of the earliest adopters of data science ideas, dating from long before the term had been coined, and even before the computer had been invented. But these areas have fallen behind the latest advances in data science technology - which means there is considerable potential for applying modern data analytic ideas. Consumer banking has been described as one the first and major success stories of the data revolution. Dating from the 1960s, when the first credit cards were launched, techniques for analysing the massive data sets of consumer financial transactions have driven much of the development of data mining and data science ideas. But new model types, and new sources of data, are leading to a rich opportunity for significant developments. In investment banking the "efficient market hypothesis" of classic economics says that it is impossible to predict the financial markets. But this is false - though very nearly true. That means that there is an opportunity to use advanced data analytic methods to exploit the tiny gap between conventional theory and what actually happens. Other data science issues, such as data quality, ethics, and security, along with the need to understand the limitations of models, become particularly pointed in the context of financial applications.
Detecting Vehicle Illegal Parking Events using Sharing Bikes' Trajectories
Authors: Tianfu He, Jie Bao, Ruiyuan Li, Sijie Ruan, Yanhua Li, Chao Tian, Yu Zheng
Venue: KDD
Year: 2018

Illegal vehicle parking is a common urban problem faced by major cities in the world, as it incurs traffic jams, which lead to air pollution and traffic accidents. Traditional approaches to detect illegal vehicle parking events rely highly on active human efforts, e.g., police patrols or surveillance cameras. However, these approaches are extremely ineffective to cover a large city. The massive and high quality sharing bike trajectories from Mobike offer us with a unique opportunity to design a ubiquitous illegal parking detection system, as most of the illegal parking events happen at curbsides and have significant impact on the bike users. Two main components are employed to mine the trajectories in our system: 1)~trajectory pre-processing, which filters outlier GPS points, performs map-matching and builds indexes for bike trajectories; and 2)~illegal parking detection, which models the normal trajectories, extracts features from the evaluation trajectories and utilizes a distribution test-based method to discover the illegal parking events. The system is deployed on the cloud internally used by Mobike. Finally, extensive experiments and many insightful case studies based on the massive trajectories in Beijing are presented.
Humans, Jobs, and the Economy: The Future of Finance in the Age of Big Data
Authors: James Hodson
Venue: KDD
Year: 2018

Finance is the efficient allocation of capital to achieve individual and societal objectives. Finance runs on information, from the number of ships under construction in the ports of Dalian, to the beliefs of investors in a marketplace--we want to put capital to work in the places it will have the biggest impact. Increasingly, large new data sources are facilitating our understanding of how individuals, teams, companies, governments, and other entities operate, allowing for new types of modelling that can unlock value and accelerate growth. In this talk, we will explore several open questions at the intersection of individuals, jobs, companies, and financial markets, where new data brings the promise of new understanding. How do people and firms interact to create economic value? How do we accelerate this value creation? How can the KDD community help?
Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application
Authors: Yujing Hu, Qing Da, Anxiang Zeng, Yang Yu, Yinghui Xu
Venue: KDD
Year: 2018

In E-commerce platforms such as Amazon and TaoBao , ranking items in a search session is a typical multi-step decision-making problem. Learning to rank (LTR) methods have been widely applied to ranking problems. However, such methods often consider different ranking steps in a session to be independent, which conversely may be highly correlated to each other. For better utilizing the correlation between different ranking steps, in this paper, we propose to use reinforcement learning (RL) to learn an optimal ranking policy which maximizes the expected accumulative rewards in a search session. Firstly, we formally define the concept of search session Markov decision process (SSMDP) to formulate the multi-step ranking problem. Secondly, we analyze the property of SSMDP and theoretically prove the necessity of maximizing accumulative rewards. Lastly, we propose a novel policy gradient algorithm for learning an optimal ranking policy, which is able to deal with the problem of high reward variance and unbalanced reward distribution of an SSMDP. Experiments are conducted in simulation and TaoBao search engine. The results demonstrate that our algorithm performs much better than the state-of-the-art LTR methods, with more than 40% and 30% growth of total transaction amount in the simulation and the real application, respectively.
Metric Learning from Probabilistic Labels
Authors: Mengdi Huai, Chenglin Miao, Yaliang Li, Qiuling Suo, Lu Su, Aidong Zhang
Venue: KDD
Year: 2018

Metric learning aims to learn a good distance metric that can capture the relationships among instances, and its importance has long been recognized in many fields. In the traditional settings of metric learning, an implicit assumption is that the associated labels of the instances are deterministic. However, in many real-world applications, the associated labels come naturally with probabilities instead of deterministic values. Thus, the existing metric learning methods cannot work well in these applications. To tackle this challenge, in this paper, we study how to effectively learn the distance metric from datasets that contain probabilistic information, and then propose two novel metric learning mechanisms for two types of probabilistic labels, i.e., the instance-wise probabilistic label and the group-wise probabilistic label. Compared with the existing metric learning methods, our proposed mechanisms are capable of learning distance metrics directly from the probabilistic labels with high accuracy. We also theoretically analyze the two proposed mechanisms and provide theoretical bounds on the sample complexity for both of them. Additionally, extensive experiments based on real-world datasets are conducted to verify the desirable properties of the proposed mechanisms.
WattHome: A Data-driven Approach for Energy Efficiency Analytics at City-scale
Authors: Srinivasan Iyengar, Stephen Lee, David E. Irwin, Prashant J. Shenoy, Benjamin Weil
Venue: KDD
Year: 2018

Buildings consume over 40% of the total energy in modern societies and improving their energy efficiency can significantly reduce our energy footprint. In this paper, we present WattHome, a data-driven approach to identify the least energy efficient buildings from a large population of buildings in a city or a region. Unlike previous approaches such as least squares that use point estimates, WattHome uses Bayesian inference to capture the stochasticity in the daily energy usage by estimating the parameter distribution of a building. Further, it compares them with similar homes in a given population using widely available datasets. WattHome also incorporates a fault detection algorithm to identify the underlying causes of energy inefficiency. We validate our approach using ground truth data from different geographical locations, which showcases its applicability in different settings. Moreover, we present results from a case study from a city containing >10,000 buildings and show that more than half of the buildings are inefficient in one way or another indicating a significant potential from energy improvement measures. Additionally, we provide probable cause of inefficiency and find that 41%, 23.73%, and 0.51% homes have poor building envelope, heating, and cooling system faults respectively.
Concepts-Bridges: Uncovering Conceptual Bridges Based on Biomedical Concept Evolution
Authors: Kishlay Jha, Guangxu Xun, Yaqing Wang, Vishrawas Gopalakrishnan, Aidong Zhang
Venue: KDD
Year: 2018

Given two topics of interest (A, C) that are otherwise disconnected - for instance two concepts: a disease ("Migraine") and a therapeutic substance ("Magnesium") - this paper attempts to find the conceptual bridges (e.g., serotonin (B)) that connects them in a novel way. This problem of mining implicit linkage is known as hypotheses generation and its potential to accelerate scientific progress is widely recognized. Almost all of the prior studies to tackle this problem ignore the temporal dynamics of concepts. This is limiting because it is known that the semantic meaning of a concept evolves over time. To overcome this issue, in this study, we define this problem as mining time-aware Top-k conceptual bridges, and in doing so provide a systematic approach to formalize the problem. Specifically, the proposed model first extracts relevant entities from the corpus, represents them in time-specific latent spaces, and then further reasons upon it to generate novel and experimentally testable hypotheses. The key challenge in this approach is to learn a mapping function that encodes the temporal characteristics of concepts and aligns the across-time latent spaces. To solve this, we propose an effective algorithm that learns precise mapping sensitive to both global and local semantics of the input query. Both qualitative and quantitative evaluations performed on the largest available biomedical corpus substantiate the importance of leveraging the evolutionary semantics of medical concepts and suggests that the generated hypotheses are novel and worthy of clinical trials.
Resolving Abstract Anaphora Implicitly in Conversational Assistants using a Hierarchically stacked RNN
Authors: Prerna Khurana, Puneet Agarwal, Gautam Shroff, Lovekesh Vig
Venue: KDD
Year: 2018

Recent proliferation of conversational systems has resulted in an increased demand for more natural dialogue systems, capable of more sophisticated interactions than merely providing factual answers. This is evident from usage pattern of a conversational system deployed within our organization. Users not only expect it to perform co-reference resolution of anaphora, but also of the antecedent or posterior facts presented by users with respect to their query. Presence of such facts in a conversation sometimes modifies the answer of main query, e.g., answer to 'how many sick leave do I get?' would be different when a fact 'I am on contract' is also present. Sometimes there is a need to collectively resolve three or four such facts. In this paper, we propose a novel solution which uses hierarchical neural network, comprising of BiLSTM layer and a maxpool layer that is hierarchically stacked to first obtain a representation of each user utterance and then to obtain a representation for sequence of utterances. This representation is used to identify users' intention. We also improvise this model by using skip connections in the second network to allow better gradient flow. Our model, not only a)~resolves the antecedent and posterior facts, but also b)~performs better even on self-contained queries. It is also c)~faster to train, making it the most promising approach for use in our environment where frequent training and tuning is needed. It slightly outperforms the benchmark on a publicly available dataset, and e)~performs better than obvious baselines approaches on our datasets.