OpenRaiser/Intern-Atlas huggingface dataset:
Graph Adversarial Attack via Rewiring
Authors: Yao Ma, Suhang Wang, Tyler Derr, Lingfei Wu, Jiliang Tang
Venue: KDD
Year: 2021

Graph Neural Networks (GNNs) have demonstrated their powerful capability in learning representations for graph-structured data. Consequently, they have enhanced the performance of many graph-related tasks such as node classification and graph classification. However, it is evident from recent studies that GNNs are vulnerable to adversarial attacks. Their performance can be largely impaired by deliberately adding carefully created unnoticeable perturbations to the graph. Existing attacking methods often produce perturbation by adding/deleting a few edges, which might be noticeable even when the number of modified edges is small. In this paper, we propose a graph rewiring operation to perform the attack. It can affect the graph in a less noticeable way compared to existing operations such as adding/deleting edges. We then utilize deep reinforcement learning to learn the strategy to effectively perform the rewiring operations. Experiments on real-world graphs demonstrate the effectiveness of the proposed framework. To understand the proposed framework, we further analyze how its generated perturbation impacts the target model and the advantages of the rewiring operations. The implementation of the proposed framework is available at https://github.com/alge24/ReWatt.
TMC 2021: 2021 International Workshop on Talent and Management Computing
Authors: Hui Xiong, Hengshu Zhu, Tong Xu, Xi Zhang
Venue: KDD
Year: 2021

In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to deal with the talent and management related tasks in a quantitative manner. Indeed, thanks to the era of big data, the availability of large-scale talent data provides unparalleled opportunities for business leaders to understand the rules of talent and management, which in turn deliver intelligence for effective decision making and management for their organizations. In the past few years, talent and management computing have increasingly attracted attentions from KDD communities, and a number of research/applied data science efforts have been devoted. To this end, the purpose of this workshop, i.e., the 2021 International Workshop on Talent and Management Computing, is to bring together researchers and practitioners to discuss both the critical problems faced by talent and management related domains, and potential data-driven solutions by leveraging state-of-the-art data mining technologies.
When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Control
Authors: Chaochao Chen, Jun Zhou, Li Wang, Xibin Wu, Wenjing Fang, Jin Tan, Lei Wang, Alex X. Liu, Hao Wang, Cheng Hong
Venue: KDD
Year: 2021

Logistic Regression (LR) is the most widely used machine learning model in industry for its efficiency, robustness, and interpretability. Due to the problem of data isolation and the requirement of high model performance, many applications in industry call for building a secure and efficient LR model for multiple parties. Most existing work uses either Homomorphic Encryption (HE) or Secret Sharing (SS) to build secure LR. HE based methods can deal with high-dimensional sparse features, but they incur potential security risks. SS based methods have provable security, but they have efficiency issue under high-dimensional sparse features. In this paper, we first present CAESAR, which combines HE and SS to build secure large-scale sparse logistic regression model and achieves both efficiency and security. We then present the distributed implementation of CAESAR for scalability requirement. We have deployed CAESAR in a risk control task and conducted comprehensive experiments. Our experimental results show that CAESAR improves the state-of-the-art model by around 130 times.
Coupled Graph ODE for Learning Interacting System Dynamics
Authors: Zijie Huang, Yizhou Sun, Wei Wang
Venue: KDD
Year: 2021

Many real-world systems such as social networks and moving planets are dynamic in nature, where a set of coupled objects are connected via the interaction graph and exhibit complex behavior along the time. For example, the COVID-19 pandemic can be considered as a dynamical system, where objects represent geographical locations (e.g., states) whose daily confirmed cases of infection evolve over time. Outbreak at one location may influence another location as people travel between these locations, forming a graph. Thus, how to model and predict the complex dynamics for these systems becomes a critical research problem. Existing work on modeling graph-structured data mostly assumes a static setting. How to handle dynamic graphs remains to be further explored. On one hand, features of objects change over time, influenced by the linked objects in the interaction graph. On the other hand, the graph itself can also evolve, where new interactions (links) may form and existing links may drop, which may in turn be affected by the dynamic features of objects. In this paper, we propose coupled graph ODE: a novel latent ordinary differential equation (ODE) generative model that learns the coupled dynamics of nodes and edges with a graph neural network (GNN) based ODE in a continuous manner. Our model consists of two coupled ODE functions for modeling the dynamics of edges and nodes based on their latent representations respectively. It employs a novel encoder parameterized by a GNN for inferring the initial states from historical data, which serves as the starting point of the predicted latent trajectories. Experiment results on the COVID-19 dataset and the simulated social network dataset demonstrate the effectiveness of our proposed method.
Bootstrapping Recommendations at Chrome Web Store
Authors: Zhen Qin, Honglei Zhuang, Rolf Jagerman, Xinyu Qian, Po Hu, Dan Chary Chen, Xuanhui Wang, Michael Bendersky, Marc Najork
Venue: KDD
Year: 2021

Google Chrome, one of the world's most popular web browsers, features an extension framework allowing third-party developers to enhance Chrome's functionality. Chrome extensions are distributed through the Chrome Web Store (CWS), a Google-operated online marketplace. In this paper, we describe how we developed and deployed three recommender systems for discovering relevant extensions in CWS, namely non-personalized recommendations, related extension recommendations, and personalized recommendations. Unlike most existing papers that focus on novel algorithms, this paper focuses on sharing practical experiences when building large-scale recommender systems under various real-world constraints, such as privacy constraints, data sparsity and skewness issues, and product design choices (e.g., user interface). We show how these constraints make standard approaches difficult to succeed in practice. We share success stories that turn negative live metrics to positive ones, including: 1) how we use interpretable neural models to bootstrap the systems, help identifying pipeline issues, and pave the way for more advanced models; 2) a new item-item based algorithm for related recommendations that works under highly skewed data distributions; and 3) how the previous two techniques can help bootstrapping the personalized recommendations, which significantly reduces development cycles and bypasses various real-world difficulties. All the explorations in this work are verified in live traffic on millions of users. We believe that the findings in this paper can help practitioners to build better large-scale recommender systems.
Relational Message Passing for Knowledge Graph Completion
Authors: Hongwei Wang, Hongyu Ren, Jure Leskovec
Venue: KDD
Year: 2021

Knowledge graph completion aims to predict missing relations between entities in a knowledge graph. In this work, we propose a relational message passing method for knowledge graph completion. Different from existing embedding-based methods, relational message passing only considers edge features (i.e., relation types) without entity IDs in the knowledge graph, and passes relational messages among edges iteratively to aggregate neighborhood information. Specifically, two kinds of neighborhood topology are modeled for a given entity pair under the relational message passing framework: (1) Relational context, which captures the relation types of edges adjacent to the given entity pair; (2) Relational paths, which characterize the relative position between the given two entities in the knowledge graph. The two message passing modules are combined together for relation prediction. Experimental results on knowledge graph benchmarks as well as our newly proposed dataset show that, our method PathCon outperforms state-of-the-art knowledge graph completion methods by a large margin. PathCon is also shown applicable to inductive settings where entities are not seen in training stage, and it is able to provide interpretable explanations for the predicted results. The code and all datasets are available at https://github.com/hwwang55/PathCon.
Que2Search: Fast and Accurate Query and Document Understanding for Search at Facebook
Authors: Yiqun Liu, Kaushik Rangadurai, Yunzhong He, Siddarth Malreddy, Xunlong Gui, Xiaoyi Liu, Fedor Borisyuk
Venue: KDD
Year: 2021

In this paper, we present Que2Search, a deployed query and product understanding system for search. Que2Search leverages multi-task and multi-modal learning approaches to train query and product representations. We achieve over 5% absolute offline relevance improvement and over 4% online engagement gain over state-of-the-art Facebook product understanding system by combining the latest multilingual natural language understanding architectures like XLM and XLM-R with multi-modal fusion techniques. In this paper, we describe how we deploy XLM-based search query understanding model that runs <1.5ms @P99 on CPU at Facebook scale, which has been a significant challenge in the industry. We also describe what model optimizations worked (and what did not) based on numerous offline and online A/B experiments. We deploy Que2Search to Facebook Marketplace Search and share our deployment experience to production and tuning tricks to achieve higher efficiency in online A/B experiments. Que2Search has demonstrated gains in production applications and operates at Facebook scale.
Practical Approach to Asynchronous Multivariate Time Series Anomaly Detection and Localization
Authors: Ahmed Abdulaal, Zhuanghua Liu, Tomer Lancewicki
Venue: KDD
Year: 2021

Engineers at eBay utilize robust methods in monitoring IT system signals for anomalies. However, the growing scale of signals, both in volumes and dimensions, overpowers traditional statistical state-space or supervised learning tools. Thus, state-of-the-art methods based on unsupervised deep learning are sought in recent research. However, we experienced flaws when implementing those methods, such as requiring partial supervision and weaknesses to high dimensional datasets, among other reasons discussed in this paper. We propose a practical approach for inferring anomalies from large multivariate sets. We observe an abundance of time series in real-world applications, which exhibit asynchronous and consistent repetitive variations, such as IT, weather, utility, and transportation. Our solution is designed to leverage this behavior. The solution utilizes spectral analysis on the latent representation of a pre-trained autoencoder to extract dominant frequencies across the signals, which are then used in a subsequent network that learns the phase shifts across the signals and produces a synchronized representation of the raw multivariate. Random subsets of the synchronous multivariate are then fed into an array of autoencoders learning to minimize the quantile reconstruction losses, which are then used to infer and localize anomalies based on a majority vote. We benchmark this method against state-of-the-art approaches on public datasets and eBay's data using their referenced evaluation methods. Furthermore, we address the limitations of the referenced evaluation methods and propose a more realistic evaluation method.
Fragile Earth: Accelerating Progress towards Equitable Sustainability
Authors: Naoki Abe, Kathleen Buckingham, Bistra Dilkina, Emre Eftelioglu, Auroop R. Ganguly, James Hodson, Ramakrishnan Kannan
Venue: KDD
Year: 2021

Fragile Earth 2021, our annual workshop is taking place as part of the Earth Day events at ACM's KDD 2021 Conference on research in Machine Learning and its applications. The 5th edition of Fragile Earth will bring together the research community, industry, and policymakers to develop radically new technological foundations for advancing and meeting the Sustainable Development Goals in a way that ensures equitable and inclusive progress.
The 4th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery (epiDAMIK 4.0 @ KDD2021)
Authors: Bijaya Adhikari, Ajitesh Srivastava, Sen Pei, Sarah Kefayati, Rose Yu, Amulya Yadav, Alexander Rodríguez, Arvind Ramanathan, Anil Vullikanti, B. Aditya Prakash
Venue: KDD
Year: 2021

The 4th epiDAMIK@SIGKDD workshop is a forum to discuss new insights into how data mining can play a bigger role in epidemiology and public health research. While the integration of data science methods into epidemiology has significant potential, it remains under studied. We aim to raise the profile of this emerging research area of data-driven and computational epidemiology, and create a venue for presenting state-of-the-art and in-progress results-in particular, results that would otherwise be difficult to present at a major data mining conference, including lessons learnt in the 'trenches'. The current COVID-19 pandemic has only showcased the urgency and importance of this area. Our target audience consists of data mining and machine learning researchers from both academia and industry who are interested in epidemiological and public-health applications of their work, and practitioners from the areas of mathematical epidemiology and public health.
Software as a Medical Device: Regulating AI in Healthcare via Responsible AI
Authors: Muhammad Aurangzeb Ahmad, Steve Overman, Christine Allen, Vikas Kumar, Ankur Teredesai, Carly Eckert
Venue: KDD
Year: 2021

With the increased adoption of AI in healthcare, there is a growing recognition and demand to regulate AI in healthcare to avoid potential harm and unfair bias against vulnerable populations. Around a hundred governmental bodies and commissions as well as leaders in the tech sector have proposed principles to create responsible AI systems. However, most of these proposals are short on specifics which has led to charges of ethics washing. In this tutorial we offer a guide to help navigate through complex governmental regulations and explain the various constituent practical elements of a responsible AI system in healthcare in the light of proposed regulations. Additionally, we breakdown and emphasize that the recommendations from regulatory bodies like FDA or the EU are necessary but not sufficient elements of creating a responsible AI system. We elucidate how regulations and guidelines often focus on epistemic concerns to the detriment of practical concerns e.g., requirement for fairness without explicating what fairness constitutes for a use case. FDA's Software as a medical device document and EU's GDPR among other AI governance documents talk about the need for implementing sufficiently good machine learning practices. In this tutorial we elucidate what that would mean from a practical perspective for real world use cases in healthcare throughout the machine learning cycle i.e., Data Management, Data Specification, Feature Engineering, Model Evaluation, Model Specification, Model Explainability, Model Fairness, Reproducibility, checks for data leakage and model leakage. We note that conceptualizing responsible AI as a process rather than an end goal accords well with how AI systems are used in practice. We also discuss how a domain centric stakeholder perspective translates into balancing requirements for multiple competing optimization criteria.
Data Science on Blockchains
Authors: Cuneyt Gurcan Akcora, Murat Kantarcioglu, Yulia R. Gel
Venue: KDD
Year: 2021

Blockchain technology garners an ever-increasing interest of researchers in various domains that benefit from scalable cooperation among trust-less parties. As blockchains and their applications proliferate, so do the complexity and volume of data stored by Blockchains. Analyzing this data has emerged as an important research topic, already leading to methodological advancements in information sciences. In this tutorial, we offer a holistic view of applied Data Science on Blockchains. Starting with the core components of Blockchain, we will detail the state of art in Blockchain data analytics for graph, security, and finance domains. Our examples will answer questions, such as, how to parse, extract and clean the data stored in blockchains?, how to store and query Blockchain data? and what features we could compute from blockchains? We will share tutorial notes, collected meta-information, and further reading pointers on our tutorial website at https://blockchaintutorial.github.io/
LawyerPAN: A Proficiency Assessment Network for Trial Lawyers
Authors: Yanqing An, Qi Liu, Han Wu, Kai Zhang, Linan Yue, Mingyue Cheng, Hongke Zhao, Enhong Chen
Venue: KDD
Year: 2021

Assessing the proficiency of trial lawyers in different legal fields is of significant importance since a qualified lawyer or lawyer team can strive for his clients' best rights while ensuring the fairness of litigations. However, proficiency assessment for lawyers is very challenging due to many technical and domain challenges, such as the lack of unified evaluation standards, and the complex interactions between lawyers and cases in real legal systems. To this end, we propose a novel proficiency assessment network for trial lawyers (LawyerPAN) to quantify lawyer proficiency through online litigation records. Specifically, we first leverage the theories in psychological measurement for mapping the proficiency of lawyers in each field into a unified real number space. Meanwhile, the characteristics of cases (i.e., case difficulty and discrimination) are well modeled to ensure fairness when assessing lawyers in different cases and fields. Then, we model the interactions between lawyers and cases from two perspectives: the anticipatory perspective aims to measure the personal proficiency of anticipated strategy, and the adversarial perspective seeks to depict the gap of lawyers' proficiency between both sides (i.e., plaintiffs and defendants). Finally, we conduct extensive experiments on real-world data, and the results show the effectiveness and interpretability of our approaches on assessing the proficiency of trial lawyers.
Fine-Grained System Identification of Nonlinear Neural Circuits
Authors: Dawna Bagherian, James Gornet, Jeremy Bernstein, Yu-Li Ni, Yisong Yue, Markus Meister
Venue: KDD
Year: 2021

We study the problem of sparse nonlinear model recovery of high dimensional compositional functions. Our study is motivated by emerging opportunities in neuroscience to recover fine-grained models of biological neural circuits using collected measurement data. Guided by available domain knowledge in neuroscience, we explore conditions under which one can recover the underlying biological circuit that generated the training data. Our results suggest insights of both theoretical and practical interests. Most notably, we find that a sign constraint on the weights is a necessary condition for system recovery, which we establish both theoretically with an identifiability guarantee and empirically on simulated biological circuits. We conclude with a case study on retinal ganglion cell circuits using data collected from mouse retina, showcasing the practical potential of this approach.
AdKDD 2021
Authors: Abraham Bagherjeiran, Nemanja Djuric, Mihajlo Grbovic, Kuang-chih Lee, Kun Liu, Vladan Radosavljevic, Suju Rajan
Venue: KDD
Year: 2021

The digital advertising field has always had challenging ML problems, learning from petabytes of data that is highly imbalanced, reactivity times in the milliseconds and more recently compounded with the complex user's path to purchase across devices, across platforms and even online/real-world behavior. The AdKDD workshop continues to be a forum for researchers in advertising, during and after KDD. Our website which hosts slides and abstracts receives approximately 2,000 monthly visits. In surveys during AdKDD 2019 and 2020, over 60% agreed that AdKDD is the reason they attended KDD and over 90% indicated they would attend next year. The 2021 edition is particularly timely because of ongoing developments in ad tracking. We will aim to discuss notions of privacy and tracking enforced by GDPR and through company policies. In addition, we will seek papers that discuss fairness in the context of advertising, to what extent does hyper-personalization work, and on whether the ad industry as a whole needs to think through more effective business models such as incrementality. Ad tech is in an interesting place of evolution/maturity now and we would like to use the AdKDD forum to get the researchers to think not only about the ML aspects but also spark conversations about the societal ones.
Multi-facet Contextual Bandits: A Neural Network Perspective
Authors: Yikun Ban, Jingrui He, Curtiss B. Cook
Venue: KDD
Year: 2021

Contextual multi-armed bandit has shown to be an effective tool in recommender systems. In this paper, we study a novel problem of multi-facet bandits involving a group of bandits, each characterizing the users' needs from one unique aspect. In each round, for the given user, we need to select one arm from each bandit, such that the combination of all arms maximizes the final reward. This problem can find immediate applications in E-commerce, healthcare, etc. To address this problem, we propose a novel algorithm, named MuFasa, which utilizes an assembled neural network to jointly learn the underlying reward functions of multiple bandits. It estimates an Upper Confidence Bound (UCB) linked with the expected reward to balance between exploitation and exploration. Under mild assumptions, we provide the regret analysis of MuFasa. It can achieve the near-optimal Õ((K + 1) √T) regret bound where K is the number of bandits and T is the number of played rounds. Furthermore, we conduct extensive experiments to show that MuFasa outperforms strong baselines on real-world data sets.
Partial Label Dimensionality Reduction via Confidence-Based Dependence Maximization
Authors: Wei-Xuan Bao, Jun-Yi Hang, Min-Ling Zhang
Venue: KDD
Year: 2021

Partial label learning deals with training examples each associated with a set of candidate labels, among which only one is valid. Most existing works focus on manipulating the label space by estimating the labeling confidences of candidate labels, while the task of manipulating the feature space by dimensionality reduction has been rarely investigated. In this paper, a novel partial label dimensionality reduction approach named CENDA is proposed via confidence-based dependence maximization. Specifically, CENDA adapts the Hilbert-Schmidt Independence Criterion (HSIC) to help identify the projection matrix, where the dependence between projected feature information and confidence-based labeling information is maximized iteratively. In each iteration, the projection matrix admits closed-form solution by solving a tailored generalized eigenvalue problem, while the labeling confidences of candidate labels are updated by conducting kNN aggregation in the projected feature space. Extensive experiments over a broad range of benchmark data sets show that the predictive performance of well-established partial label learning algorithms can be significantly improved by coupling with the proposed dimensionality reduction approach.