OpenRaiser/Intern-Atlas huggingface dataset:
Imputation-based Time-Series Anomaly Detection with Conditional Weight-Incremental Diffusion Models
Authors: Chunjing Xiao, Zehua Gou, Wenxin Tai, Kunpeng Zhang 0001, Fan Zhou 0002
Venue: KDD
Year: 2023

Existing anomaly detection models for time series are primarily trained with normal-point-dominant data and would become ineffective when anomalous points intensively occur in certain episodes. To solve this problem, we propose a new approach, called DiffAD, from the perspective of time series imputation. Unlike previous prediction- and reconstruction-based methods that adopt either partial or complete data as observed values for estimation, DiffAD uses a density ratio-based strategy to select normal observations flexibly that can easily adapt to the anomaly concentration scenarios. To alleviate the model bias problem in the presence of anomaly concentration, we design a new denoising diffusion-based imputation method to enhance the imputation performance of missing values with conditional weight-incremental diffusion, which can preserve the information of observed values and substantially improves data generation quality for stable anomaly detection. Besides, we customize a multi-scale state space model to capture the long-term dependencies across episodes with different anomaly patterns. Extensive experimental results on real-world datasets show that DiffAD performs better than state-of-the-art benchmarks.
Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications
Authors: Han Xie, Da Zheng 0004, Jun Ma 0029, Houyu Zhang, Vassilis N. Ioannidis, Xiang Song 0003, Qing Ping, Sheng Wang 0012, Carl Yang 0001, Yi Xu 0011, Belinda Zeng, Trishul Chilimbi
Venue: KDD
Year: 2023

Model pre-training on large text corpora has been demonstrated effective for various downstream applications in the NLP domain. In the graph mining domain, a similar analogy can be drawn for pre-training graph models on large graphs in the hope of benefiting downstream graph applications, which has also been explored by several recent studies. However, no existing study has ever investigated the pre-training of text plus graph models on large heterogeneous graphs with abundant textual information (a.k.a. large graph corpora) and then fine-tuning the model on different related downstream applications with different graph schemas. To address this problem, we propose a framework of graph-aware language model pre-training (GaLM) on a large graph corpus, which incorporates large language models and graph neural networks, and a variety of fine-tuning methods on downstream applications. We conduct extensive experiments on Amazon's real internal datasets and large public datasets. Comprehensive empirical results and in-depth analysis demonstrate the effectiveness of our proposed methods along with lessons learned.
A Dual-Agent Scheduler for Distributed Deep Learning Jobs on Public Cloud via Reinforcement Learning
Authors: Mingzhe Xing, Hangyu Mao, Shenglin Yin, Lichen Pan, Zhengchao Zhang, Zhen Xiao, Jieyi Long
Venue: KDD
Year: 2023

Public cloud GPU clusters are becoming emerging platforms for training distributed deep learning jobs. Under this training paradigm, the job scheduler is a crucial component to improve user experiences, i.e., reducing training fees and job completion time, which can also save power costs for service providers. However, the scheduling problem is known to be NP-hard. Most existing work divides it into two easier sub-tasks, i.e., ordering task and placement task, which are responsible for deciding the scheduling orders of jobs and placement orders of GPU machines, respectively. Due to the superior adaptation ability, learning-based policies can generally perform better than traditional heuristic-based methods. Nevertheless, there are still two main challenges that have not been well-solved. First, most learning-based methods only focus on ordering or placement policy independently, while ignoring their cooperation. Second, the unbalanced machine performances and resource contention impose huge overhead and uncertainty on job duration, but rarely be considered in existing work. To tackle these issues, this paper presents a dual-agent scheduler framework abstracted from the two sub-tasks to jointly learn the ordering and placement policies and make better-informed scheduling decisions. Specifically, we design an ordering agent with a scalable squeeze-and-communicate strategy for better cooperation; for the placement agent, we propose a novel Random Walk Gaussian Process to learn the performance similarities of GPU machines while being aware of the uncertain performance fluctuation. Finally, the dual-agent is jointly optimized with multi-agent reinforcement learning. Extensive experiments conducted on the real-world production cluster trace demonstrate the superiority of our model.
A Data-Driven Decision Support Framework for Player Churn Analysis in Online Games
Authors: Yu Xiong, Runze Wu 0001, Shiwei Zhao, Jianrong Tao, Xudong Shen, Tangjie Lyu, Changjie Fan, Peng Cui 0001
Venue: KDD
Year: 2023

Faced with saturated market and fierce competition of online games, it is of great value to analyze the causes of the player churn for improving the game product, maintaining the player retention. A large number of research efforts on churn analysis have been made into churn prediction, which can achieve a sound accuracy benefiting from the booming of AI technologies. However, game publishers are usually unable to apply high-accuracy prediction methods in practice for preventing or relieving the churn due to the lack of the specific decision support (e.g., why they leave and what to do next). In this study, we fully exploit the expertise in online games and propose a comprehensive data-driven decision support framework for addressing game player churn. We first define the churn analysis in online games from a commercial perspective and elaborate the core demands of game publishers for churn analysis. Then we employ and improve the cutting-edge eXplainable AI (XAI) methods to predict player churn and analyze the potential churn causes. The possible churn causes can finally guide game publishers to make specific decisions of revision or intervention in our designed procedure. We demonstrate the effectiveness and high practical value of the framework by conducting extensive experiments on a real-world large-scale online game, Justice PC. The whole decision support framework, bringing interesting and valuable insights, also receives quite positive reviews from the game product and operation teams. Notably, the whole pipeline is readily transplanted to other online systems for decision support to address similar issues.
Learning Behavior-oriented Knowledge Tracing
Authors: Bihan Xu, Zhenya Huang, Jiayu Liu 0001, Shuanghong Shen, Qi Liu 0003, Enhong Chen, Jinze Wu, Shijin Wang 0001
Venue: KDD
Year: 2023

Exploring how learners' knowledge states evolve during the learning activities is a critical task in online learning systems, which can facilitate personalized services downstream, such as course recommendation. Most of existing methods have devoted great efforts to analyzing learners' knowledge states according to their responses (i.e., right or wrong) to different questions. However, the significant effect of learners' learning behaviors (e.g., answering speed, the number of attempts) is omitted, which can reflect their knowledge acquisition deeper and ensure the reliability of the response. In this paper, we propose a Learning Behavior-oriented Knowledge Tracing (LBKT) model, with the goal of explicitly exploring the learning behavior effects on learners' knowledge states. Specifically, we first analyze and summarize several dominated learning behaviors including Speed, Attempts and Hints in the learning process. As the characteristics of different learning behaviors vary greatly, we separately estimate their various effects on learners' knowledge acquisition in a quantitative manner. Then, considering that different learning behaviors are closely dependent with each other, we assess the fused effect of multiple learning behaviors by capturing their complex dependent patterns. Finally, we integrate the forgetting factor with learners' knowledge acquisition to comprehensively update their changing knowledge states in learning. Extensive experimental results on several public datasets demonstrate that our model generates better performance prediction for learners against existing methods. Moreover, LBKT shows good interpretability in tracking learners' knowledge state by incorporating the learning behavior effects. Our codes are available at https://github.com/xbh0720/LBKT.
Workshop on Applied Data Science for Healthcare: Applications and New Frontiers of Generative Models for Healthcare
Authors: Tao Xu 0020, Fei Wang 0001, Prithwish Chakraborty, Pei-Yun Sabrina Hsueh, Gregor Stiglic, Jiang Bian 0001, Lixia Yao, Alexej Gossmann, Florian Buettner 0001
Venue: KDD
Year: 2023

Built on the success of the past five years, KDD DSHealth 2023 will further catalyze the development of links between academic and industrial data science groups. The workshop aims to stimulate discussion on strategic areas for development and to facilitate future cross-disciplinary collaborations. In accordance with the multi-year goal to continue fostering this community via timely topics, this year the workshop will focus on the applications and new development of generative models in healthcare, including the new development and application of LLMs. The workshop invites full papers, as well as work-in-progress on the application of data science in healthcare. The workshop will feature two invited talks from eminent speakers, spanning academia, industry, clinical researchers, and governmental regulatory bodies. In addition, we will invite community members to submit their research works and bring them for discussion. The summary gives a brief description of the half-day workshop to be held on August 7th, 2023.
MimoSketch: A Framework to Mine Item Frequency on Multiple Nodes with Sketches
Authors: Yuchen Xu 0003, Wenfei Wu, Bohan Zhao, Tong Yang 0003, Yikai Zhao 0001
Venue: KDD
Year: 2023

We abstract a MIMO scenario in distributed data stream mining, where a stream of multiple items is mined by multiple nodes. We design a framework named MimoSketch for the MIMO-specific scenario, which improves the fundamental mining task of item frequency estimation. MimoSketch consists of an algorithm design and a policy to schedule items to nodes. MimoSketch's algorithm applies random counting to preserve a mathematically proven unbiasedness property, which makes it friendly to the aggregate query on multiple nodes; its memory layout is dynamically adaptive to the runtime item size distribution, which maximizes the estimation accuracy by storing more items. MimoSketch's scheduling policy balances items among nodes, avoiding nodes being overloaded or underloaded, which improves the overall mining accuracy. Our prototype and evaluation show that our algorithm can improve the item frequency estimation accuracy by an order of magnitude compared with the state-of-the-art solutions, and the scheduling policy further promotes the performance in MIMO scenarios.
Large-Scale Graph Neural Networks: The Past and New Frontiers
Authors: Rui Xue 0006, Haoyu Han 0001, Tong Zhao 0003, Neil Shah, Jiliang Tang, Xiaorui Liu
Venue: KDD
Year: 2023

Graph Neural Networks (GNNs) have gained significant attention in recent years due to their ability to model complex relationships between entities in graph-structured data such as social networks, protein structures, and knowledge graphs. However, due to the size of real-world industrial graphs and the special architecture of GNNs, it is a long-lasting challenge for engineers and researchers to deploy GNNs on large-scale graphs, which significantly limits their applications in real-world applications. In this tutorial, we will cover the fundamental scalability challenges of GNNs, frontiers of large-scale GNNs including classic approaches and some newly emerging techniques, the evaluation and comparison of scalable GNNs, and their large-scale real-world applications. Overall, this tutorial aims to provide a systematic and comprehensive understanding of the challenges and state-of-the-art techniques for scaling GNNs. The summary and discussion on future directions will inspire engineers and researchers to explore new ideas and developments in this rapidly evolving field. The website of this tutorial is available at https://sites.google.com/ncsu.edu/gnnkdd2023tutorial.
E-commerce Search via Content Collaborative Graph Neural Network
Authors: Guipeng Xv, Chen Lin 0001, Wanxian Guan, Jinping Gou, Xubin Li, Hongbo Deng, Jian Xu 0015, Bo Zheng 0007
Venue: KDD
Year: 2023

Recently, many E-commerce search models are based on Graph Neural Networks (GNNs). Despite their promising performances, they are (1) lacking proper semantic representation of product contents; (2) less efficient for industry-scale graphs; and (3) less accurate on long-tail queries and cold-start products. To address these problems simultaneously, this paper proposes CC-GNN, a novel Content Collaborative Graph Neural Network. Firstly, CC-GNN enables content phrases to participate explicitly in graph propagation to capture the proper meaning of phrases and semantic drifts. Secondly, CC-GNN presents several efforts towards a more scalable graph learning framework, including efficient graph construction, MetaPath-guided Message Passing, and Difficulty-aware Representation Perturbation for graph contrastive learning. Furthermore, CC-GNN adopts Counterfactual Data Supplement at both supervised and contrastive learning to resolve the long-tail/cold-start problems. Extensive experiments on a real E-commerce dataset of 100-million-scale nodes show that CC-GNN produces significant improvements over existing methods (i.e., more than 10% improvements in terms of several key evaluation metrics for overall, long-tail queries and cold-start products) while reducing computational complexity. The proposed components of CC-GNN can be applied to other models for search and recommendation tasks. Experiments on a public dataset show that applying the proposed components can improve the performance of different recommendation models.
KDD 2023 International Workshop on Data Science for Social Good (DSSG-23)
Authors: Amulya Yadav, Aparna Taneja, Ayan Mukhopadhyay, Serina Chang
Venue: KDD
Year: 2023

This workshop will bring together researchers and practitioners across different strands of data science research and a wide range of important real-world application domains. The objective is to share the current state of research and practice, explore future work directions, and create collaboration opportunities. In addition, the workshop will emphasize highlighting data science approaches for tackling the United Nations Sustainable Development Goals (see preliminary agenda below). The organizers believe that data science research has an important role to play in providing unique insights about critical challenges faced by marginalized communities around the world; we encourage submissions from both data science researchers as well as social workers, agronomists, epidemiologists, health policy researchers, and other domain experts who are interested in engaging with the SIGKDD community.
CriticalFL: A Critical Learning Periods Augmented Client Selection Framework for Efficient Federated Learning
Authors: Gang Yan 0002, Hao Wang 0022, Xu Yuan 0001, Jian Li 0008
Venue: KDD
Year: 2023

Federated learning (FL) is a distributed optimization paradigm that learns from data samples distributed across a number of clients. Adaptive client selection that is cognizant of the training progress of clients has become a major trend to improve FL efficiency but not yet well-understood. Most existing FL methods such as FedAvg and its state-of-the-art variants implicitly assume that all learning phases during the FL training process are equally important. Unfortunately, this assumption has been revealed to be invalid due to recent findings on critical learning periods (CLP), in which small gradient errors may lead to an irrecoverable deficiency on final test accuracy. In this paper, we develop CriticalFL, a CLP augmented FL framework to reveal that adaptively augmenting exiting FL methods with CLP, the resultant performance is significantly improved when the client selection is guided by the discovered CLP. Experiments based on various machine learning models and datasets validate that the proposed CriticalFL framework consistently achieves an improved model accuracy while maintains better communication efficiency as compared to state-of-the-art methods, demonstrating a promising and easily adopted method for tackling the heterogeneity of FL training.
Empowering General-purpose User Representation with Full-life Cycle Behavior Modeling
Authors: Bei Yang, Jie Gu, Ke Liu 0012, Xiaoxiao Xu, Renjun Xu, Qinghui Sun, Hong Liu
Venue: KDD
Year: 2023

User Modeling plays an essential role in industry. In this field, task-agnostic approaches, which generate general-purpose representation applicable to diverse downstream user cognition tasks, is a promising direction being more valuable and economical than task-specific representation learning. With the rapid development of Internet service platforms, user behaviors have been accumulated continuously. However, existing general-purpose user representation researches have little ability for full-life cycle modeling on extremely long behavior sequences since user registration. In this study, we propose a novel framework called full- Life cycle User Representation Model (LURM) to tackle this challenge. Specifically, LURM consists of two cascaded sub-models: (\romannumeral1) Bag-of-Interests (BoI) encodes user behaviors in any time period into a sparse vector with super-high dimension (\textite. \textitg. , 10^5 ); (\romannumeral2) Self-supervised Multi-anchor Encoder Network (SMEN) maps sequences of BoI features to multiple low-dimensional user representations. Specially, SMEN achieves almost lossless dimensionality reduction, benefiting from a novel multi-anchor module which can learn different aspects of user interests. Experiments on several benchmark datasets show that our approach outperforms state-of-the-art general-purpose representation methods.
BatchSampler: Sampling Mini-Batches for Contrastive Learning in Vision, Language, and Graphs
Authors: Zhen Yang 0034, Tinglin Huang 0001, Ming Ding 0004, Yuxiao Dong, Rex Ying, Yukuo Cen, Yangliao Geng, Jie Tang 0001
Venue: KDD
Year: 2023

In-Batch contrastive learning is a state-of-the-art self-supervised method that brings semantically-similar instances close while pushing dissimilar instances apart within a mini-batch. Its key to success is the negative sharing strategy, in which every instance serves as a negative for the others within the mini-batch. Recent studies aim to improve performance by sampling hard negatives within the current mini-batch, whose quality is bounded by the mini-batch itself. In this work, we propose to improve contrastive learning by sampling mini-batches from the input data. We present BatchSampler\footnoteThe code is available at BatchSampler to sample mini-batches of hard-to-distinguish (i.e., hard and true negatives to each other) instances. To make each mini-batch have fewer false negatives, we design the proximity graph of randomly-selected instances. To form the mini-batch, we leverage random walk with restart on the proximity graph to help sample hard-to-distinguish instances. BatchSampler is a simple and general technique that can be directly plugged into existing contrastive learning models in vision, language, and graphs. Extensive experiments on datasets of three modalities show that BatchSampler can consistently improve the performance of powerful contrastive models, as shown by significant improvements of SimCLR on ImageNet-100, SimCSE on STS (language), and GraphCL and MVGRL on graph datasets.
MAPLE: Semi-Supervised Learning with Multi-Alignment and Pseudo-Learning
Authors: Juncheng Yang, Chao Li, Zuchao Li, Wei Yu 0009, Bo Du 0001, Shijun Li 0001
Venue: KDD
Year: 2023

Data augmentation has undoubtedly enabled a significant leap forward in training a high-accuracy deep network. Besides the commonly used augmentation to target data, e.g., random cropping, flipping, and rotation, recent works have been dedicated to mining generalized knowledge by using multiple sources. However, along with plentiful data comes the huge data distribution gap between the target and different sources (hybrid shift). To mitigate this problem, existing methods tend to manually annotate more data. Unlike previous methods, this paper focuses on the study of learning deep models by gathering knowledge from multiple sources in a labor-free fashion and further proposes the "Multi-Alignment and Pseudo-Learning'' method, dubbed MAPLE. MAPLE constructs the multi-alignment module, which consists of multiple discriminators to align different data distributions via an adversarial process. In addition, a novel semi-supervised learning (SSL) manner is introduced to further facilitate the utility of our MAPLE. Extensive evaluations conducted on four benchmarks show the effectiveness of the proposed MAPLE, which achieves state-of-the-art performance outperforming existing methods by an obvious margin.
Test Accuracy vs. Generalization Gap: Model Selection in NLP without Accessing Training or Testing Data
Authors: Yaoqing Yang, Ryan Theisen, Liam Hodgkinson, Joseph E. Gonzalez, Kannan Ramchandran, Charles H. Martin, Michael W. Mahoney
Venue: KDD
Year: 2023

Selecting suitable architecture parameters and training hyperparameters is essential for enhancing machine learning (ML) model performance. Several recent empirical studies conduct large-scale correlational analysis on neural networks (NNs) to search for effective generalization metrics that can guide this type of model selection. Effective metrics are typically expected to correlate strongly with test performance. In this paper, we expand on prior analyses by examining generalization-metric-based model selection with the following objectives: (i) focusing on natural language processing (NLP) tasks, as prior work primarily concentrates on computer vision (CV) tasks; (ii) considering metrics that directly predict test error instead of the generalization gap; (iii) exploring metrics that do not need access to data to compute. From these objectives, we are able to provide the first model selection results on large pretrained Transformers from Huggingface using generalization metrics. Our analyses consider (I) hundreds of Transformers trained in different settings, in which we systematically vary the amount of data, the model size and the optimization hyperparameters, (II) a total of 51 pretrained Transformers from eight families of Huggingface NLP models, including GPT2, BERT, etc., and (III) a total of 28 existing and novel generalization metrics. Despite their niche status, we find that metrics derived from the heavy-tail (HT) perspective are particularly useful in NLP tasks, exhibiting stronger correlations than other, more popular metrics. To further examine these metrics, we extend prior formulations relying on power law (PL) spectral distributions to exponential (EXP) and exponentially-truncated power law (E-TPL) families.