OpenRaiser/Intern-Atlas huggingface dataset:
A Multi-Graph Attributed Reinforcement Learning based Optimization Algorithm for Large-scale Hybrid Flow Shop Scheduling Problem
Authors: Fei Ni, Jianye Hao, Jiawen Lu, Xialiang Tong, Mingxuan Yuan, Jiahui Duan, Yi Ma, Kun He
Venue: KDD
Year: 2021

Hybrid Flow Shop Scheduling Problem (HFSP) is an essential problem in the automated warehouse scheduling, aiming at optimizing the sequence of jobs and the assignment of machines to utilize the makespan or other objectives. Existing algorithms adopt fixed search paradigm based on expert knowledge to seek satisfactory solutions. However, considering the varying data distribution and large scale of the practical HFSP, these methods fail to guarantee the quality of the obtained solution under the real-time requirement, especially facing extremely different data distribution. To address this challenge, we propose a novel Multi-Graph Attributed Reinforcement Learning based Optimization (MGRO) algorithm to better tackle the practical large-scale HFSP and improve the existing algorithm. Owing to incorporating the reinforcement learning-based policy search approach with classic search operators and the powerful multi-graph based representation, MGRO is capable of adjusting the search paradigm according to specific instances and enhancing the search efficiency. Specifically, we formulate the Gantt chart of the instance into the multi-graph-structured data. Then Graph Neural Network (GNN) and attention-based adaptive weighted pooling are employed to represent the state and make MGRO size-agnostic across arbitrary sizes of instances. In addition, a useful reward shaping approach is designed to facilitate model convergence. Extensive numerical experiments on both the publicly available dataset and real industrial dataset from Huawei Supply Chain Business Unit demonstrate the superiority of MGRO over existing baselines.
PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Design
Authors: Amin Heyrani Nobari, Wei Chen, Faez Ahmed
Venue: KDD
Year: 2021

Engineering design tasks often require synthesizing new designs that meet desired performance requirements. The conventional design process, which requires iterative optimization and performance evaluation, is slow and dependent on initial designs. Past work has used conditional generative adversarial networks (cGANs) to enable direct design synthesis for given target performances. However, most existing cGANs are restricted to categorical conditions. Recent work on Continuous conditional GAN (CcGAN) tries to address this problem, but still faces two challenges: 1) it performs poorly on non-uniform performance distributions, and 2) the generated designs may not cover the entire design space. We propose a new model, named Performance Conditioned Diverse Generative Adversarial Network (PcDGAN), which introduces a singular vicinal loss combined with a Determinantal Point Processes (DPP) based loss function to enhance diversity. PcDGAN uses a new self-reinforcing score called the Lambert Log Exponential Transition Score (LLETS) for improved conditioning. Experiments on synthetic problems and a real-world airfoil design problem demonstrate that PcDGAN outperforms state-of-the-art GAN models and improves the conditioning likelihood by 69% in an airfoil generation task and up to 78% in synthetic conditional generation tasks and achieves greater design space coverage. The proposed method enables efficient design synthesis and design space exploration with applications ranging from CAD model generation to metamaterial selection.
Toward Explainable Deep Anomaly Detection
Authors: Guansong Pang, Charu C. Aggarwal
Venue: KDD
Year: 2021

Anomaly explanation, also known as anomaly localization, is as important as, if not more than, anomaly detection in many real-world applications. However, it is challenging to build explainable detection models due to the lack of anomaly-supervisory information and the unbounded nature of anomaly; most existing studies exclusively focus on the detection task only, including the recently emerging deep learning-based anomaly detection that leverages neural networks to learn expressive low-dimensional representations or anomaly scores for the detection task. Deep learning models, including deep anomaly detection models, are often constructed as black boxes, which have been criticized for the lack of explainability of their prediction results. To tackle this explainability issue, there have been numerous techniques introduced over the years, many of which can be utilized or adapted to offer highly explainable detection results. This tutorial aims to present a comprehensive review of the advances in deep learning-based anomaly detection and explanation. We first review popular state-of-the-art deep anomaly detection methods from different categories of approaches, followed by the introduction of a number of principled approaches used to provide anomaly explanation for deep detection models. Through this tutorial, we aim to promote the development in algorithms, theories and evaluation of explainable deep anomaly detection in the machine learning and data mining community. The slides and other materials of the tutorial are made publicly available at https://tinyurl.com/explainableDeepAD.
Anomaly and Novelty Detection, Explanation, and Accommodation (ANDEA)
Authors: Guansong Pang, Jundong Li, Anton van den Hengel, Longbing Cao, Thomas G. Dietterich
Venue: KDD
Year: 2021

The detection of, explanation of, and accommodation to anomalies and novelties are active research areas in multiple communities, including data mining, machine learning, and computer vision. They are applied in various guises including anomaly detection, out-of-distribution example detection, adversarial example recognition and detection, curiosity-driven reinforcement learning, and open-set recognition and adaptation, all of which are of great interest to the SIGKDD community. The techniques developed have been applied in a wide range of domains including fraud detection and anti-money laundering in fintech, early disease detection, intrusion detection in large-scale computer networks and data centers, defending AI systems from adversarial attacks, and in improving the practicality of agents through overcoming the closed-world assumption. This workshop is focused on Anomaly and Novelty Detection, Explanation, and Accommodation (ANDEA). It will gather researchers and practitioners from data mining, machine learning, and computer vision communities and diverse knowledge background to promote the development of fundamental theories, effective algorithms, and novel applications of anomaly and novelty detection, characterization, and adaptation. All materials of keynote talks, panel discussion, and accepted papers of the workshop are made available at https://tinyurl.com/andea2021.
Faster and Generalized Temporal Triangle Counting, via Degeneracy Ordering
Authors: Noujan Pashanasangi, C. Seshadhri
Venue: KDD
Year: 2021

Triangle counting is a fundamental technique in network analysis, that has received much attention in various input models. The vast majority of triangle counting algorithms are targeted to static graphs. Yet, many real-world graphs are directed and temporal, where edges come with timestamps. Temporal triangles yield much more information, since they account for both the graph topology and the timestamps. Temporal triangle counting has seen a few recent results, but there are varying definitions of temporal triangles. In all cases, temporal triangle patterns enforce constraints on the time interval between edges (in the triangle). We define a general notion (δ1,3, δ1,2, δ2,3)-temporal triangles that allows for separate time constraints for all pairs of edges. Our main result is a new algorithm, DOTTT (Degeneracy Oriented Temporal Triangle Totaler), that exactly counts all directed variants of (δ1,3, δ1,2, δ2,3)-temporal triangles. Using the classic idea of degeneracy ordering with careful combinatorial arguments, we can prove that DOTTT runs in O(mκłog m) time, where m is the number of (temporal) edges and κ is the graph degeneracy (max core number). Up to log factors, this matches the running time of the best static triangle counters. Moreover, this running time is better than existing. DOTTT has excellent practical behavior and runs twice as fast as existing state-of-the-art temporal triangle counters (and is also more general). For example, DOTTT computes all types of temporal queries in Bitcoin temporal network with half a billion edges in less than an hour on a commodity machine.
2nd International Workshop on Data Quality Assessment for Machine Learning
Authors: Hima Patel, Fuyuki Ishikawa, Laure Berti-Équille, Nitin Gupta, Sameep Mehta, Satoshi Masuda, Shashank Mujumdar, Shazia Afzal, Srikanta Bedathur, Yasuharu Nishi
Venue: KDD
Year: 2021

The 2nd International Workshop on Data Quality Assessment for Machine Learning (DQAML'21) is organized in conjunction with the Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD). This workshop aims to serve as a forum for the presentation of research related to data quality assessment and remediation in AI/ML pipeline. Data quality is a critical issue in the data preparation phase and involves numerous challenging problems related to detection, remediation, visualization and evaluation of data issues. The workshop aims to provide a platform to researchers and practitioners to discuss such challenges across different modalities of data like structured, time series, text and graphical. The aim is to attract perspectives from both industrial and academic circles.
Data Pricing and Data Asset Governance in the AI Era
Authors: Jian Pei, Feida Zhu, Zicun Cong, Xuan Luo, Huiwen Liu, Xin Mu
Venue: KDD
Year: 2021

Data is one of the most critical resources in the AI Era. While substantial research has been dedicated to training machine learning models using various types of data, much less efforts have been invested in the exploration of assessing and governing data assets in end-to-end processes of machine learning and data science, that is, the pipeline where data is collected and processed, and then machine learning models are produced, requested, deployed, shared and evolved. To provide a state-of-the-art overall picture of this important and novel area and advocate the related research and development, we present a tutorial addressing two essential problems. First, in the pipeline of machine learning, how can data and machine learning models be priced properly so that contributions from various parties can be assessed and recognized in a fair manner? Second, in the collaboration among many parties in building, distributing and sharing machine learning models, how can data as assets be managed? Accordingly, the first part of our proposal surveys data and model pricing in the pipeline of machine learning, while the second part discusses data asset governance for collaborative artificial intelligence. Each part is self-contained. At the same time, the two parts echo each other and connect a series of interesting and important problems into a dynamic big picture.
VDS'21: Visualization in Data Science
Authors: Claudia Plant, Alvitta Ottley, Liang Gou, Torsten Möller, Adam Perer, Alexander Lex, Junming Shao
Venue: KDD
Year: 2021

Data science is the practice of deriving insight from data, enabled by modeling, computational methods, interactive visual analysis, and domain-driven problem solving. Data science draws from methodology developed in such fields as applied mathematics, statistics, machine learning, data mining, data management, visualization, and HCI. It drives discoveries in business, economy, biology, medicine, environmental science, the physical sciences, the humanities and social sciences, and beyond. Machine learning and data mining and visualization are integral parts of data science, and essential to enable sophisticated analysis of data. Nevertheless, both research areas are currently still rather separated and investigated by different communities rather independently. The goal of this workshop is to bring researchers from both communities together in order to discuss common interests, to talk about practical issues in application-related projects, and to identify open research problems. This summary gives a brief overview of the ACM KDD Workshop on Visualization in Data Science (VDS at ACM KDD and IEEE VIS), which will take place virtually on Aug 14-18, 2021 (Held in conjunction with KDD'21). The workshop website is available at: http://www.visualdatascience.org/2021/
MaNIACS: Approximate Mining of Frequent Subgraph Patterns through Sampling
Authors: Giulia Preti, Gianmarco De Francisci Morales, Matteo Riondato
Venue: KDD
Year: 2021

We present MaNIACS, a sampling-based randomized algorithm for computing high-quality approximations of the collection of the subgraph patterns that are frequent in a single, large, vertex-labeled graph, according to the Minimum Node Image-based (MNI) frequency measure. The output of MaNIACS comes with strong probabilistic guarantees, obtained by using the empirical Vapnik-Chervonenkis (VC) dimension, a key concept from statistical learning theory, together with strong probabilistic tail bounds on the difference between the frequency of a pattern in the sample and its exact frequency. MaNIACS leverages properties of the MNI-frequency to aggressively prune the pattern search space, and thus to reduce the time spent in exploring subspaces containing no frequent patterns. In turn, this pruning leads to better bounds to the maximum frequency estimation error, which leads to increased pruning, resulting in a beneficial feedback effect. The results of our experimental evaluation of MaNIACS on real graphs show that it returns high-quality collections of frequent patterns in large graphs up to two orders of magnitude faster than the exact algorithm.
MiLeTS'21: 7th KDD Workshop on Mining and Learning from Time Series
Authors: Sanjay Purushotham, Yaguang Li, Zhengping Che
Venue: KDD
Year: 2021

Time series data are ubiquitous. Rapid advances in diverse sensing technologies, ranging from remote sensors to wearables and social sensing, are generating a rapid growth in the size and complexity of time series archives. This has resulted in a fundamental shift away from parsimonious, infrequent measurement to nearly continuous monitoring and recording. This demands development of new tools and solutions. The goals of this workshop are to: (1) highlight the significant challenges that underpin learning and mining from time series data (e.g. irregular sampling, spatiotemporal structure, and uncertainty quantification), (2) discuss recent algorithmic, theoretical, statistical, or systems-based developments for tackling these problems, and (3) synergize the research activities and discuss both new and open problems in time series analysis and mining.
Learning to Recommend Visualizations from Data
Authors: Xin Qian, Ryan A. Rossi, Fan Du, Sungchul Kim, Eunyee Koh, Sana Malik, Tak Yeon Lee, Joel Chan
Venue: KDD
Year: 2021

Visualization recommendation is important for exploratory analysis and making sense of the data quickly by automatically recommending relevant visualizations to the user. In this work, we propose the first end-to-end ML-based visualization recommendation system that leverages a large corpus of datasets and their relevant visualizations to learn a visualization recommendation model automatically. Then, given a new unseen dataset from an arbitrary user, the model automatically generates visualizations for that new dataset, derives scores for the visualizations, and outputs a list of recommended visualizations to the user ordered by effectiveness. We also describe an evaluation framework to quantitatively evaluate visualization recommendation models learned from a large corpus of visualizations and datasets. Through quantitative experiments, a user study, and qualitative analysis, we show that our end-to-end ML-based system recommends more effective and useful visualizations compared to existing state-of-the-art rule-based systems.
Retrieval & Interaction Machine for Tabular Data Prediction
Authors: Jiarui Qin, Weinan Zhang, Rong Su, Zhirong Liu, Weiwen Liu, Ruiming Tang, Xiuqiang He, Yong Yu
Venue: KDD
Year: 2021

Prediction over tabular data is an essential task in many data science applications such as recommender systems, online advertising, medical treatment, etc. Tabular data is structured into rows and columns, with each row as a data sample and each column as a feature attribute. Both the columns and rows of the tabular data carry useful patterns that could improve the model prediction performance. However, most existing models focus on the cross-column patterns yet overlook the cross-rowpatterns as they deal with single samples independently. In this work, we propose a general learning framework named Retrieval & Interaction Machine (RIM) that fully exploits both cross-row and cross-column patterns among tabular data. Specifically, RIM first leverages search engine techniques to efficiently retrieve useful rows of the table to assist the label prediction of the target row, then uses feature interaction networks to capture the cross-column patterns among the target row and the retrieved rows so as to make the final label prediction. We conduct extensive experiments on 11 datasets of three important tasks, i.e., CTR prediction (classification), top-n recommendation (ranking) and rating prediction (regression). Experimental results show that RIM achieves significant improvements over the state-of-the-art and various baselines, demonstrating the superiority and efficacy of RIM.
Network-Wide Traffic States Imputation Using Self-interested Coalitional Learning
Authors: Huiling Qin, Xianyuan Zhan, Yuanxun Li, Xiaodu Yang, Yu Zheng
Venue: KDD
Year: 2021

Accurate network-wide traffic state estimation is vital to many transportation operations and urban applications. However, existing methods often suffer from the scalability issue when performing real-time inference at the city-level, or not robust enough under limited data. Currently, GPS trajectory data from probe vehicles has become a popular data source for many transportation applications. GPS trajectory data has large coverage area, which is ideal for network-wide applications, but also has the disadvantage of being sparse and highly heterogeneous among different time and locations. In this study, we focus on developing a robust and interpretable network-wide traffic state imputation framework using partially observed traffic information. We introduce a new learning strategy, called self-interested coalitional learning (SCL), which forges cooperation between a main self-interested semi-supervised learning task and a discriminator as a critic to facilitate main task training while providing interpretability on the results. In our detailed model, we use a temporal graph convolutional variational autoencoder (TG-VAE) as the reconstructor, which models the complex spatio-temporal pattern in data and solves the main traffic state imputation task. A discriminator is introduced to output interpretable imputation confidence on the estimated results and also help to enhance the performance of the reconstructor. The framework is evaluated using a large GPS trajectory dataset from taxis in Jinan, China. Extensive experiments against the state-of-the-art baselines demonstrate the effectiveness and robustness of the proposed method for network-wide traffic state estimation.
ImGAGN: Imbalanced Network Embedding via Generative Adversarial Graph Networks
Authors: Liang Qu, Huaisheng Zhu, Ruiqi Zheng, Yuhui Shi, Hongzhi Yin
Venue: KDD
Year: 2021

Imbalanced classification on graphs is ubiquitous yet challenging in many real-world applications, such as fraudulent node detection. Recently, graph neural networks (GNNs) have shown promising performance on many network analysis tasks. However, most existing GNNs have almost exclusively focused on the balanced networks, and would get unappealing performance on the imbalanced networks. To bridge this gap, in this paper, we present a generative adversarial graph network model, called ImGAGN to address the imbalanced classification problem on graphs. It introduces a novel generator for graph structure data, named GraphGenerator, which can simulate both the minority class nodes' attribute distribution and network topological structure distribution by generating a set of synthetic minority nodes such that the number of nodes in different classes can be balanced. Then a graph convolutional network (GCN) discriminator is trained to discriminate between real nodes and fake (i.e., generated) nodes, and also between minority nodes and majority nodes on the synthetic balanced network. To validate the effectiveness of the proposed method, extensive experiments are conducted on four real-world imbalanced network datasets. Experimental results demonstrate that the proposed method ImGAGN outperforms state-of-the-art algorithms for semi-supervised imbalanced node classification task.
Individual Treatment Prescription Effect Estimation in a Low Compliance Setting
Authors: Thibaud Rahier, Amélie Héliou, Matthieu Martin, Christophe Renaudin, Eustache Diemert
Venue: KDD
Year: 2021

Individual Treatment Effect (ITE) estimation is an extensively researched problem, with applications in various domains. We model the case where there exists heterogeneous non-compliance to a randomly assigned treatment, a typical situation in health (because of non-compliance to prescription) or digital advertising (because of competition and ad blockers for instance). The lower the compliance, the more the effect of treatment prescription - or individual prescription effect (IPE) - signal fades away and becomes harder to estimate. We propose a new approach for the estimation of the IPE that takes advantage of observed compliance information to prevent signal fading. Using the Structural Causal Model framework and do-calculus, we define a general mediated causal effect setting and propose a corresponding estimator which consistently recovers the IPE with asymptotic variance guarantees. Finally, we conduct experiments on both synthetic and real-world datasets that highlight the benefit of the approach, which consistently improves state-of-the-art in low compliance settings.
Lambda Learner: Fast Incremental Learning on Data Streams
Authors: Rohan Ramanath, Konstantin Salomatin, Jeffrey D. Gee, Kirill Talanine, Onkar Dalal, Gungor Polatkan, Sara Smoot, Deepak Kumar
Venue: KDD
Year: 2021

One of the most well-established applications of machine learning is in deciding what content to show website visitors. When observation data comes from high-velocity, user-generated data streams, machine learning methods perform a balancing act between model complexity, training time, and computational costs. Furthermore, when model freshness is critical, the training of models becomes time-constrained. Parallelized batch offline training, although horizontally scalable, is often not time-considerate or cost-effective. In this paper, we propose Lambda Learner, a new framework for training models by incremental updates in response to mini-batches from data streams. We show that the resulting model of our framework closely estimates a periodically updated model trained on offline data and outperforms it when model updates are time-sensitive. We provide theoretical proof that the incremental learning updates improve the loss-function over a stale batch model. We present a large-scale deployment on the sponsored content platform for a large social network, serving hundreds of millions of users across different channels (e.g., desktop, mobile). We address challenges and complexities from both algorithms and infrastructure perspectives, illustrate the system details for computation, storage, stream processing training data, and open-source the system.