OpenRaiser/Intern-Atlas huggingface dataset:
DotHash: Estimating Set Similarity Metrics for Link Prediction and Document Deduplication
Authors: Igor Nunes, Mike Heddes, Pere Vergés, Danny Abraham, Alexander V. Veidenbaum, Alex Nicolau, Tony Givargis
Venue: KDD
Year: 2023

Metrics for set similarity are a core aspect of several data mining tasks. To remove duplicate results in a Web search, for example, a common approach looks at the Jaccard index between all pairs of pages. In social network analysis, a much-celebrated metric is the Adamic-Adar index, widely used to compare node neighborhood sets in the important problem of predicting links. However, with the increasing amount of data to be processed, calculating the exact similarity between all pairs can be intractable. The challenge of working at this scale has motivated research into efficient estimators for set similarity metrics. The two most popular estimators, MinHash and SimHash, are indeed used in applications such as document deduplication and recommender systems where large volumes of data need to be processed. Given the importance of these tasks, the demand for advancing estimators is evident. We propose DotHash, an unbiased estimator for the intersection size of two sets. DotHash can be used to estimate the Jaccard index and, to the best of our knowledge, is the first method that can also estimate the Adamic-Adar index and a family of related metrics. We formally define this family of metrics, provide theoretical bounds on the probability of estimate errors, and analyze its empirical performance. Our experimental results indicate that DotHash is more accurate than the other estimators in link prediction and detecting duplicate documents with the same complexity and similar comparison time.
A Higher-Order Temporal H-Index for Evolving Networks
Authors: Lutz Oettershagen, Nils M. Kriege, Petra Mutzel
Venue: KDD
Year: 2023

The H-index of a node in a static network is the maximum value h such that at least h of its neighbors have a degree of at least h. Recently, a generalized version, the n-th order H-index, was introduced, allowing to relate degree centrality, H-index, and the k-core of a node. We extend the n-th order H-index to temporal networks and define corresponding temporal centrality measures and temporal core decompositions. Our n-th order temporal H-index respects the reachability in temporal networks leading to node rankings, which reflect the importance of nodes in spreading processes. We derive natural decompositions of temporal networks into subgraphs with strong temporal coherence. We analyze a recursive computation scheme and develop a highly scalable streaming algorithm. Our experimental evaluation demonstrates the efficiency of our algorithms and the conceptional validity of our approach. Specifically, we show that the n-th order temporal H-index is a strong heuristic for identifying possible super-spreaders in evolving social networks and detects temporally well-connected components.
Deep Landscape Forecasting in Multi-Slot Real-Time Bidding
Authors: Weitong Ou, Bo Chen 0023, Yingxuan Yang, Xinyi Dai, Weiwen Liu, Weinan Zhang 0001, Ruiming Tang, Yong Yu 0001
Venue: KDD
Year: 2023

Real-Time Bidding (RTB) has shown remarkable success in display advertising and has been employed in other advertising scenarios, e.g., sponsored search advertising with multiple ad slots. Many current RTB techniques built for single-slot display advertising are thus no longer applicable, especially in the bid landscape forecasting. Landscape forecasting predicts market competition, including the highest bid price and winning probability, which is preliminary and crucial for the subsequent bidding strategy design. In the multi-slot advertising, predicting the winning prices for each position requires a more precise differentiation of bids among top advertisers. Furthermore, defining the winning probability and addressing censorship issues are not as straightforward as in the case of a single slot. In view of these challenges, how to forecast the bidding landscape in the multi-slot environment remains open.
Rewiring Police Officer Training Networks to Reduce Forecasted Use of Force
Authors: Ritika Pandey, Jeremy G. Carter, James H. Hill, George O. Mohler
Venue: KDD
Year: 2023

Research has shown that police officer involved shootings, misconduct and excessive use of force complaints exhibit network effects, where officers are at greater risk of being involved in these incidents when they socialize with officers who have a history of use of force and misconduct. In this work, we first construct a network survival model for the time-to-event of use of force incidents involving new police trainees. The model includes network effects of the diffusion of risk from field training officer (FTO) to trainee. We then introduce a network rewiring algorithm to maximize the expected time to use of force events upon completion of field training. We study several versions of the algorithm, including constraints that encourage demographic diversity of FTOs. Using data from Indianapolis, we show that rewiring the network can increase the expected time (in days) of a recruit's first use of force incident by 8%. We then discuss the potential benefits and challenges associated with implementing such an algorithm in practice.
Deep Weakly-supervised Anomaly Detection
Authors: Guansong Pang, Chunhua Shen, Huidong Jin 0001, Anton van den Hengel
Venue: KDD
Year: 2023

Recent semi-supervised anomaly detection methods that are trained using small labeled anomaly examples and large unlabeled data (mostly normal data) have shown largely improved performance over unsupervised methods. However, these methods often focus on fitting abnormalities illustrated by the given anomaly examples only (i.e., seen anomalies), and consequently they fail to generalize to those that are not, i.e., new types/classes of anomaly unseen during training. To detect both seen and unseen anomalies, we introduce a novel deep weakly-supervised approach, namely Pairwise Relation prediction Network (PReNet), that learns pairwise relation features and anomaly scores by predicting the relation of any two randomly sampled training instances, in which the pairwise relation can be anomaly-anomaly, anomaly-unlabeled, or unlabeled-unlabeled. Since unlabeled instances are mostly normal, the relation prediction enforces a joint learning of anomaly-anomaly, anomaly-normal, and normal-normal pairwise discriminative patterns, respectively. PReNet can then detect any seen/unseen abnormalities that fit the learned pairwise abnormal patterns, or deviate from the normal patterns. Further, this pairwise approach also seamlessly and significantly augments the training anomaly data. Empirical results on 12 real-world datasets show that PReNet significantly outperforms nine competing methods in detecting seen and unseen anomalies. We also theoretically and empirically justify the robustness of our model w.r.t. anomaly contamination in the unlabeled data. The code is available at https://github.com/mala-lab/PReNet.
Domain-Guided Spatio-Temporal Self-Attention for Egocentric 3D Pose Estimation
Authors: Jinman Park, Kimathi Kaai, Saad Hossain, Norikatsu Sumi, Sirisha Rambhatla, Paul W. Fieguth
Venue: KDD
Year: 2023

Vision-based ego-centric 3D human pose estimation (ego-HPE) is essential to support critical applications of xR-technologies. However, severe self-occlusions and strong distortion introduced by the fish-eye view from the head mounted camera, make ego-HPE extremely challenging. To address these challenges, we propose a domain-guided spatio-temporal transformer model that leverages information specific to ego-views. Powered by this domain-guided transformer, we build Egocentric Spatio-Temporal Self-Attention Network (Ego-STAN), which uses 2D image representations and spatio-temporal attention to address both distortions and self-occlusions in ego-HPE. Additionally, we introduce a spatial concept called feature map tokens (FMT) which endows Ego-STAN with the ability to draw complex spatio-temporal information encoded in ego-centric videos. Our quantitative evaluation on the contemporary xR-EgoPose dataset, achieves a 38.2% improvement on the highest error joints against the SOTA ego-HPE model, while accomplishing a 22% decrease in the number of parameters. Finally, we also demonstrate the generalization capabilities of our model to real-world HPE tasks beyond ego-views achieving 7.7% improvement on 2D human pose estimation with the Human3.6M dataset. Our code is also made available at: https://github.com/jmpark0808/Ego-STAN
Extreme Multi-Label Classification for Ad Targeting using Factorization Machines
Authors: Martin Pavlovski, Srinath Ravindran, Djordje Gligorijevic, Shubham Agrawal, Ivan Stojkovic, Nelson Segura-Nunez, Jelena Gligorijevic
Venue: KDD
Year: 2023

Applications involving Extreme Multi-Label Classification (XMLC) face several practical challenges with respect to scale, model size and prediction latency, while maintaining satisfactory predictive accuracy. In this paper, we propose a Multi-Label Factorization Machine (MLFM) model, which addresses some of the challenges in XMLC problems. We use behavioral ad targeting as a case study to illustrate the benefits of the MLFM model. Predicting user qualifications for targeting segments plays a major role in both personalization and real-time bidding. Considering the large number of segments and the prediction time requirements of real-world production systems, building scalable models is often difficult and computationally burdensome. To cope with these challenges, we (1) reformulate the problem of assigning users to segments as a multi-label classification (XMLC) problem, and (2) leverage the benefits of the conventional FM model and generalize its capacity to joint prediction across a large number of targeting segments. We have shown that the MLFM model is both effective and computationally efficient compared to several baseline models on publicly available datasets in addition to the targeting use case.
Few-shot Low-resource Knowledge Graph Completion with Multi-view Task Representation Generation
Authors: Shichao Pei, Ziyi Kou, Qiannan Zhang, Xiangliang Zhang 0001
Venue: KDD
Year: 2023

Despite their capacity to convey knowledge, most existing knowledge graphs (KGs) are created for specific domains using low-resource data sources, especially those in non-global languages, and thus unavoidably suffer from the incompleteness problem. The automatic discovery of missing triples for KG completion is thus hindered by the challenging long-tail relations problem in low-resource KGs. Few-shot learning models trained on rich-resource KGs are unable to tackle this challenge due to a lack of generalization. To alleviate the impact of the intractable long-tail problem on low-resource KG completion, in this paper, we propose a novel few-shot learning framework empowered by multi-view task representation generation. The framework consists of four components, i.e., few-shot learner, perturbed few-shot learner, relation knowledge distiller, and pairwise contrastive distiller. The key idea is to utilize the different views of each few-shot task to improve and regulate the training of the few-shot learner. For each few-shot task, instead of augmenting it by complicated task designs, we generate its representation of different views using the relation knowledge distiller and perturbed few-shot learner, which are obtained by distilling knowledge from a KG encoder and perturbing the few-shot learner. Then, the generated representation of different views is utilized by the pairwise contrastive distiller based on a teacher-student framework to distill the knowledge of how to represent relations from different views into the few-shot learner and facilitate few-shot learning. Extensive experiments conducted on several real-world low-resource KGs validate the effectiveness of our proposed method.
Efficient Centrality Maximization with Rademacher Averages
Authors: Leonardo Pellegrina
Venue: KDD
Year: 2023

The identification of the set of k most central nodes of a graph, or centrality maximization, is a key task in network analysis, with various applications ranging from finding communities in social and biological networks to understanding which seed nodes are important to diffuse information in a graph. As the exact computation of centrality measures does not scale to modern-sized networks, the most practical solution is to resort to rigorous, but efficiently computable, randomized approximations. In this work we present CentRA, the first algorithm based on progressive sampling to compute high-quality approximations of the set of k most central nodes. CentRA is based on a novel approach to efficiently estimate Monte Carlo Rademacher Averages, a powerful tool from statistical learning theory to compute sharp data-dependent approximation bounds. Then, we study the sample complexity of centrality maximization using the VC-dimension, a key concept from statistical learning theory. We show that the number of random samples required to compute high-quality approximations scales with finer characteristics of the graph, such as its vertex diameter, or of the centrality of interest, significantly improving looser bounds derived from standard techniques. We apply CentRA to analyze large real-world networks, showing that it significantly outperforms the state-of-the-art approximation algorithm in terms of number of samples, running times, and accuracy.
Entity-aware Multi-task Learning for Query Understanding at Walmart
Authors: Zhiyuan Peng 0001, Vachik S. Dave, Nicole McNabb, Rahul Sharnagat, Alessandro Magnani, Ciya Liao, Yi Fang 0008, Sravanthi Rajanala
Venue: KDD
Year: 2023

Query Understanding (QU) is a fundamental process in E-commerce search engines by extracting the shopping intents of customers. It usually includes a set of different tasks such as named entity recognization and query classification. Traditional approaches often tackle each task separately by its own network, which leads to excessive workload for development and maintenance as well as increased latency and resource usage in large-scale E-commerce platforms. To tackle these challenges, this paper presents a multi-task learning approach to query understanding at Walmart. We experimented with several state-of-the-art multi-task learning architectures including MTDNN, MMoE, and PLE. Furthermore, we propose a novel large-scale entity-aware multi-task learning model (EAMT)1 by retrieving entities from engagement data as query context to augment the query representation. To the best of our knowledge, there exists no prior work on multi-task learning for E-commerce query understanding. Comprehensive offline experiments are conducted on industry-scale datasets (up to 965M queries) to illustrate the effectiveness of our approach. The results from online experiments show substantial gains in key accuracy and latency metrics. https://github.com/zhiyuanpeng/KDD2023-EAMT
Learning from Positive and Unlabeled Multi-Instance Bags in Anomaly Detection
Authors: Lorenzo Perini, Vincent Vercruyssen, Jesse Davis
Venue: KDD
Year: 2023

In the multi-instance learning (MIL) setting instances are grouped together into bags. Labels are provided only for the bags and not on the level of individual instances. A positive bag label means that at least one instance inside the bag is positive, while a negative bag label restricts all the instances in the bag to be negative. MIL data naturally arises in many contexts, such as anomaly detection, where labels are rare and costly, and one often ends up annotating the label for sets of instances. Moreover, in many real-world anomaly detection problems, only positive labels are collected because they usually represent critical events. Such a setting, where only positive labels are provided along with unlabeled data, is called Positive and Unlabeled (PU) learning. Despite being useful for several use cases, there is no work dedicated to learning from positive and unlabeled data in a multi-instance setting for anomaly detection. Therefore, we propose the first method that learns from PU bags in anomaly detection. Our method uses an autoencoder as an underlying anomaly detector. We alter the autoencoder's objective function and propose a new loss that allows it to learn from positive and unlabeled bags of instances. We theoretically analyze this method. Experimentally, we evaluate our method on 30 datasets and show that it performs better than multiple baselines adapted to work in our setting.
Graph Neural Networks in TensorFlow
Authors: Bryan Perozzi, Sami Abu-El-Haija, Anton Tsitsulin
Venue: KDD
Year: 2023

Graphs are general data structures that can represent information from a variety of domains (social, biomedical, online transactions, and many more). Graph Neural Networks (GNNs) are quickly becoming the de-facto Machine Learning models for learning from Graph data and hereby infer missing information, such as, predicting labels of nodes or imputing missing edges.
How to DP-fy ML: A Practical Tutorial to Machine Learning with Differential Privacy
Authors: Natalia Ponomareva 0001, Sergei Vassilvitskii, Zheng Xu 0002, Brendan McMahan, Alexey Kurakin, Chiyaun Zhang
Venue: KDD
Year: 2023

Machine Learning (ML) models are ubiquitous in real world applications and are a constant focus of research. At the same time, the community has started to realize the importance of protecting the privacy of models' training data.
The 9th SIGKDD International Workshop on Mining and Learning from Time Series
Authors: Sanjay Purushotham, Dongjin Song, Qingsong Wen, Jun Huan, Cong Shen 0001, Yuriy Nevmyvaka
Venue: KDD
Year: 2023

Time series data has become pervasive across domains such as finance, transportation, retail, entertainment, and healthcare. This shift towards continuous monitoring and recording, fueled by advancements in sensing technologies, necessitates the development of new tools and solutions. Despite extensive study, the importance of time series analysis continues to increase. However, modern time series data present challenges to existing techniques, including irregular sampling and spatiotemporal structures. Time series mining research is both challenging and rewarding as it connects diverse disciplines and requires interdisciplinary 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, uncertainty quantification), (2) discuss recent algorithmic, theoretical, statistical, or systems-based developments for tackling these problems, and (3) to synergize the research activities and discuss both new and open problems in time series analysis and mining. In summary, our workshop will focus on both the theoretical and practical aspects of time series data analysis and will provide a platform for researchers and practitioners from academia and industry to discuss potential research directions and critical technical issues and present solutions to tackle related issues in practical applications. We will invite researchers and practitioners from the related areas of AI, machine learning, data science, statistics, and many others to contribute to this workshop.
KDD-2023 Workshop on Decision Intelligence and Analytics for Online Marketplaces
Authors: Zhiwei (Tony) Qin, Rui Song 0006, Jieping Ye, Hongtu Zhu, Michael I. Jordan
Venue: KDD
Year: 2023

Online marketplace is a digital platform that connects buyers (demand) and sellers (supply) and provides exposure opportunities that individual participants would not otherwise have access to. Online marketplaces exist in a diverse set of domains and industries, for example, rideshare (Lyft, DiDi, Uber), house rental (Airbnb), real estate (Beke), online retail (Amazon, Ebay), and food ordering and delivery (Doordash, Meituan). Besides academia, many companies and institutions are researching on topics specific to their particular domains. The fundamental mechanism of an online marketplace is to match supply and demand to generate transactions, with objectives considering service quality, participants experience, financial and operational efficiency. It is valuable to bring together researchers and practitioners from different application domains to discuss their experiences, challenges, and opportunities to leverage cross-domain knowledge. The goal of this workshop is to offer an opportunity to appreciate the diversity in applications, to draw connections to inform decision optimization across different industries, and to discover new problems that are fundamental to marketplaces of different domains. The previous version of this workshop at KDD-2022 was a tremendous success in terms of participation, technical contribution, and community interest. This updated version of the workshop is especially timely to cover the issues and algorithms pertinent to general online marketplaces, specific problems and applications arising from those diverse domains, as well as emerging topics such as competition and resilience to market condition shifts.
un-xPass: Measuring Soccer Player's Creativity
Authors: Pieter Robberechts, Maaike Van Roy, Jesse Davis
Venue: KDD
Year: 2023

Creativity is highly valued in soccer players. It contributes to exciting and unpredictable play, which can help teams to overcome defensive strategies and create scoring opportunities. Consequently, evaluating the creative abilities of players is an important aspect of the player recruitment process. However, there is currently no clear way to measure creativity in soccer. It is not captured by the typical result-based performance indicators, as being creative entails going beyond just doing something useful, to accomplishing something useful but in a unique or atypical way. Therefore in this paper, we define a novel metric to quantify the level of creativity involved in a player's passes. Our Creative Decision Rating (CDR) utilizes machine learning techniques to assess two important factors: the originality of a pass, and its value in terms of increasing the team's chances of scoring a goal. We validated our metric on StatsBomb 360 contextual event stream data of the 2021/22 English Premier League season and show through a number of use cases that it provides another angle on a player's skill, complementing existing player evaluation metrics. Overall, our metric provides a concise method for capturing and quantifying the creativity of soccer players and could have important implications for player recruitment and talent development in the sport.