OpenRaiser/Intern-Atlas huggingface dataset:
Where Have You Been? A Study of Privacy Risk for Point-of-Interest Recommendation
Authors: Kunlin Cai, Jinghuai Zhang, Zhiqing Hong, William Shand, Guang Wang 0001, Desheng Zhang 0002, Jianfeng Chi, Yuan Tian 0001
Venue: KDD
Year: 2024

As location-based services (LBS) have grown in popularity, more human mobility data has been collected. The collected data can be used to build machine learning (ML) models for LBS to enhance their performance and improve overall experience for users. However, the convenience comes with the risk of privacy leakage since this type of data might contain sensitive information related to user identities, such as home/work locations. Prior work focuses on protecting mobility data privacy during transmission or prior to release, lacking the privacy risk evaluation of mobility data-based ML models. To better understand and quantify the privacy leakage in mobility data-based ML models, we design a privacy attack suite containing data extraction and membership inference attacks tailored for point-of-interest (POI) recommendation models, one of the most widely used mobility data-based ML models. These attacks in our attack suite assume different adversary knowledge and aim to extract different types of sensitive information from mobility data, providing a holistic privacy risk assessment for POI recommendation models. Our experimental evaluation using two real-world mobility datasets demonstrates that current POI recommendation models are vulnerable to our attacks. We also present unique findings to understand what types of mobility data are more susceptible to privacy attacks. Finally, we evaluate defenses against these attacks and highlight future directions and challenges.
CompanyKG: A Large-Scale Heterogeneous Graph for Company Similarity Quantification
Authors: Lele Cao, Vilhelm von Ehrenheim, Mark Granroth-Wilding, Richard Anselmo Stahl, Andrew McCornack, Armin Catovic, Dhiana Deva Cavalcanti Rocha
Venue: KDD
Year: 2024

This paper presents CompanyKG (version 2), a large-scale heterogeneous graph developed for fine-grained company similarity quantification and relationship prediction, crucial for applications in the investment industry such as market mapping, competitor analysis, and mergers and acquisitions. CompanyKG comprises 1.17 million companies represented as graph nodes, enriched with company description embeddings, and 51.06 million weighted edges denoting 15 distinct inter-company relations. To facilitate a thorough evaluation of methods for company similarity quantification and relationship prediction, we have created four annotated evaluation tasks: similarity prediction, competitor retrieval, similarity ranking, and edge prediction. We offer extensive benchmarking results for 11 reproducible predictive methods, categorized into three groups: node-only, edge-only, and node+edge. To our knowledge, CompanyKG is the first large-scale heterogeneous graph dataset derived from a real-world investment platform, specifically tailored for quantifying inter-company similarity and relationships.
DiffusionE: Reasoning on Knowledge Graphs via Diffusion-based Graph Neural Networks
Authors: Zongsheng Cao, Jing Li 0114, Zigan Wang, Jinliang Li
Venue: KDD
Year: 2024

Graph Neural Networks (GNNs) have demonstrated powerful capabilities in reasoning within Knowledge Graphs (KGs), gathering increasing attention. Our idea stems from the observation that the prior work typically employs hand-designed or sample-designed paradigms in the process of message propagation, engaging a set of adjacent entities at each step of propagation. As a result, such methods struggle with the increasing number of entities involved as propagation steps extend. Moreover, they neglect the message interactions between adjacent entities and propagation relations in KG reasoning, leading to semantic inconsistency during the message aggregation phase. To address these issues, we introduce a novel knowledge graph embedding method through a diffusion process, termed DiffusionE. Specifically, we reformulate the message propagation in knowledge reasoning as a diffusion process, regarding the message semantics as the diffusion signal. In this sense, guided by semantic information, messages can be transmitted between nodes effectively and adaptively. Furthermore, the theoretical analysis suggests our method can leverage an optimal diffusivity for message propagation in the semantic interactions of KGs. It shows that DiffusionE effectively leverages message interactions between entities and propagation relations, ensuring semantic consistency in KG reasoning. Comprehensive experiments reveal that our method attains state-of-the-art performance compared to prior work on several well-established benchmarks.
Tackling Instance-Dependent Label Noise with Class Rebalance and Geometric Regularization
Authors: Shuzhi Cao, Jianfei Ruan, Bo Dong 0001, Bin Shi
Venue: KDD
Year: 2024

In label-noise learning, accurately identifying the transition matrix is crucial for developing statistically consistent classifiers. This task is complicated by instance-dependent noise, which introduces identifiability challenges in the absence of stringent assumptions. Existing methods use neural networks to estimate the transition matrix by initially extracting confident clean instances. However, this extraction process is hindered by severe inter-class imbalance and a bias toward selecting unambiguous intra-class instances, leading to a distorted understanding of noise patterns. To tackle these challenges, our paper introduces a Class Rebalance and Geometric Regularization-based Framework (CRGR). CRGR employs a smoothed, noise-tolerant reweighting mechanism to equilibrate inter-class representation, thereby mitigating the risk of model overfitting to dominant classes. Additionally, recognizing that instances with similar characteristics often exhibit parallel noise patterns, we propose that the transition matrix should mirror the similarity of the feature space. This insight promotes the inclusion of ambiguous instances in training, serving as a form of geometric regularization. Such a strategy enhances the model's ability to navigate diverse noise patterns and strengthens its generalization capabilities. By addressing both inter-class and intra-class biases, CRGR offers a more balanced and robust classification model. Extensive experiments on both synthetic and real-world datasets demonstrate CRGR's superiority over existing state-of-the-art methods, significantly boosting classification accuracy and showcasing its effectiveness in handling instance-dependent noise.
Equity, Diversity & Inclusion (EDI): Special Day at ACM KDD 2024
Authors: Tania Cerquitelli, Amin Mantrach
Venue: KDD
Year: 2024

The Equity, Diversity & Inclusion event is a special day organized in conjunction with KDD '24, the 30 ^th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, which will take place from Sunday, August 25 to Thursday, August 29, 2024 at the Center de Convencions Internacional de Barcelona in Barcelona, Spain.This special day, scheduled for August 28, 2024, promotes equity, diversity, and inclusion (EDI) in data science, artificial intelligence, and beyond. It will bring together academics, researchers, practitioners, and human resources professionals (i) to present algorithms, techniques, methodologies, and projects in data science that enable responsible data processing and modeling; (ii) to discuss policies, best practices, and guidelines to promote an inclusive work environment and effective collaboration; (iii) to share personal stories to encourage young researchers, including those from groups unrepresented in the research community, to develop strong careers in data science; and (iv) to collaboratively develop and discuss an EDI Manifesto to promote an inclusive workplace environment and guiding principles in the development of research activities.
Diffusion Model-based Mobile Traffic Generation with Open Data for Network Planning and Optimization
Authors: Haoye Chai, Tao Jiang 0002, Li Yu
Venue: KDD
Year: 2024

With the rapid development of the Fifth Generation Mobile Communication Technology (5G) networks, network planning and optimization have become increasingly crucial. Generating high-fidelity network traffic data can preemptively estimate the network demands of mobile users, which holds potential for network operators to improve network performance. However, the data required by existing generation methods is predominantly inaccessible to the public, resulting in a lack of reproducibility for the models and high deployment costs in practice. In this article, we propose an Open data-based Diffusion model for mobile traffic generation (OpenDiff), where a multi-positive contrastive learning algorithm is designed to construct conditional information for the diffusion model using entirely publicly available satellite remote sensing images, Point of Interest (POI), and population data. The conditional information contains relevant human activities in geographical areas, which can effectively guide the generation of network traffic data. We further design an attention-based fusion mechanism to capture the implicit correlations between network traffic and human activity features, enhancing the model's controllable generation capability. We conduct evaluations on three different cities with varying scales, where experimental results verify that our proposed model outperforms existing methods by 14.36% and 13.05% in terms of generation fidelity and controllability. To further validate the effectiveness of the model, we leverage the generated traffic data to assist the operators with network planning on a real-world network optimization platform of China Mobile Communications Corporation. The source code is available online:https://github.com/impchai/OpenDiff-diffusion-model-with-open-data.
A Hierarchical Context Augmentation Method to Improve Retrieval-Augmented LLMs on Scientific Papers
Authors: Tian-Yi Che, Xian-Ling Mao, Tian Lan 0003, Heyan Huang
Venue: KDD
Year: 2024

Scientific papers of a large scale on the Internet encompass a wealth of data and knowledge, attracting the attention of numerous researchers. To fully utilize these knowledge, Retrieval-Augmented Large Language Models (LLMs) usually leverage large-scale scientific corpus to train and then retrieve relevant passages from external memory to improve generation, which have demonstrated outstanding performance. However, existing methods can only capture one-dimension fragmented textual information without incorporating hierarchical structural knowledge, eg. the deduction relationship of abstract and main body, which makes it difficult to grasp the central thought of papers. To tackle this problem, we propose a hierarchical context augmentation method, which helps Retrieval-Augmented LLMs to autoregressively learn the structure knowledge of scientific papers. Specifically, we utilize the document tree to represent the hierarchical relationship of a paper and enhance the structure information of scientific context from three aspects: scale, format and global information. First, we think each top-bottom path of document tree is a logical independent context, which can be used to largely increase the scale of extracted structural corpus. Second, we propose a novel label-based format to represent the structure of context in textual sequences, unified between training and inference. Third, we introduce the global information of retrieved passages to further enhance the structure of context. Extensive experiments on three scientific tasks show that the proposed method significantly improves the performance of Retrieval-Augmented LLMs on all tasks. Besides, our method achieves start-of-art performance in Question Answer task and outperforms ChatGPT. Moreover, it also brings considerate gains with irrelevant retrieval passages, illustrating its effectiveness on practical application scenarios.
QGRL: Quaternion Graph Representation Learning for Heterogeneous Feature Data Clustering
Authors: Junyang Chen, Yuzhu Ji, Rong Zou, Yiqun Zhang 0006, Yiu-ming Cheung
Venue: KDD
Year: 2024

Clustering is one of the most commonly used techniques for unsupervised data analysis. As real data sets are usually composed of numerical and categorical features that are heterogeneous in nature, the heterogeneity in the distance metric and feature coupling prevents deep representation learning from achieving satisfactory clustering accuracy. Currently, supervised Quaternion Representation Learning (QRL) has achieved remarkable success in efficiently learning informative representations of coupled features from multiple views derived endogenously from the original data. To inherit the advantages of QRL for unsupervised heterogeneous feature representation learning, we propose a deep QRL model that works in an encoder-decoder manner. To ensure that the implicit couplings of heterogeneous feature data can be well characterized by representation learning, a hierarchical coupling encoding strategy is designed to convert the data set into an attributed graph to be the input of QRL. We also integrate the clustering objective into the model training to facilitate a joint optimization of the representation and clustering. Extensive experimental evaluations illustrate the superiority of the proposed Quaternion Graph Representation Learning (QGRL) method in terms of clustering accuracy and robustness to various data sets composed of arbitrary combinations of numerical and categorical features. The source code is opened at https://github.com/Juny-Chen/QGRL.git.
Explicit and Implicit Modeling via Dual-Path Transformer for Behavior Set-informed Sequential Recommendation
Authors: Ming Chen, Weike Pan, Zhong Ming 0001
Venue: KDD
Year: 2024

Sequential recommendation (SR) and multi-behavior sequential recommendation (MBSR) both come from real-world scenarios. Compared with SR, MBSR takes into account the dependencies of different behaviors. We find that most existing works on MBSR are studied in the context of e-commerce scenarios. In terms of the data format of the behavior types, we observe that the conventional label-formatted data carries limited information and is inadequate for scenarios like social media. With this observation, we introducebehavior set and extend MBSR to behavior set-informed sequential recommendation (BSSR). In BSSR, behavior dependencies become more complex and personalized, and user interest arousal may lack explicit contextual associations. To delve into the dynamics inhered within a behavior set and adaptively tailor recommendation lists upon its variability, we propose a novel solution called Explicit and Implicit modeling via Dual-Path Transformer (EIDP) for BSSR. Our EIDP adopts a dual-path architecture, distinguishing between explicit modeling path (EMP) and implicit modeling path (IMP) based on whether to directly incorporate the behavior representations. EMP features the personalized behavior set-wise transition pattern extractor (PBS-TPE) as its core component. It couples behavioral representations with both the items and positions to explore intra-behavior dynamics within a behavior set at a fine granularity. IMP utilizes light multi-head self-attention blocks (L-MSAB) as encoders under specific behavior types. The obtained multi-view representations are then aggregated by cross-behavior attention fusion (CBAF), using the behavior set of the next time step as a guidance to extract collaborative semantics at the behavioral level. Extensive experiments on two real-world datasets demonstrate the effectiveness of our EIDP. We release the implementation code at: https://github.com/OshiNoCSMA/EIDP.
Health Day: Building Health AI Ecosystem: From Data Harmonization to Knowledge Discovery
Authors: Jake Y. Chen, Peipei Ping
Venue: KDD
Year: 2024

The ACM KDD 2024 Health Day theme, "Building Health AI Ecosystem: From Data Harmonization to Knowledge Discovery," highlights the transformative potential of AI-driven ecosystems in healthcare, translational biomedical research, and basic biological research. This extended abstract discusses recent advancements, challenges, and future directions, focusing on integrating AI-ready data sets, interdisciplinary collaborations, and ethical AI practices. It aims to catalyze discussions on the potential of AI ecosystems in revolutionizing healthcare and related fields.
Maximum-Entropy Regularized Decision Transformer with Reward Relabelling for Dynamic Recommendation
Authors: Xiaocong Chen, Siyu Wang 0001, Lina Yao 0001
Venue: KDD
Year: 2024

Reinforcement learning-based recommender systems have recently gained popularity. However, due to the typical limitations of simulation environments (e.g., data inefficiency), most of the work cannot be broadly applied in all domains. To counter these challenges, recent advancements have leveraged offline reinforcement learning methods, notable for their data-driven approach utilizing offline datasets. A prominent example of this is the Decision Transformer. Despite its popularity, the Decision Transformer approach has inherent drawbacks, particularly evident in recommendation methods based on it. This paper identifies two key shortcomings in existing Decision Transformer-based methods: a lack of stitching capability and limited effectiveness in online adoption. In response, we introduce a novel methodology named Max-Entropy enhanced Decision Transformer with Reward Relabeling for Offline RLRS (EDT4Rec). Our approach begins with a max entropy perspective, leading to the development of a max-entropy enhanced exploration strategy. This strategy is designed to facilitate more effective exploration in online environments. Additionally, to augment the model's capability to stitch sub-optimal trajectories, we incorporate a unique reward relabeling technique. To validate the effectiveness and superiority of EDT4Rec, we have conducted comprehensive experiments across six real-world offline datasets and in an online simulator.
Cluster-Wide Task Slowdown Detection in Cloud System
Authors: Feiyi Chen, Yingying Zhang, Lunting Fan, Yuxuan Liang 0002, Guansong Pang, Qingsong Wen, Shuiguang Deng
Venue: KDD
Year: 2024

Slow task detection is a critical problem in cloud operation and maintenance since it is highly related to user experience and can bring substantial liquidated damages. Most anomaly detection methods detect it from a single-task aspect. However, considering millions of concurrent tasks in large-scale cloud computing clusters, it becomes impractical and inefficient. Moreover, single-task slowdowns are very common and do not necessarily indicate a malfunction of a cluster due to its violent fluctuation nature in a virtual environment. Thus, we shift our attention to cluster-wide task slowdowns by utilizing the duration time distribution of tasks across a cluster, so that the computation complexity is not relevant to the number of tasks. The task duration time distribution often exhibits compound periodicity and local exceptional fluctuations over time. Though transformer-based methods are one of the most powerful methods to capture these time series normal variation patterns, we empirically find and theoretically explain the flaw of the standard attention mechanism in reconstructing subperiods with low amplitude when dealing with compound periodicity. To tackle these challenges, we propose SORN (i.e., <u>S</u>kimming <u>O</u>ff subperiods in descending amplitude order and <u>R</u>econstructing <u>N</u>on-slowing fluctuation), which consists of a Skimming Attention mechanism to reconstruct the compound periodicity and a Neural Optimal Transport module to distinguish cluster-wide slowdowns from other exceptional fluctuations. Furthermore, since anomalies in the training set are inevitable in a practical scenario, we propose a picky loss function, which adaptively assigns higher weights to reliable time slots in the training set. Extensive experiments demonstrate that SORN outperforms state-of-the-art methods on multiple real-world industrial datasets.
Enhancing Contrastive Learning on Graphs with Node Similarity
Authors: Hongliang Chi, Yao Ma 0001
Venue: KDD
Year: 2024

Graph Neural Networks (GNN) have proven successful for graph-related tasks. However, many GNNs methods require labeled data, which is challenging to obtain. To tackle this, graph contrastive learning (GCL) have gained attention. GCL learns by contrasting similar nodes (positives) and dissimilar nodes (negatives). Current GCL methods, using data augmentation for positive samples and random selection for negative samples, can be sub-optimal due to limited positive samples and the possibility of false-negative samples. In this study, we propose an enhanced objective addressing these issues. We first introduce an ideal objective with all positive and no false-negative samples, then transform it probabilistically based on sampling distributions. We next model these distributions with node similarity and derive an enhanced objective. Comprehensive experiments have shown the effectiveness of the proposed enhanced objective for a broad set of GCL models.
NudgeRank: Digital Algorithmic Nudging for Personalized Health
Authors: Jodi Chiam, Aloysius Lim, Ankur Teredesai
Venue: KDD
Year: 2024

In this paper we describe NudgeRankTM, an innovative digital algorithmic nudging system designed to foster positive health behaviors on a population-wide scale. Utilizing a novel combination of Graph Neural Networks augmented with an extensible Knowledge Graph, this Recommender System is operational in production, delivering personalized and context-aware nudges to over 1.1 million care recipients daily. This enterprise deployment marks one of the largest AI-driven health behavior change initiatives, accommodating diverse health conditions and wearable devices. Rigorous evaluation reveals statistically significant improvements in health outcomes, including a 6.17% increase in daily steps and 7.61% more exercise minutes. Moreover, user engagement and program enrollment surged, with a 13.1% open rate compared to baseline systems' 4%. Demonstrating scalability and reliability, NudgeRankTM operates efficiently on commodity compute resources while maintaining automation and observability standards essential for production systems.
Iterative Weak Learnability and Multiclass AdaBoost
Authors: In-Koo Cho, Jonathan A. Libgober, Cheng Ding
Venue: KDD
Year: 2024

We propose an efficient boosting algorithm for multiclass classification, called AdaBoost.Iter, that extends SAMME and AdaBoost. The algorithm iteratively applies the weak learnability condition of SAMME to eliminate classes to find the correct classificiation. The iterative weak learnability is a sufficient and necessary condition for boostability, but it is also easier to validate than the EOR criterion of AdaBoost.MM \citeMukherjeeSchapire2013. We show that the training error of AdaBoost.Iter vanishes at the exponential rate, while the generalization error converges to zero at the same rate as AdaBoost. AdaBoost.Iter numerically outperforms SAMME and achieves performance comparable to AdaBoost.MM on benchmark datasets.
Workshop on Human-Interpretable AI
Authors: Gabriele Ciravegna, Mateo Espinosa Zarlenga, Pietro Barbiero, Francesco Giannini, Zohreh Shams, Damien Garreau, Mateja Jamnik, Tania Cerquitelli
Venue: KDD
Year: 2024

This workshop aims to spearhead research on Human-Interpretable Artificial Intelligence (HI-AI) by providing: (i) a general overview of the key aspects of HI-AI, in order to equip all researchers with the necessary background and set of definitions; (ii) novel and interesting ideas coming from both invited talks and top paper contributions; (iii) the chance to engage in dialogue with prominent scientists during poster presentations and coffee breaks. The workshop welcomes contributions covering novel interpretable-by-design or post-hoc approaches, as well as theoretical analysis of existing works. Additionally, we accept visionary contributions speculating on the future potential of this field. Finally, we welcome contributions from related fields such as Ethical AI, Knowledge-driven Machine learning, Human-machine Interaction, but also applications in Medicine and Industry, and analyses from Regulatory experts.