OpenRaiser/Intern-Atlas huggingface dataset:
Bi-Objective Contract Allocation for Guaranteed Delivery Advertising
Authors: Yan Li, Yundu Huang, Wuyang Mao, Furong Ye, Xiang He 0005, Zhonglin Zu, Shaowei Cai 0001
Venue: KDD
Year: 2024

Contemporary systems of Guaranteed Delivery (GD) advertising work with two different stages, namely, the offline selling stage and the online serving stage. The former deals with contract allocation, and the latter fulfills the impression allocation of signed contracts. Existing work usually handles these two stages separately. For example, contracts are formulated offline without concerning practical situations in the online serving stage. Therefore, we address in this paper a bi-objective contract allocation for GD advertising, which maximizes the impressions, i.e., Ad resource assignments, allocated for the new incoming advertising orders, and at the same time, controls the balance in the inventories. Since the proposed problem is high dimensional and heavily constrained, we design an efficient local search that focuses on the two objectives alternatively. The experimental results indicate that our algorithm outperforms multi-objective evolutionary algorithms and Gurobi, the former of which is commonly applied for multi-objective optimization and the latter of which is a well-known competitive commercial tool.
Text Matching Indexers in Taobao Search
Authors: Sen Li 0001, Fuyu Lv, Ruqing Zhang 0001, Dan Ou, Zhixuan Zhang, Maarten de Rijke
Venue: KDD
Year: 2024

Product search is an important service on Taobao, the largest e-commerce platform in China. Through this service, users can easily find products relevant to their specific needs. Coping with billion-size query loads, Taobao product search has traditionally relied on classical term-based retrieval models due to their powerful and interpretable indexes. In essence, efficient retrieval hinges on the proper storage of the inverted index. Recent successes involve reducing the size (pruning) of the inverted index but the construction and deployment of lossless static index pruning in practical product search still pose non-trivial challenges.
Improving Robustness of Hyperbolic Neural Networks by Lipschitz Analysis
Authors: Yuekang Li, Yidan Mao, Yifei Yang 0001, Dongmian Zou
Venue: KDD
Year: 2024

Hyperbolic neural networks (HNNs) are emerging as a promising tool for representing data embedded in non-Euclidean geometries, yet their adoption has been hindered by challenges related to stability and robustness. In this work, we conduct a rigorous Lipschitz analysis for HNNs and propose using Lipschitz regularization as a novel strategy to enhance their robustness. Our comprehensive investigation spans both the Poincaré ball model and the hyperboloid model, establishing Lipschitz bounds for HNN layers. Importantly, our analysis provides detailed insights into the behavior of the Lipschitz bounds as they relate to feature norms, particularly distinguishing between scenarios where features have unit norms and those with large norms. Further, we study regularization using the derived Lipschitz bounds. Our empirical validations demonstrate consistent improvements in HNN robustness against noisy perturbations.
InLN: Knowledge-aware Incremental Leveling Network for Dynamic Advertising
Authors: Xujia Li, Jingshu Peng, Lei Chen 0002
Venue: KDD
Year: 2024

In today's fast-paced world, advertisers are increasingly demanding real-time and accurate personalized ad delivery based on dynamic preference modeling, which emphasizes the temporality existing in both user preference and product characteristics. Meanwhile, with the development of graph neural networks (GNNs), E-commerce knowledge graphs (KG) with rich semantic relatedness are invoked to improve accuracy and provide appropriate explanations to encourage advertisers' willingness to invest in ad expenses. However, it is still challenging for existing methods to comprehensively consider both time-series interactions and graph-structured knowledge triples in a unified model, i.e., the case in knowledge-aware dynamic advertising. The interaction graph between users and products changes rapidly over time, while the knowledge in KG remains relatively stable. This results in an uneven distribution of temporal and semantic information, causing existing GNNs to fail in this scenario. In this work, we quantitatively define the above phenomenon as temporal unevenness and introduce the Incremental Leveling Network (InLN) with three novel techniques: the periodic-focusing window for node-level dynamic modeling, the biased temporal walk for subgraph-level dynamic modeling and the incremental leveling mechanism for KG updating. Verified by comprehensive and intensive experiments, InLN outperforms nine baseline models in three tasks by substantial margins, reaching up to a 9.9% improvement and averaging a 5.7% increase.
Graph Intelligence with Large Language Models and Prompt Learning
Authors: Jia Li 0009, Xiangguo Sun, Yuhan Li 0001, Zhixun Li, Hong Cheng 0001, Jeffrey Xu Yu
Venue: KDD
Year: 2024

Graph plays a significant role in representing and analyzing complex relationships in real-world applications such as citation networks, social networks, and biological data. Graph intelligence is rapidly becoming a crucial aspect of understanding and exploiting the intricate interconnections within graph data. Recently, large language models (LLMs) and prompt learning techniques have pushed graph intelligence forward, outperforming traditional Graph Neural Network (GNN) pre-training methods and setting new benchmarks for performance. In this tutorial, we begin by offering a comprehensive review and analysis of existing methods that integrate LLMs with graphs. We introduce existing works based on a novel taxonomy that classifies them into three distinct categories according to the roles of LLMs in graph tasks: as enhancers, predictors, or alignment components. Secondly, we introduce a new learning method that utilizes prompting on graphs, offering substantial potential to enhance graph transfer capabilities across diverse tasks and domains. We discuss existing works on graph prompting within a unified framework and introduce our developed tool for executing a variety of graph prompting tasks. Additionally, we discuss the applications of combining Graphs, LLMs, and prompt learning across various tasks, such as urban computing, recommendation systems, and anomaly detection. This lecture-style tutorial is an extension of our original work published in IJCAI 2024[44] and arXiv[77] with the invitation of KDD24.
Scalable Multitask Learning Using Gradient-based Estimation of Task Affinity
Authors: Dongyue Li, Aneesh Sharma, Hongyang R. Zhang
Venue: KDD
Year: 2024

Multitask learning is a widely used paradigm for training models on diverse tasks, with applications ranging from graph neural networks to language model fine-tuning. Since tasks may interfere with each other, a key notion for modeling their relationships is task affinity. This includes pairwise task affinity, computed among pairs of tasks, and higher-order affinity, computed among subsets of tasks. Naively computing either of them requires repeatedly training on data pooled from various task combinations, which is computationally intensive. We present a new algorithm Grad-TAG that can estimate task affinities without this repeated training.
Physics-informed Neural ODE for Post-disaster Mobility Recovery
Authors: Jiahao Li, Huandong Wang, Xinlei Chen
Venue: KDD
Year: 2024

Urban mobility undergoes a profound decline in the aftermath of a disaster, subsequently exhibiting a complex recovery trajectory. Effectively capturing and predicting this dynamic recovery process holds paramount importance for devising more efficient post-disaster recovery strategies, such as resource allocation to areas with protracted recovery periods. Existing models for post-disaster mobility recovery predominantly employ basic mathematical methods, which are strongly based on simplifying assumptions, and their limited parameters restrict their capacity to fully capture the mobility recovery patterns. In response to this gap, we introduce the Coupled Dynamic Graph ODE Network (CDGON) to model the intricate dynamics of post-disaster mobility recovery. Our model seamlessly integrates existing physical knowledge pertaining to post-disaster mobility recovery and incorporates the nuanced interactions between intra-regional and inter-regional population flows. Extensive experimental results demonstrate the efficiency of our model in capturing the dynamic recovery patterns of urban population mobility in post-disaster scenarios, surpassing the capabilities of current dynamic graph prediction models.
The 13th International Workshop on Urban Computing
Authors: Yuxuan Liang 0002, Chuishi Meng, Yanhua Li, Yu Zheng 0004, Jieping Ye, Qiang Yang 0001, Philip S. Yu, Ouri Wolfson
Venue: KDD
Year: 2024

Urbanization's rapid progress has led to many big cities, which have modernized many people's lives but also engendered big challenges, such as air pollution, increased energy consumption, and traffic congestion. Tackling these challenges was nearly impossible years ago given the complex and dynamic settings of cities. Nowadays, sensing technologies and large-scale computing infrastructures have produced a variety of big data in urban spaces, e.g., human mobility, air quality, traffic patterns, and geographical data. Motivated by the opportunities of building more intelligent cities, we came up with a vision of urban computing, which aims to unlock the power of knowledge from big and heterogeneous data collected in urban spaces and apply this powerful information to solve major issues our cities face today.
Customizing Graph Neural Network for CAD Assembly Recommendation
Authors: Fengqi Liang, Huan Zhao 0002, Yuhan Quan, Wei Fang, Chuan Shi 0001
Venue: KDD
Year: 2024

CAD assembly modeling, which refers to using CAD software to design new products from a catalog of existing machine components, is important in the industrial field. The graph neural network (GNN) based recommender system for CAD assembly modeling can help designers make decisions and speed up the design process by recommending the next required component based on the existing components in CAD software. These components can be represented as a graph naturally. However, present recommender systems for CAD assembly modeling adopt fixed GNN architectures, which may be sub-optimal for different manufacturers with different data distribution. Therefore, to customize a well-suited recommender system for different manufacturers, we propose a novel neural architecture search (NAS) framework, dubbed CusGNN, which can design data-specific GNN automatically. Specifically, we design a search space from three dimensions (i.e., aggregation, fusion, and readout functions), which contains a wide variety of GNN architectures. Then, we develop an effective differentiable search algorithm to search high-performing GNN from the search space. Experimental results show that the customized GNNs achieve 1.5-5.1% higher top-10 accuracy compared to previous manual designed methods, demonstrating the superiority of the proposed approach. Code and data are available at https://github.com/BUPT-GAMMA/CusGNN.
Empower an End-to-end Scalable and Interpretable Data Science Ecosystem using Statistics, AI and Domain Science
Authors: Xihong Lin
Venue: KDD
Year: 2024

The data science ecosystem encompasses data fairness, statistical, ML and AI methods and tools, interpretable data analysis and results, and trustworthy decision-making. Rapid advancements in AI have revolutionized data utilization and enabled machines to learn from data more effectively. Statistics, as the science of learning from data while accounting for uncertainty, plays a pivotal role in addressing complex real-world problems and facilitating trustworthy decision-making. In this talk, I will discuss the challenges and opportunities involved in building an end-to-end scalable and interpretable data science ecosystem using the analysis of whole genome sequencing studies and biobanks that integrates statistics, ML/AI, and genomic and health science as an example. Biobanks collect whole genome data, electronic health records and epidemiological data. I will illustrate key points using the analysis of multi-ancestry whole genome sequencing studies and biobanks by discussing a few scalable and interpretable statistical and ML/AI methods, tools and data science resources.
Hyper-Local Deformable Transformers for Text Spotting on Historical Maps
Authors: Yijun Lin 0001, Yao-Yi Chiang
Venue: KDD
Year: 2024

Text on historical maps contains valuable information providing georeferenced historical, political, and cultural contexts. However, text extraction from historical maps has been challenging due to the lack of (1) effective methods and (2) training data. Previous approaches use ad-hoc steps tailored to only specific map styles. Recent machine learning-based text spotters (e.g., for scene images) have the potential to solve these challenges because of their flexibility in supporting various types of text instances. However, these methods remain challenges in extracting precise image features for predicting every sub-component (boundary points and characters) in a text instance. This is critical because map text can be lengthy and highly rotated with complex backgrounds, posing difficulties in detecting relevant image features from a rough text region. This paper proposes PALETTE, an end-to-end text spotter for scanned historical maps of a wide variety. PALETTE introduces a novel hyper-local sampling module to explicitly learn localized image features around the target boundary points and characters of a text instance for detection and recognition. PALETTE also enables hyper-local positional embeddings to learn spatial interactions between boundary points and characters within and across text instances. In addition, this paper presents a novel approach to automatically generate synthetic map images, SYNTHMAP+, for training text spotters for historical maps. The experiment shows that PALETTE with SYNTHMAP+ outperforms SOTA text spotters on two new benchmark datasets of historical maps, particularly for long and angled text. We have deployed PALETTE with SYNTHMAP+ to process over 60,000 maps in the David Rumsey Historical Map collection and generated over 100 million text labels to support map searching.
MulSTE: A Multi-view Spatio-temporal Learning Framework with Heterogeneous Event Fusion for Demand-supply Prediction
Authors: Li Lin 0011, Zhiqiang Lu, Shuai Wang 0008, Yunhuai Liu, Zhiqing Hong, Haotian Wang 0008, Shuai Wang 0008
Venue: KDD
Year: 2024

Recently, integrated warehouse and distribution logistics systems are widely used in E-commerce industries to adjust to constantly changing customer demands. It makes the prediction of purchase demand and delivery supply capacity a crucial problem to streamline operations and improve efficiency. The interaction between such demand and supply not only relies on their economic relationships but also on consumer psychology caused by daily events, such as epidemics, promotions, and festivals. Although existing studies have made great efforts in the joint prediction of demand and supply considering modeling the demand-supply interactions, they seldom refer to the impacts of diverse events. In this work, we propose MulSTE, a Multi-view Spatio-Temporal learning framework with heterogeneous Event fusion. Firstly, an Event Fusion Representation (EFR) module is designed to fuse the textual, numerical, and categorical heterogeneous information for emergent and periodic events. Secondly, a Multi-graph Adaptive Convolution Recurrent Network (MGACRN) is developed as the spatio-temporal encoder (ST-Encoder) to capture the evolutional features of demand, supply, and events. Thirdly, the Event Gated Demand-Supply Interaction Attention (EGIA) module is designed to model the demand-supply interactions during events. The evaluations are conducted on two real-world datasets collected from JD Logistics and public websites. The experimental results show that our method outperforms state-of-the-art baselines in various metrics.
Robust Auto-Bidding Strategies for Online Advertising
Authors: Qilong Lin, Zhenzhe Zheng 0001, Fan Wu 0006
Venue: KDD
Year: 2024

In online advertising, existing auto-bidding strategies for bid shading mainly adopt the approach of first predicting the winning price distribution and then calculating the optimal bid. However, the winning price information available to the Demand Side Platforms (DSPs) is extremely limited, and the associated uncertainties make it challenging for DSPs to accurately estimate winning price distribution. To address this challenge, we conducted a comprehensive analysis of the process by which DSPs obtain winning price information, and abstracted two types of uncertainties from it: known uncertainty and unknown uncertainty. Based on these uncertainties, we proposed two levels of robust bidding strategies: Robust Bidding for Censorship (RBC) and Robust Bidding for Distribution Shift (RBDS), which offer guarantees for the surplus in the worst-case scenarios under uncertain conditions. Experimental results on public datasets demonstrate that our robust bidding strategies consistently enable DSPs to achieve superior surpluses, both on test sets and under worst-case conditions.
KDD workshop on Evaluation and Trustworthiness of Generative AI Models
Authors: Yuan Ling, Shujing Dong, Yarong Feng, Zongyi Joe Liu, George Karypis, Chandan K. Reddy
Venue: KDD
Year: 2024

The KDD workshop on Evaluation and Trustworthiness of Generative AI Models aims to address the critical need for reliable generative AI technologies by exploring comprehensive evaluation strategies. This workshop will delve into various aspects of assessing generative AI models, including Large Language Models (LLMs) and diffusion models, focusing on trustworthiness, safety, bias, fairness, and ethical considerations. With an emphasis on interdisciplinary collaboration, the workshop will feature invited talks, peer-reviewed paper presentations, and panel discussions to advance the state of the art in generative AI evaluation.
CONFIDE: Contextual Finite Difference Modelling of PDEs
Authors: Ori Linial, Orly Avner, Dotan Di Castro
Venue: KDD
Year: 2024

We introduce a method for inferring an explicit PDE from a data sample generated by previously unseen dynamics, based on a learned context. The training phase integrates knowledge of the form of the equation with a differential scheme, while the inference phase yields a PDE that fits the data sample and enables both signal prediction and data explanation. We include results of extensive experimentation, comparing our method to SOTA approaches, together with ablation studies that examine different flavors of our solution.
TDNetGen: Empowering Complex Network Resilience Prediction with Generative Augmentation of Topology and Dynamics
Authors: Chang Liu 0092, Jingtao Ding, Yiwen Song, Yong Li 0008
Venue: KDD
Year: 2024

Predicting the resilience of complex networks, which represents the ability to retain fundamental functionality amidst external perturbations or internal failures, plays a critical role in understanding and improving real-world complex systems. Traditional theoretical approaches grounded in nonlinear dynamical systems rely on prior knowledge of network dynamics. On the other hand, data-driven approaches frequently encounter the challenge of insufficient labeled data, a predicament commonly observed in real-world scenarios. In this paper, we introduce a novel resilience prediction framework for complex networks, designed to tackle this issue through generative data augmentation of network topology and dynamics. The core idea is the strategic utilization of the inherent joint distribution present in unlabeled network data, facilitating the learning process of the resilience predictor by illuminating the relationship between network topology and dynamics. Experiment results on three network datasets demonstrate that our proposed framework TDNetGen can achieve high prediction accuracy up to 85%-95%. Furthermore, the framework still demonstrates a pronounced augmentation capability in extreme low-data regimes, thereby underscoring its utility and robustness in enhancing the prediction of network resilience. We have open-sourced our code in the following link, https://github.com/tsinghua-fib-lab/TDNetGen.
ACER: Accelerating Complex Event Recognition via Two-Phase Filtering under Range Bitmap-Based Indexes
Authors: Shizhe Liu, Haipeng Dai 0001, Shaoxu Song, Meng Li 0010, Jingsong Dai, Rong Gu 0001, Guihai Chen
Venue: KDD
Year: 2024

Complex event recognition (CER) refers to identifying specific patterns composed of several primitive events in event stores. Since full-scanning event stores to identify primitive events holding query constraint conditions will incur costly I/O overhead, a mainstream and practical approach is using index techniques to obtain these events. However, prior index-based approaches suffer from significant I/O and sorting overhead when dealing with high predicate selectivity or long query window (common in real-world applications), which leads to high query latency. To address this issue, we propose ACER, a Range Bitmap-based index, to accelerate CER. Firstly, ACER achieves a low index space overhead by grouping the events with the same type into a cluster and compressing the cluster data, alleviating the I/O overhead of reading indexes. Secondly, ACER builds Range Bitmaps in batch (block) for queried attributes and ensures that the events of each cluster in the index block are chronologically ordered. Then, ACER can always obtain ordered query results for a specific event type through merge operations, avoiding sorting overhead. Most importantly, ACER avoids unnecessary disk access in indexes and events via two-phase filtering based on the window condition, thus alleviating the I/O overhead further. Our experiments on six real-world and synthetic datasets demonstrate that ACER reduces the query latency by up to one order of magnitude compared with SOTA techniques.
Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective
Authors: Yunfei Liu, Jintang Li, Yuehe Chen, Ruofan Wu, Ericbk Wang, Jing Zhou, Sheng Tian, Shuheng Shen, Xing Fu, Changhua Meng, Weiqiang Wang 0002, Liang Chen 0001
Venue: KDD
Year: 2024

Graph clustering, a fundamental and challenging task in graph mining, aims to classify nodes in a graph into several disjoint clusters. In recent years, graph contrastive learning (GCL) has emerged as a dominant line of research in graph clustering and advances the new state-of-the-art. However, GCL-based methods heavily rely on graph augmentations and contrastive schemes, which may potentially introduce challenges such as semantic drift and scalability issues. Another promising line of research involves the adoption of modularity maximization, a popular and effective measure for community detection, as the guiding principle for clustering tasks. Despite the recent progress, the underlying mechanism of modularity maximization is still not well understood. In this work, we dig into the hidden success of modularity maximization for graph clustering. Our analysis reveals the strong connections between modularity maximization and graph contrastive learning, where positive and negative examples are naturally defined by modularity. In light of our results, we propose a community-aware graph clustering framework, coined øurs, which leverages modularity maximization as a contrastive pretext task to effectively uncover the underlying information of communities in graphs, while avoiding the problem of semantic drift. Extensive experiments on multiple graph datasets verify the effectiveness of øurs in terms of scalability and clustering performance compared to state-of-the-art graph clustering methods. Notably, øurs easily scales a sufficiently large graph with 100M nodes while outperforming strong baselines.