OpenRaiser/Intern-Atlas huggingface dataset:
Source Localization for Cross Network Information Diffusion
Authors: Chen Ling 0003, Tanmoy Chowdhury, Jie Ji, Sirui Li, Andreas Züfle, Liang Zhao 0002
Venue: KDD
Year: 2024

Source localization aims to locate information diffusion sources only given the diffusion observation, which has attracted extensive attention in the past few years. Existing methods are mostly tailored for single networks and may not be generalized to handle more complex networks like cross-networks. Cross-network is defined as two interconnected networks, where one network's functionality depends on the other. Source localization on cross-networks entails locating diffusion sources on the source network by only giving the diffused observation in the target network. The task is challenging due to challenges including: 1) diffusion sources distribution modeling; 2) jointly considering both static and dynamic node features; and 3) heterogeneous diffusion patterns learning. In this work, we propose a novel method, namely CNSL, to handle the three primary challenges. Specifically, we propose to learn the distribution of diffusion sources through Bayesian inference and leverage disentangled encoders to learn static and dynamic node features separately. The learning objective is coupled with the cross-network information propagation estimation model to make the inference of diffusion sources considering the overall diffusion process. Additionally, we also provide two novel cross-network datasets collected by ourselves. Extensive experiments are conducted on both datasets to demonstrate the effectiveness of CNSL in handling the source localization on cross-networks.
Reasoning and Planning with Large Language Models in Code Development
Authors: Hao Ding 0003, Ziwei Fan 0001, Ingo Gühring, Gaurav Gupta, Wooseok Ha, Jun Huan, Linbo Liu, Behrooz Omidvar-Tehrani, Shiqi Wang 0002, Hao Zhou 0036
Venue: KDD
Year: 2024

Large Language Models (LLMs) are revolutionizing the field of code development by leveraging their deep understanding of code patterns, syntax, and semantics to assist developers in various tasks, from code generation and testing to code understanding and documentation. In this survey, accompanying our proposed lecture-style tutorial for KDD 2024, we explore the multifaceted impact of LLMs on the code development, delving into techniques for generating a high-quality code, creating comprehensive test cases, automatically generating documentation, and engaging in an interactive code reasoning. Throughout the survey, we highlight some crucial components surrounding LLMs, including pre-training, fine-tuning, prompt engineering, iterative refinement, agent planning, and hallucination mitigation. We put forward that such ingredients are essential to harness the full potential of these powerful AI models in revolutionizing software engineering and paving the way for a more efficient, effective, and innovative future in code development.
DyGKT: Dynamic Graph Learning for Knowledge Tracing
Authors: Ke Cheng 0003, Linzhi Peng, Pengyang Wang, Junchen Ye, Leilei Sun, Bowen Du 0001
Venue: KDD
Year: 2024

Knowledge Tracing aims to assess student learning states by predicting their performance in answering questions. Different from the existing research which utilizes fixed-length learning sequence to obtain the student states and regards KT as a static problem, this work is motivated by three dynamical characteristics: 1) The scales of students answering records are constantly growing; 2) The semantics of time intervals between the records vary; 3) The relationships between students, questions and concepts are evolving. The three dynamical characteristics above contain the great potential to revolutionize the existing knowledge tracing methods. Along this line, we propose a Dynamic Graph-based Knowledge Tracing model, namely DyGKT. In particular, a continuous-time dynamic question-answering graph for knowledge tracing is constructed to deal with the infinitely growing answering behaviors, and it is worth mentioning that it is the first time dynamic graph learning technology is used in this field. Then, a dual time encoder is proposed to capture long-term and short-term semantics among the different time intervals. Finally, a multiset indicator is utilized to model the evolving relationships between students, questions, and concepts via the graph structural feature. Numerous experiments are conducted on five real-world datasets, and the results demonstrate the superiority of our model. All the used resources are publicly available at https://github.com/PengLinzhi/DyGKT.
Weather Knows What Will Occur: Urban Public Nuisance Events Prediction and Control with Meteorological Assistance
Authors: Yi Xie 0003, Tianyu Qiu, Yun Xiong, Xiuqi Huang, Xiaofeng Gao 0001, Chao Chen 0004, Qiang Wang 0066, Haihong Li
Venue: KDD
Year: 2024

Urban public nuisance events, like garbage exposure, illegal parking, facilities damage, and etc., impair the quality of life for city residents. Predicting and controlling these nuisances is crucial but complicated due to their ties to subjective and psychological factors. In this study, we reveal a significant correlation between such nuisances and meteorological indicators, influenced by the impact of climate on people's psychological states. We employ meteorology predictions that are integrated in Hawkes processes to enhance the accuracy of predicting the category and timing of these nuisances. To this end, we propose Spatial-Temporal Two-Tower Transformer (ST-T3), which simultaneously considers spatial data and further improves the prediction accuracy. Evaluated by about three-year data from both downtown and suburban Shanghai, our method outperforms both traditional and advanced prediction systems. We share a portion of the de-identified dataset for open research.
Practical Single Domain Generalization via Training-time and Test-time Learning
Authors: Shuai Yang 0003, Zhen Zhang 0070, Lichuan Gu
Venue: KDD
Year: 2024

Single domain generalization aims to learn a model that generalizes well to unseen target domains by using a related source domain. However, most existing methods only focus on improving the generalization performance of the model during training, making it difficult to achieve satisfactory performance when deployed in the target domain with large domain shifts. In this paper, we propose a Practical Single Domain Generalization (PSDG) method, which first leverages the knowledge in a source domain to establish a model with good generalization ability in the training phase, and subsequently updates the model to adapt to target domain data using knowledge in the unlabeled target domain during the testing phase. Specifically, during training, PSDG leverages a newly proposed style (e.g., background features) generator named StyIN to generate novel domain data. Moreover, PSDG introduces style-diversity regularization to constantly synthesize distinct styles to expand the coverage of training data, and introduces object-consistency regularization to capture consistency between the currently generated data and the original data, making the model filter style knowledge during training. During testing, PSDG uses a sample-aware and sharpness-aware minimization method to seek for a flat entropy minimum surface for further model optimization by using the knowledge in the unlabeled target domain. Using three real-world datasets the experiments have demonstrated the effectiveness of PSDG, in comparison with several state-of-the-art methods.
Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Networks
Authors: Weijia Zhang 0003, Le Zhang 0010, Jindong Han, Hao Liu 0026, Yanjie Fu, Jingbo Zhou 0003, Yu Mei 0002, Hui Xiong 0001
Venue: KDD
Year: 2024

Accurate traffic forecasting is crucial for the development of Intelligent Transportation Systems (ITS), playing a pivotal role in modern urban traffic management. Traditional forecasting methods, however, struggle with the irregular traffic time series resulting from adaptive traffic signal controls, presenting challenges in asynchronous spatial dependency, irregular temporal dependency, and predicting variable-length sequences. To this end, we propose an Asynchronous Spatio-tEmporal graph convolutional nEtwoRk (ASeer) tailored for irregular traffic time series forecasting. Specifically, we first propose an Asynchronous Graph Diffusion Network to capture the spatial dependency between asynchronously measured traffic states regulated by adaptive traffic signals. After that, to capture the temporal dependency within irregular traffic state sequences, a personalized time encoding is devised to embed the continuous time signals. Then, we propose a Transformable Time-aware Convolution Network, which adapts meta-filters for time-aware convolution on the sequences with inconsistent temporal flow. Additionally, a Semi-Autoregressive Prediction Network, comprising a state evolution unit and a semiautoregressive predictor, is designed to predict variable-length traffic sequences effectively and efficiently. Extensive experiments on a newly established benchmark demonstrate the superiority of ASeer compared with twelve competitive baselines across six metrics.
Scalable Differentiable Causal Discovery in the Presence of Latent Confounders with Skeleton Posterior
Authors: Pingchuan Ma 0004, Rui Ding 0001, Qiang Fu 0015, Jiaru Zhang, Shuai Wang 0011, Shi Han, Dongmei Zhang 0001
Venue: KDD
Year: 2024

Differentiable causal discovery has made significant advancements in the learning of directed acyclic graphs. However, its application to real-world datasets remains restricted due to the ubiquity of latent confounders and the requirement to learn maximal ancestral graphs (MAGs). To date, existing differentiable MAG learning algorithms have been limited to small datasets and failed to scale to larger ones (e.g., with more than 50 variables).
Truthful Bandit Mechanisms for Repeated Two-stage Ad Auctions
Authors: Haoming Li 0017, Yumou Liu, Zhenzhe Zheng 0001, Zhilin Zhang 0003, Jian Xu 0015, Fan Wu 0006
Venue: KDD
Year: 2024

Online advertising platforms leverage a two-stage auction architecture to deliver personalized ads to users with low latency. The first stage efficiently selects a small subset of promising candidates out of the complete pool of ads. In the second stage, an auction is conducted within the subset to determine the winning ad for display, using click-through-rate predictions from the second-stage machine learning model. In this work, we investigate the online learning process of the first-stage subset selection policy, while ensuring game-theoretic properties in repeated two-stage ad auctions. Specifically, we model the problem as designing a combinatorial bandit mechanism with a general reward function, as well as additional requirements of truthfulness and individual rationality (IR). We establish an O(T) regret lower bound for truthful bandit mechanisms, which demonstrates the challenge of simultaneously achieving allocation efficiency and truthfulness. To circumvent this impossibility result, we introduce truthful α-approximation oracles and evaluate the bandit mechanism through α-approximation regret. Two mechanisms are proposed, both of which are ex-post truthful and ex-post IR. The first mechanism is an explore-then-commit mechanism with regret O(T2/3 ), and the second mechanism achieves an improved O(log T /ΔΦ2) regret where ΔΦ is a distribution-dependent gap, but requires additional assumptions on the oracles and information about the strategic bidders.
Distributional Network of Networks for Modeling Data Heterogeneity
Authors: Jun Wu 0019, Jingrui He, Hanghang Tong
Venue: KDD
Year: 2024

Heterogeneous data widely exists in various high-impact applications. Domain adaptation and out-of-distribution generalization paradigms have been formulated to handle the data heterogeneity across domains. However, most existing domain adaptation and out-of-distribution generalization algorithms do not explicitly explain how the label information can be adaptively propagated from the source domains to the target domain. Furthermore, little effort has been devoted to theoretically understanding the convergence of existing algorithms based on neural networks.To address these problems, in this paper, we propose a generic distributional network of networks (TENON) framework, where each node of the main network represents an individual domain associated with a domain-specific network. In this case, the edges within the main network indicate the domain similarity, and the edges within each network indicate the sample similarity. The crucial idea of TENON is to characterize the within-domain label smoothness and cross-domain parameter smoothness in a unified framework. The convergence and optimality of TENON are theoretically analyzed. Furthermore, we show that based on the TENON framework, domain adaptation and out-of-distribution generalization can be naturally formulated as transductive and inductive distribution learning problems, respectively. This motivates us to develop two instantiated algorithms (TENON-DA and TENON-OOD) of the proposed TENON framework for domain adaptation and out-of-distribution generalization. The effectiveness and efficiency of TENON-DA and TENON-OOD are verified both theoretically and empirically.
CE-RCFR: Robust Counterfactual Regression for Consensus-Enabled Treatment Effect Estimation
Authors: Fan Wang 0020, Chaochao Chen 0001, Weiming Liu 0005, Tianhao Fan, Xinting Liao, Yanchao Tan, Lianyong Qi, Xiaolin Zheng
Venue: KDD
Year: 2024

Estimating individual treatment effects (ITE) from observational data is challenging due to the absence of counterfactuals and the treatment selection bias. Prevalent ITE estimation methods tackle these challenges by aligning the treated and controlled distributions in the representational space. However, two critical issues have long been overlooked: (1)Mini-batch sampling sensitivity (MSS) issue, where representation distribution alignment at a mini-batch level is vulnerable to poor sampling cases, such as data imbalance and outliers; (2)Inconsistent representation learning (IRL) issue, where representation learning within a unified backbone network suffers from inconsistent gradient update directions due to the distribution skew between different treatment groups. To resolve these issues, we propose CE-RCFR, a <u>R</u>obust <u>C</u>ounter<u>F</u>actual <u>R</u>egression framework for <u>C</u>onsensus-<u>E</u>nabled causal effect estimation, including a relaxed distribution discrepancy regularizer (RDDR) module and a consensus-enabled aggregator (CEA) module. Specifically, for the robust representation alignment perspective, RDDR addresses the MSS issue by minimizing unbalanced optimal transport divergence between different treatment groups with a relaxed marginal constraint. For the accurate representation optimization perspective, CEA addresses the IRL issue by resolving the consistent gradient update directions on shared parameters within the backbone network. Extensive experiments demonstrate that CE-RCFR significantly outperforms the state-of-the-art methods in treatment effect estimations.
CutAddPaste: Time Series Anomaly Detection by Exploiting Abnormal Knowledge
Authors: Rui Wang 0118, Xudong Mou, Renyu Yang, Kai Gao, Pin Liu, Chongwei Liu, Tianyu Wo, Xudong Liu 0001
Venue: KDD
Year: 2024

Detecting time-series anomalies is extremely intricate due to the rarity of anomalies and imbalanced sample categories, which often result in costly and challenging anomaly labeling. Most of the existing approaches largely depend on assumptions of normality, overlooking labeled abnormal samples. While anomaly assumptions based methods can incorporate prior knowledge of anomalies for data augmentation in training classifiers, the adopted random or coarse-grained augmentation approaches solely focus on pointwise anomalies and lack cutting-edge domain knowledge, making them less likely to achieve better performance. This paper introduces CutAddPaste, a novel anomaly assumption-based approach for detecting time-series anomalies. It primarily employs a data augmentation strategy to generate pseudo anomalies, by exploiting prior knowledge of anomalies as much as possible. At the core of CutAddPaste is cutting patches from random positions in temporal subsequence samples, adding linear trend terms, and pasting them into other samples, so that it can well approximate a variety of anomalies, including point and pattern anomalies. Experiments on standard benchmark datasets demonstrate that our method outperforms the state-of-the-art approaches.
Decoding the AI Pen: Techniques and Challenges in Detecting AI-Generated Text
Authors: Sara Abdali, Richard Anarfi, C. J. Barberan, Jia He
Venue: KDD
Year: 2024

Large Language Models (LLMs) have revolutionized the field of Natural Language Generation (NLG) by demonstrating an impressive ability to generate human-like text. However, their widespread usage introduces challenges that necessitate thoughtful examination, ethical scrutiny, and responsible practices. In this study, we delve into these challenges, explore existing strategies for mitigating them, with a particular emphasis on identifying AI-generated text as the ultimate solution. Additionally, we assess the feasibility of detection from a theoretical perspective and propose novel research directions to address the current limitations in this domain.
Dynamic Pricing for Multi-Retailer Delivery Platforms with Additive Deep Learning and Evolutionary Optimization
Authors: Ahmed Abdulaal, Ali Polat, Hari Narayan, Wenrong Zeng, Yimin Yi
Venue: KDD
Year: 2024

Dynamic Pricing for online retail has been discussed extensively in literature. However, past solutions fell short of addressing the unique challenges of independent multi-retailer platforms for grocery delivery. From limited visibility of retailers' inventories to diverse demand-side dynamics across retail brands and locations, the highly decentralized nature of multi-retailer platforms deviates from the classical framework of modeling price elasticity and cross-elasticity of demand. In this paper, we present a novel scheme to scalable and practical price adjustment in the highly dynamic multi-retailer context. First, we present a deep learning framework to distinctly model complex cross-elasticity relationships via additive neural networks augmented with adversarial data. Second, we present evolutionary optimization agents for adjusting itemized prices in a location-decentralized manner, while adhering to custom business constraints and objectives. The optimization utilizes the genetic algorithm structure, where we introduce a potential mechanism, inspired by bandit algorithms, in order to improve convergence speed by managing exploitation and exploration trade-offs. Our solution is deployed at Shipt and is extendable to other types of multi-retailer platforms, such as restaurant delivery. Finally, we empirically demonstrate performance using public and industry datasets of hundreds and thousands of diverse products across tens of stores, offering an optimization targets coverage scale in the tens of thousands, far larger than experimental setups in past research.
Approximating Memorization Using Loss Surface Geometry for Dataset Pruning and Summarization
Authors: Andrea Agiollo, Young In Kim, Rajiv Khanna
Venue: KDD
Year: 2024

The sustainable training of modern neural network models represents an open challenge. Several existing methods approach this issue by identifying a subset of relevant data samples from the full training data to be used in model optimization with the goal of matching the performance of the full data training with that of the subset data training. Our work explores using memorization scores to find representative and atypical samples. We demonstrate that memorization-aware dataset summarization improves the subset construction performance. However, computing memorization scores is notably resource-intensive. To this end, we propose a novel method that leverages the discrepancy between sharpness-aware minimization and stochastic gradient descent to capture data points atypicality. We evaluate our metric over several efficient approximation functions for memorization scores - namely proxies -, empirically showing superior correlation and effectiveness. We explore the causes behind our approximation quality, highlighting how typical data points trigger a flatter loss landscape compared to atypical ones. Extensive experiments confirm the effectiveness of our proxy for dataset pruning and summarization tasks, surpassing state-of-the-art approaches both on canonical setups - where atypical data points benefit performance - and few-shot learning scenarios-where atypical data points can be detrimental.
Resilient k-Clustering
Authors: Sara Ahmadian, MohammadHossein Bateni, Hossein Esfandiari, Silvio Lattanzi, Morteza Monemizadeh, Ashkan Norouzi-Fard
Venue: KDD
Year: 2024

We study the problem of resilient clustering in the metric setting where one is interested in designing algorithms that return high quality solutions that preserve the clustering structure under perturbations of the input points. Our first contribution is to introduce a formal notion of algorithmic resiliency for clustering problems that, roughly speaking, requires an algorithm to have similar outputs on close inputs. Then, we notice that classic algorithms have weak resiliency guarantees and develop new algorithms for fundamental clustering problems such as k-center, k-median, and k-means. Finally, we complement our results with an experimental analysis showing the effectiveness of our techniques on real-world instances.
Machine Learning in Finance
Authors: Leman Akoglu, Nitesh V. Chawla, Josep Domingo-Ferrer, Eren Kurshan, Senthil Kumar, Vidyut M. Naware, José A. Rodríguez-Serrano, Isha Chaturvedi, Saurabh Nagrecha, Mahashweta Das, Tanveer A. Faruquie
Venue: KDD
Year: 2024

This workshop aims to explore the intersection of Generative AI with the rich tapestry of financial data types, seeking to uncover new methodologies and techniques that can enhance predictive analytics, fraud detection, and customer insights across the sector. By harnessing these advancements in AI, we can pave the way to not only understand customer behavior but also anticipate their needs more effectively, leading to superior customer outcomes and more personalized services. Our objective is to shed light on the challenges and opportunities presented by the diverse data formats in finance. We aim to bridge the gap between the dominance of traditional models for tabular data analysis and the emerging potential of Generative AI to revolutionize the treatment of time series, click streams, and other unstructured data forms.
Workshop on Deep Learning and Large Language Models for Knowledge Graphs (DL4KG)
Authors: Mehwish Alam, Davide Buscaldi, Michael Cochez, Genet Asefa Gesese, Francesco Osborne, Diego Reforgiato Recupero
Venue: KDD
Year: 2024

The use of Knowledge Graphs (KGs) which constitute large networks of real-world entities and their interrelationships, has grown rapidly. A substantial body of research has emerged, exploring the integration of deep learning (DL) and large language models (LLMs) with KGs. This workshop aims to bring together leading researchers in the field to discuss and foster collaborations on the intersection of KG and DL/LLMs.