OpenRaiser/Intern-Atlas huggingface dataset:
A Bayesian Framework for Measuring Association and Its Application to Emotional Dynamics in Web Discourse
Authors: Henrique S. Xavier, Diogo Cortiz, Mateus Silvestrin, Ana Luísa Freitas, Letícia Yumi Nakao Morello, Fernanda Naomi Pantaleão, Gabriel Gaudencio do Rêgo
Venue: WWW
Year: 2024

This paper introduces a Bayesian framework designed to measure the degree of association between categorical random variables. The method is grounded in the formal definition of variable independence and is implemented using Markov Chain Monte Carlo (MCMC) techniques. Unlike commonly employed techniques in Association Rule Learning, this approach enables a clear and precise estimation of confidence intervals and the statistical significance of the measured degree of association. We applied the method to non-exclusive emotions identified by annotators in 4,613 tweets written in Portuguese. This analysis revealed pairs of emotions that exhibit associations and mutually opposed pairs. Moreover, the method identifies hierarchical relations between categories, a feature observed in our data, and is utilized to cluster emotions into basic-level groups.
Hierarchical Graph Signal Processing for Collaborative Filtering
Authors: Jiafeng Xia, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang, Li Shang, Ning Gu
Venue: WWW
Year: 2024

Graph Signal Processing (GSP) has proven to be a highly effective and efficient tool for predicting user future interactions in recommender systems. However, current GSP methods recognize user interaction patterns based on the interactions of all users, so that the recognized interaction patterns are not fully user-matched and easily impacted by other users with different interaction behaviors, resulting in sub-optimal recommendation performance. To this end, we propose a hierarchical graph signal processing method (HiGSP) for collaborative filtering, which consists of two key modules: 1) the cluster-wise filter module that recognizes user unique interaction patterns merely from interactions of users with similar preferences, making the recognized patterns able to reflect user preference without being influenced by other users with different interaction behaviors, and 2) the globally-aware filter module that serves as a complementary to the cluster-wise filter module to recognize user general interaction patterns more effectively from all user interactions. By linearly combining these two modules, HiGSP can recognize user-matched interaction patterns, so as to model user preference and predict user future interactions more accurately. Extensive experiments on six real-world datasets demonstrate the superiority of HiGSP compared to other GCN-based and GSP-based recommendation methods in terms of efficacy and efficiency.
Why Deeper Layers May Introduce More Bias
Authors: Rui Xia, Lisong Wang, Pingping Shi
Venue: WWW
Year: 2024

One of the byproducts of message passing neural networks (MPNNs) is their potential bias towards weakly connected nodes, which can result in degraded performance. This paper confirms that as the number of layers increases, this bias becomes more closely associated with an imbalance in the distribution of eigenvector centrality, known as localization, which further amplifies the discrepancy in label influence on nodes, resulting in a performance gap. Therefore, we explore the effectiveness of non-backtracking centrality and PageRank centrality in mitigating this bias in MPNNs.
Multi-Scenario Pricing for Hotel Revenue Management
Authors: Wendong Xiao, Shuqi Zhang, Zhiyi Huang, Yao Yu
Venue: WWW
Year: 2024

Dynamic pricing algorithms have been widely studied to manage hotel and platform revenue over online travel platforms (OTPs). For better dynamic pricing, the accurate estimation of the market demand and the market competitiveness are crucial. However, the existing approaches obtain a pricing strategy tailored to each specific scenario using data only from that scenario. They are not considering the shared information between different scenarios, i.e., the data from different scenarios are not fully utilized. So we propose a Multi Scenario Pricing model (MSP) with a novel sharing structure design that leverages cross-scenario and specific information to capture more accurate market demand and competitiveness. Specifically, the model structure explicitly separates information into shared components as market demand and specific information as scenario-wise price competitiveness to prevent domain seesaw. To capture the inherent correlation between listings in different scenarios, an attention network named Price Competitiveness Representation Extraction (PCRE) is well-designed. Meanwhile, traditional metrics are skewed towards model that tends to reduce the price regardless of sample distribution. Thus we propose new offline evaluation metrics that shift attention with sample distribution to avoid biased pricing strategies, which is proved to be more closely related to actual business revenue. Our proposed MSP shows superiority under both offline and online experiments on real-world datasets. The multi-scenario industry dataset and our code are available. To the best of our knowledge, it will be the first real-industry multi-scenario pricing data.
NCTM: A Novel Coded Transmission Mechanism for Short Video Deliveries
Authors: Zhenge Xu, Qing Li, Wanxin Shi, Yong Jiang, Zhenhui Yuan, Peng Zhang, Gabriel-Miro Muntean
Venue: WWW
Year: 2024

With the rapid popularity of short video applications, a large number of short video transmissions occupy the bandwidth, placing a heavy load on the Internet. Due to the extensive number of short videos and the predominant service for mobile users, traditional approaches (e.g., CDN delivery, edge caching) struggle to achieve the expected performance, leading to a significant number of redundant transmissions. In order to reduce the amount of traffic, we design a Novel Coded Transmission Mechanism (NCTM), which transmits XOR-coded data instead of the original video content. NCTM caches the short videos that users have already watched in user devices, and encodes, multicasts, and decodes XOR-coded files separately at the server, edge nodes, and clients, with the assistance of cached content. This approach enables NCTM to deliver more short video data given the limited bandwidth. Our extensive trace-driven simulations show how NCTM reduces network load by 3.02%-14.75%, cuts peak traffic by 23.01%, and decreases rebuffering events by 43%-85% in comparison to a CDN-supported scheme and a naive edge caching scheme. Additionally, NCTM also increases the user's buffered video duration by 1.21x-13.53x, ensuring improved playback smoothness.
I-CoSim: Efficient Dynamic CoSimRank Retrieval on Evolving Networks
Authors: Xiaoyu Xu, Weiren Yu
Venue: WWW
Year: 2024

CoSimRank, a favorable measure for assessing node similarity based on graphs, faces computational challenges on real evolving graphs. The best-of-breed algorithm, D-CoSim, for incremental CoSimRank search evaluates similarity changes by summing dot products between two vectors. These vectors are iteratively generated from scratch in the original high-dimensional space, leading to significant costs. In this paper, we propose I-CoSim, a novel efficient dynamic CoSimRank algorithm for evolving graphs. I-CoSim resorts to two low-dimensional Krylov subspaces and maximally reuses previously computed similarities in the original graph, which substantially expedites CoSimRank search on evolving graphs. We also theoretically provide an error bound on the I-CoSim estimation with guaranteed accuracy. Experimental results on real datasets show that I-CoSim is up to 28 times faster than the best-known competitor, with only a slight compromise in accuracy.
Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks
Authors: Yichang Xu, Ming Yin, Minghong Fang, Neil Zhenqiang Gong
Venue: WWW
Year: 2024

Recent studies have revealed that federated learning (FL), once considered secure due to clients not sharing their private data with the server, is vulnerable to attacks such as client-side training data distribution inference, where a malicious client can recreate the victim's data. While various countermeasures exist, they are not practical, often assuming server access to some training data or knowledge of label distribution before the attack.
3D Face Reconstruction Using A Spectral-Based Graph Convolution Encoder
Authors: Haoxin Xu, Zezheng Zhao, Yuxin Cao, Chunyu Chen, Hao Ge, Ziyao Liu
Venue: WWW
Year: 2024

Monocular 3D face reconstruction plays a crucial role in avatar generation, with significant demand in web-related applications such as generating virtual financial advisors in FinTech. Current reconstruction methods predominantly rely on deep learning techniques and employ 2D self-supervision as a means to guide model learning. However, these methods encounter challenges in capturing the comprehensive 3D structural information of the face due to the utilization of 2D images for model training purposes. To overcome this limitation and enhance the reconstruction of 3D structural features, we propose an innovative approach that integrates existing 2D features with 3D features to guide the model learning process. Specifically, we introduce the 3D-ID Loss, which leverages the high-dimensional structure features extracted from a Spectral-Based Graph Convolution Encoder applied to the facial mesh. This approach surpasses the sole reliance on the 3D information provided by the facial mesh vertices coordinates. Our model is trained using 2D-3D data pairs from a combination of datasets and achieves state-of-the-art performance on the NoW benchmark.
Incentives in the Ether: Practical Cryptocurrency Economics & Security
Authors: Aviv Yaish
Venue: WWW
Year: 2024

Cryptocurrencies are becoming increasingly important for the modern economy. Prior literature focuses on aligning actor incentives to ensure the secure and efficient operation of cryptocurrencies against adversarial threats that are unobserved in the wild. In this work, we address the gap between the theory and practice of cryptocurrencies by advancing realistic approaches to analyze the economics and security of key cryptocurrency components: consensus mechanisms, transaction fee mechanisms (TFMs), and the application layer. We present novel models of these components that we evaluate both theoretically and using cryptocurrency clients. We augment our evaluation with the first evidence of an in-the-wild attack on a major cryptocurrency, highlighting our approach's practicality. Results contained in our work were adopted by cryptocurrency platforms that hold user assets worth over 300 billion.
Enabling Pre-Shock State Detection using Electrogram Signals from Implantable Cardioverter-Defibrillators
Authors: Runze Yan, Neal K. Bhatia, Faisal M. Merchant, Alex Fedorov, Ran Xiao, Cheng Ding, Xiao Hu
Venue: WWW
Year: 2024

Identifying electrical signatures preceding a ventricular arrhythmia from the implantable cardioverter-defibrillators (ICDs) can help predict an upcoming ICD shock. To achieve this, we first deployed a large-scale study (N=326) to continuously monitor the electrogram (EGM) data from the ICDs and select the EGM segments prior to a shock event and under the normal condition. Next, we design a novel cohesive framework that integrates metric learning, prototype learning, and few-shot learning, enabling learning from an imbalanced dataset. We implement metric learning by leveraging a Siamese neural network architecture, which incorporates LSTM units. We innovatively utilize triplet and pair losses in a sequential manner throughout the training process on EGM samples. This approach generates embeddings that significantly enhance the distinction of EGM signals under different conditions. In the inference stage, k-means clustering identifies prototypes representing pre-shock and normal states from these embeddings. In summary, this framework leverages the predictive potential of signals before ICD shocks, addressing the gap in early cardiac arrhythmia detection. Our experimental results show a notable F1 score of 0.87, sensitivity of 0.97, and precision of 0.79. Our framework offers a significant advancement in cardiac care predictive analytics, promising enhanced ICD decision-making for improved patient outcomes.
PaCEr: Network Embedding From Positional to Structural
Authors: Yuchen Yan, Yongyi Hu, Qinghai Zhou, Lihui Liu, Zhichen Zeng, Yuzhong Chen, Menghai Pan, Huiyuan Chen, Mahashweta Das, Hanghang Tong
Venue: WWW
Year: 2024

Network embedding plays an important role in a variety of social network applications. Existing network embedding methods, explicitly or implicitly, can be categorized into positional embedding (PE) methods or structural embedding (SE) methods. Specifically, PE methods encode the positional information and obtain similar embeddings for adjacent/close nodes, while SE methods aim to learn identical representations for nodes with the same local structural patterns, even if the two nodes are far away from each other. The disparate designs of the two types of methods lead to an apparent dilemma in that no embedding could perfectly capture both positional and structural information. In this paper, we seek to demystify the underlying relationship between positional embedding and structural embedding. We first point out that the positional embedding can produce the structural embedding with simple transformations, while the opposite direction cannot hold. Based on this finding, a novel network embedding model PACER is proposed, which optimizes the positional embedding with the help of random walk with restart (RWR) proximity distribution, and such positional embedding is then used to seamlessly obtain the structural embedding with simple transformations. Furthermore, two variants of PACER are proposed to handle node classification task on homophilic and heterophilic graphs. Extensive experiments on 17 datasets show that PACER achieves comparable or better performance than the state-of-the-arts.
Robust Route Planning under Uncertain Pickup Requests for Last-mile Delivery
Authors: Hua Yan, Heng Tan, Haotian Wang, Desheng Zhang, Yu Yang
Venue: WWW
Year: 2024

Empowered by the widespread adoption of Internet of Things (IoT) devices and smartphones, last-mile delivery services have evolved to accommodate both delivery and pickup tasks. An essential challenge in last-mile delivery is efficiently planning routes for couriers to handle pre-scheduled delivery requests as well as stochastic pickup requests. Existing work approaches this problem by either adjusting routes on the fly when new requests arise or preplanning routes based on predicted future pickup requests. However, these methods either compromise the optimality of planned routes or heavily rely on the accuracy of predictions. In this work, we take conformal prediction as an opportunity to address the issue of prediction uncertainty. We design ROPU, a novel courier route planning framework for logistics systems that incorporates conformal prediction into reinforcement learning. Our work advances the existing work from two aspects: (i) Pickup request prediction utilizes spatial-temporal conformal prediction to capture historical pickup request patterns, providing a unified spatial-temporal conformal interval with high confidence (ii) A spatial-temporal attention network assesses location importance from various perspectives and enables the actor to perceive time and integrate the spatial-temporal conformal interval. We implement and evaluate ROPU on one of the largest logistics platforms. Extensive experiment results demonstrate that our method outperforms other state-of-the-art methods with improvements of at least 30.49% in the pickup overdue rate, 25.00% in the delivery overdue rate, and 5.49% in the traveling distance metric.
GAUSS: GrAph-customized Universal Self-Supervised Learning
Authors: Liang Yang, Weixiao Hu, Jizhong Xu, Runjie Shi, Dongxiao He, Chuan Wang, Xiaochun Cao, Zhen Wang, Bingxin Niu, Yuanfang Guo
Venue: WWW
Year: 2024

To make Graph Neural Networks (GNNs) meet the requirements of the Web, the universality and the generalization become two important research directions. On one hand, many universal GNNs are presented for semi-supervised tasks on both homophilic and non-homophilic graphs by distinguishing homophilic and heterophilic edges with the help of labels. On the other hand, self-supervised learning (SSL) algorithms on graphs are presented by leveraging the self-supervised learning schemes from computer vision and natural language processing. Unfortunately, graph universal self-supervised learning remains resolved. Most existing SSL methods on graphs, which often employ two-layer GCN as the encoder and train the mapping functions, can't alter the low-passing filtering characteristic of GCN. Therefore, to be universal, SSL must becustomized for the graph, i.e., learning the graph. However, learning the graph via universal GNNs is disabled in SSL, since their distinguishability on homophilic and heterophilic edges disappears without the labels. To overcome this difficulty, this paper proposes novel GrAph-customized Universal Self-Supervised Learning (GAUSS) by exploiting local attribute distribution. The main idea is to replace the global parameters with locally learnable propagation. To make the propagation matrix demonstrate the affinity between the nodes, the self-representative learning framework is employed with k-block diagonal regularization. Extensive experiments on synthetic and real-world datasets demonstrate its effectiveness, universality and robustness to noises.
Not All Embeddings are Created Equal: Towards Robust Cross-domain Recommendation via Contrastive Learning
Authors: Wenhao Yang, Yingchun Jian, Yibo Wang, Shiyin Lu, Lei Shen, Bing Wang, Haihong Tang, Lijun Zhang
Venue: WWW
Year: 2024

Cross-domain recommendation (CDR) aims to leverage the rich information from the source domain to enhance recommendation performance in the target domain. However, the data imbalance problem inherent across different domains compromises the effectiveness of CDR approaches, posing a significant challenge to CDR. Most current CDR methodologies focus on creating better user embeddings for the target domain, yet usually neglect the inconsistency in user activities due to data imbalance. As a result, the process of creating user embeddings tends to prioritize users with more frequent interactions and leave less active users underserved, leading these CDR methods to struggle in making accurate recommendations for those with fewer interactions. Such bias in creating embeddings reveals the fact that ''not all embeddings are created equal'' in CDR, which serves as the primary motivation of this study. Inspired by the recent development of contrastive learning, this paper proposes User-aware Contrastive Learning for Robust cross-domain recommendation (UCLR), enhancing the robustness of cross-domain recommendation. Specifically, our proposed method consists of two sub-modules: (i) pretrained global embedding, where the global user embeddings are pretrained across all the domains; (ii) contrastive dual-stream collaborative autoencoder, where more equal user embeddings are generated by optimizing contrastive loss with individualized temperatures. To further improve the performance of our method in each domain, we finetune the whole framework of UCLR based on Low-Rank Adaptation (LoRA). Theoretically, our method is equipped with a provable convergence guarantee during the contrastive learning stage. Furthermore, we also conduct comprehensive experiments on real-world datasets to validate the effectiveness of our proposed method.
Detecting Illicit Food Factories from Chemical Declaration Data via Graph-aware Self-supervised Contrastive Anomaly Ranking
Authors: Sheng-Fang Yang, Cheng-Te Li
Venue: WWW
Year: 2024

In the global food industry, where the line between legitimate and illicit manufacturing is increasingly blurred by the scale and complexity of the supply chain, safeguarding consumer health and trust necessitates innovative detection methods. Addressing this, this paper presents Graph-aware Self-supervised Contrastive Anomaly Ranking (GraphCAR), a novel unsupervised learning model, devised to identify illicit food factories through the scrutiny of chemical declaration data. GraphCAR tackles the scarcity of labeled data and the intricacies inherent in the vast array of declared chemicals, leveraging a Graph Autoencoder fused with a self-supervised contrastive learning mechanism. This fusion not only simplifies the feature space by embedding chemical declarations within a bipartite graph but also adeptly flags subtle, potentially illicit patterns through contrastively inspecting the learned factory representations. Through rigorous evaluations conducted on real-world factory's chemical declaration data, GraphCAR has demonstrated superior performance over conventional methods on unsupervised outlier detection and one-class classification tasks, showcasing its accuracy, robustness and reliability in flagging potential malpractice. With its successful application in food safety, GraphCAR stands as a testament to the potential of AI-driven solutions to address multifaceted challenges for the greater good.
Calibrating Graph Neural Networks from a Data-centric Perspective
Authors: Cheng Yang, Chengdong Yang, Chuan Shi, Yawen Li, Zhiqiang Zhang, Jun Zhou
Venue: WWW
Year: 2024

Graph neural networks (GNNs) have gained popularity in modeling various complex networks, e.g., social network and webpage network. Despite the promising accuracy, the confidences of GNNs are shown to be miscalibrated, indicating limited awareness of prediction uncertainty and harming the reliability of model decisions. Existing calibration methods primarily focus on improving GNN models, e.g., adding regularization during training or introducing temperature scaling after training. In this paper, we argue that the miscalibration of GNNs may stem from the graph data and can be alleviated through topology modification. To support this motivation, we conduct data observations by examining the impacts ofdecisive andhomophilic edges on calibration performance, where decisive edges play a critical role in GNN predictions and homophilic edges connect nodes of the same class. By assigning larger weights to these edges in the adjacency matrix, we observe an improvement in calibration performance without sacrificing classification accuracy. This suggests the potential of a data-centric approach for calibrating GNNs. Motivated by our observations, we propose Data-centric Graph Calibration (DCGC), which uses two edge weighting modules to adjust the input graph for GNN calibration. The first module learns the weights of decisive edges by parameterizing the adjacency matrix and enabling backpropagation of the prediction loss to edge weights. This emphasizes critical edges that fit the prediction needs. The second module computes weights for homophilic edges based on predicted label distributions, assigning larger weights to edges with stronger homophily. These modifications operate at the data level and can be easily integrated with temperature scaling-based methods for better calibration. Experimental results on 8 benchmark datasets demonstrate that DCGC achieves state-of-the-art calibration performance, with an average relative improvement of 36.4% in ECE, while maintaining or even slightly improving classification accuracy. Ablation studies and hyper-parameter analysis further validate the effectiveness and robustness of our proposed method DCGC. Code and data are available at https://github.com/BUPT-GAMMA/DCGC.