OpenRaiser/Intern-Atlas huggingface dataset:
Mining Temporal Networks
Authors: Aristides Gionis, Lutz Oettershagen, Ilie Sarpe
Venue: WWW
Year: 2024

In World Wide Web (WWW) systems, networks (or graphs) serve as a fundamental tool for representing, analyzing, and understanding linked data, providing significant insights into the underlying systems. Naturally, most real-world systems have inherent temporal information, e.g., interactions in social networks occur at specific moments in time and last for a certain period. Temporal networks, i.e., network data modeling temporal information, enable novel and fundamental discoveries about the underlying systems they model, otherwise not captured by static networks that ignore such temporal information. In this tutorial, we present state-of-the-art models and algorithmic techniques for mining temporal networks that can provide precious insights into a plethora of web-related applications. We present how temporal networks can be used to extract novel information, especially in web-related network data, and highlight the challenges that arise when modeling temporal information compared to traditional static network-based approaches. We first overview different temporal network models. We then show how such powerful models can be leveraged to extract novel insights through suitable mining primitives. In particular, we present recent advances addressing most foundational problems for temporal network mining---ranging from the computation of temporal centrality measures, temporal motif counting, and temporal communities to bursty events and anomaly detection.
Perceptions in Pixels: Analyzing Perceived Gender and Skin Tone in Real-world Image Search Results
Authors: Jeffrey L. Gleason, Avijit Ghosh, Ronald E. Robertson, Christo Wilson
Venue: WWW
Year: 2024

The results returned by image search engines have the power to shape peoples' perceptions about social groups. Existing work on image search engines leverages hand-selected queries for occupations like "doctor" and "engineer" to quantify racial and gender bias in search results. We complement this work by analyzing peoples' real-world image search queries and measuring the distributions of perceived gender, skin tone, and age in their results. We collect 54,070 unique image search queries and analyze 1,481 open-ended people queries (i.e. not queries for named entities) from a representative sample of 643 US residents. For each query, we analyze the top 15 results returned on both Google and Bing Images. Analysis of real-world image search queries produces multiple insights. First, less than 5% of unique queries are open-ended people queries. Second, fashion queries are, by far, the most common category of open-ended people queries, accounting for over 30% of the total. Third, the modal skin tone on the Monk Skin Tone scale is two out of ten (the second lightest) for images from both search engines. Finally, we observe a bias against older people: eleven of our top fifteen query categories have a median age that is lower than the median age in the US.
Federated Learning Vulnerabilities: Privacy Attacks with Denoising Diffusion Probabilistic Models
Authors: Hongyan Gu, Xinyi Zhang, Jiang Li, Hui Wei, Baiqi Li, Xinli Huang
Venue: WWW
Year: 2024

Federal Learning (FL) is highly respected for protecting data privacy in a distributed environment. However, the correlation between the updated gradient and the training data opens up the possibility of data reconstruction for malicious attackers, thus threatening the basic privacy requirements of FL. Previous research on such attacks mainly focuses on two main perspectives: one exclusively relies on gradient attacks, which performs well on small-scale data but falter with large-scale data; the other incorporates images prior but faces practical implementation challenges. So far, the effectiveness of privacy leakage attacks in FL is still far from satisfactory. In this paper, we introduce the Gradient Guided Diffusion Model (GGDM), a novel learning-free approach based on a pre-trained unconditional Denoising Diffusion Probabilistic Models (DDPM), aimed at improving the effectiveness and reducing the difficulty of implementing gradient based privacy attacks on complex networks and high-resolution images. To the best of our knowledge, this is the first work to employ the DDPM for privacy leakage attacks of FL. GGDM capitalizes on the unique nature of gradients and guides DDPM to ensure that reconstructed images closely mirror the original data. In addition, in GGDM, we elegantly combine the gradient similarity function with the Stochastic Differential Equation (SDE) to guide the DDPM sampling process based on theoretical analysis, and further reveal the impact of common similarity functions on data reconstruction. Extensive evaluation results demonstrate the excellent generalization ability of GGDM. Specifically, compared with state-of-the-art methods, GGDM shows clear superiority in both quantitative metrics and visualization, significantly enhancing the reconstruction quality of privacy attacks.
Event GDR: Event-Centric Generative Document Retrieval
Authors: Yong Guan, Dingxiao Liu, Jinchen Ma, Hao Peng, Xiaozhi Wang, Lei Hou, Ru Li
Venue: WWW
Year: 2024

Generative document retrieval, an emerging paradigm in information retrieval, learns to build connections between documents and identifiers within a single model, garnering significant attention. However, there are still two challenges: (1) neglecting inner-content correlation during document representation; (2) lacking explicit semantic structure during identifier construction. Nonetheless, events have enriched relations and well-defined taxonomy, which could facilitate addressing the above two challenges. Inspired by this, we propose Event GDR, an event-centric generative document retrieval model, integrating event knowledge into this task. Specifically, we utilize an exchange-then-reflection method based on multi-agents for event knowledge extraction. For document representation, we employ events and relations to model the document to guarantee the comprehensiveness and inner-content correlation. For identifier construction, we map the events to well-defined event taxonomy to construct the identifiers with explicit semantic structure. Our method achieves significant improvement over the baselines on two datasets, and also hopes to provide insights for future research.
Generating Privacy-preserving Educational Data Records with Diffusion Model
Authors: Quanlong Guan, Yanchong Yu, Xiujie Huang, Liangda Fang, Chaobo He, Lusheng Wu, Weiqi Luo, Guanliang Chen
Venue: WWW
Year: 2024

Educational Data Records (EDR) are crucial for capturing teaching behavior and student information, forming the basis for achieving educational intelligence. However, ensuring educational privacy has become a pressing concern, posing practical challenges to the use and sharing of educational data. To address the issue of EDR privacy preserving, we present EduSyn, a privacy data release scheme that utilizes generative diffusion models and differential privacy methods. Specifically, we adopt a diffusion modeling scheme that can be applied to both discrete and continuous types of data to accommodate the data characteristics of EDR, while an invariant Post Randomization (PRAM) perturbation method that satisfies local differential privacy is applied for data attributes that need to be specially protected before model training. We conduct comprehensive validation of this scheme within the domain of education applications, showcasing that EduSyn generates a superior private EDR dataset compared to similar generative methods and strikes a better privacy-utility trade-off.
Query in Your Tongue: Reinforce Large Language Models with Retrievers for Cross-lingual Search Generative Experience
Authors: Ping Guo, Yue Hu, Yanan Cao, Yubing Ren, Yunpeng Li, Heyan Huang
Venue: WWW
Year: 2024

In the contemporary digital landscape, search engines play an invaluable role in information access, yet they often face challenges in Cross-Lingual Information Retrieval (CLIR). Though attempts are made to improve CLIR, current methods still leave users grappling with issues such as misplaced named entities and lost cultural context when querying in non-native languages. While some advances have been made using Neural Machine Translation models and cross-lingual representation, these are not without limitations. Enter the paradigm shift brought about by Large Language Models (LLMs), which have transformed search engines from simple retrievers to generators of contextually relevant information. This paper introduces the Multilingual Information Model for Intelligent Retrieval (MIMIR). Built on the power of LLMs, MIMIR directly responds in the language of the user's query, reducing the need for post-search translations. Our model's architecture encompasses a dual-module system: a retriever for searching multilingual documents and a responder for crafting answers in the user's desired language. Through a unique unified training framework, with the retriever serving as a reward model supervising the responder, and in turn, the responder producing synthetic data to refine the retriever's proficiency, MIMIR's retriever and responder iteratively enhance each other. Performance evaluations via CLEF and MKQA benchmarks reveal MIMIR's superiority over existing models, effectively addressing traditional CLIR challenges.
AI-based Prediction of Catheter-related Thrombosis Risk for Cancer Patients
Authors: Yaoqi Guo, Yun Ma, Zijian Shao, Weichen Bi, Yanfeng Wang
Venue: WWW
Year: 2024

Cancer patients face a heightened risk of venous thromboembolism (VTE), emerging as the second most prevalent cause of death within this population. Central venous catheterization (CVC), a routine procedure in cancer care, amplifies the VTE risk, leading to catheter-related thrombosis (CRT). Although traditional risk-assessment models and certain AI methods exist for VTE prediction, their capability and application in CRT risk prediciton for cancer patients remains limited. This paper addresses the shortcomings of current models (RAMs) by crafting a dedicated AI model to predict CRT risks for cancer patients. Leveraging a dataset encompassing 10,512 cancer patients undergoing catheterization over a decade, we meticulously select nine specific features for model construction, resulting in an impressive 0.794 AUROC in prediction, 54.9% higher than baseline. Furthermore, we estimate CRT-free probability using the Kaplan-Meier method. We also develop a WeChat Mini Program designed for efficient data collection and risk prediction, enhancing the efficiency of CRT risk detection for both doctors and patients.
PASS: Predictive Auto-Scaling System for Large-scale Enterprise Web Applications
Authors: Yunda Guo, Jiake Ge, Panfeng Guo, Yunpeng Chai, Tao Li, Mengnan Shi, Yang Tu, Jian Ouyang
Venue: WWW
Year: 2024

We confront two challenges in the management of a vast and diverse array of online web applications deployed on enterprise-grade auto-scaling infrastructure, primarily focused on ensuring Quality of Service (QoS) for large-scale applications and optimizing resource costs. Firstly, reacting to increased load with a response-based approach can temporarily degrade QoS because many web applications need a few minutes to warm up. Therefore, precise workload prediction is critical for predictive scaling. However, our analysis of real-world applications underscores the substantial challenges arising from the limited precision and robustness of existing single prediction algorithms in the context of predictive auto-scaling. Secondly, guaranteeing the QoS of online applications within a cost-effective structure is crucial, as it is inherently linked to corporate profitability. Nevertheless, our study shows that mainstream auto-scaling methods exhibit various limitations, either being unsuitable for online environments or inadequately ensuring QoS. To address these issues, we introduce PASS, a Predictive Auto-Scaling System tailored for large-scale online web applications in enterprise settings. Our highly robust and accurate prediction framework dynamically integrates and calibrates appropriate prediction algorithms based on the unique characteristics of each application to effectively manage workload diversity. We further establish a performance model derived from online historical logs, enhancing auto-scaling to ensure diverse QoS without adverse impacts on online applications. Additionally, we implement a reactive strategy grounded in queuing theory to promptly address QoS violations resulting from inaccurate predictions or unexpected events. Across a wide spectrum of applications and real-world workloads, PASS outperforms state-of-the-art methods, achieving higher workload prediction accuracy and a superior QoS guarantee rate with less resource cost.
Bayesian Iterative Prediction and Lexical-based Interpretation for Disturbed Chinese Sentence Pair Matching
Authors: Muzhe Guo, Muhao Guo, Juntao Su, Junyu Chen, Jiaqian Yu, Jiaqi Wang, Hongfei Du, Parmanand Sahu, Ashwin Assysh Sharma, Fang Jin
Venue: WWW
Year: 2024

In an era dominated by web-based intelligent customer services, the applications of Sentence Pair Matching are profoundly broad. Web agents, for example, automatically respond to customer queries by finding similar past questions, significantly reducing customer service expenses. While current large language models (LLMs) offer powerful text generation capabilities, they often struggle with opacity, potential text toxicity, and difficulty managing domain-specific and confidential business inquiries. Consequently, the widespread adoption of web-based intelligent customer services in real-world business still greatly relies on query-based interactions. In this paper, we introduce a series of model-agnostic techniques aimed at enhancing both the accuracy and interpretability of Chinese pairwise sentence-matching models. Our contributions include (1) An Edit-distance-weighted fine-tuning method, (2) A Bayesian Iterative Prediction algorithm, (3) A Lexical-based Dual Ranking Interpreter, and (4) A Bi-criteria Denoising strategy. Experimental results on the Large-scale Chinese Question Matching Corpus (LCQMC) with a disturbed test demonstrate that our fine-tuning and prediction methods can steadily improve matching accuracy, building on the current state-of-the-art models. Besides, our interpreter with denoising strategy markedly enhances token-level interpretation in rationality and loyalty. In both matching accuracy and interpretation, our approaches outperform classic methods and even LLMs.
HBIAS FedAvg: Smooth Federated Learning Transition for In-use Edge Models
Authors: Anupam Gupta, Pabitra Mitra, Sudip Misra
Venue: WWW
Year: 2024

Federated learning is an approach for privacy preserving machine learning. It is increasingly being used in a number of classification as well as ranking tasks. Protocols for federated learning involve model update at the edge devices and aggregation at the central servers over multiple rounds. In practice, most deep learning models deployed on the edge are already trained and in-use. Federated learning protocols lead to an oscillation in the performance of these local models over the epochs. The drop in accuracy is more prominent in the early phases. In this article, we study such effects for the popular FedAvg federated learning algorithm and suggest the modified HBIAS FedAvg algorithm. The algorithm proposes a heuristic based initialization adoption strategy for this purpose. We find that this protocol leads to smoother performance variation for experiments on benchmark datasets.
Prior-Free Mechanism with Welfare Guarantees
Authors: Guru Guruganesh, Jon Schneider, Joshua R. Wang
Venue: WWW
Year: 2024

We consider the problem of designing prior-free revenue-maximizing mechanisms for allocating items to n buyers when the mechanism is additionally provided with an estimate for the optimal welfare (which is guaranteed to be correct to within a multiplicative factor of 1/α). In the digital goods setting (where we can allocate items to an arbitrary subset of the buyers), we demonstrate a mechanism that achieves revenue that is O(log n/α)-competitive with the optimal welfare. In the public goods setting (where we either must allocate the item to all buyers or to no buyers), we demonstrate a mechanism which is O(n log 1/α) competitive. In both settings, we show the dependence on α and n is tight. Finally, we discuss generalizations to broader classes of allocation constraints.
Decoding YouTube's Recommendation System: A Comparative Study of Metadata and GPT-4 Extracted Narratives
Authors: Mayor Inna Gurung, Md Monoarul Islam Bhuiyan, Ahmed Al-Taweel, Nitin Agarwal
Venue: WWW
Year: 2024

YouTube's recommendation system is integral to shaping user experiences by suggesting content based on past interactions using collaborative filtering techniques. Nonetheless, concerns about potential biases and homogeneity in these recommendations are prevalent, with the danger of leading users into filter bubbles and echo chambers that reinforce their pre-existing beliefs. Researchers have sought to understand and address these biases in recommendation systems. However, traditionally, such research has relied primarily on metadata, such as video titles, which does not always encapsulate the full content or context of the videos. This reliance on metadata can overlook the nuances and substantive content of videos, potentially perpetuating the very biases and echo chambers that the research aims to unravel. This study advances the examination of sentiment, toxicity, and emotion within YouTube content by conducting a comparative analysis across various depths of titles and narratives extracted by leveraging GPT-4. Our analysis reveals a clear trend in sentiment, emotion, and toxicity levels as the depth of content analysis increases. Notably, there is a general shift from neutral to positive sentiments in both YouTube video titles and narratives. Emotion analysis indicates an increase in positive emotions, particularly joy, with a corresponding decrease in negative emotions such as anger and disgust in narratives, while video titles show a steady decrease in anger. Additionally, toxicity analysis presents a contrasting pattern, with video titles displaying an upward trend in toxicity, peaking at the greatest depth analyzed, whereas narratives exhibit a high initial toxicity level that sharply decreases and stabilizes at lower depths. These findings suggest that the depth of engagement with video content significantly influences emotional and sentiment expressions.
GPT Assisted Annotation of Rhetorical and Linguistic Features for Interpretable Propaganda Technique Detection in News Text
Authors: Kyle Hamilton, Luca Longo, Bojan Bozic
Venue: WWW
Year: 2024

While the use of machine learning for the detection of propaganda techniques in text has garnered considerable attention, most approaches focus on "black-box'' solutions with opaque inner workings. Interpretable approaches provide a solution, however, they depend on careful feature engineering and costly expert annotated data. Additionally, language features specific to propagandistic text are generally the focus of rhetoricians or linguists, and there is no data set labeled with such features suitable for machine learning. This study codifies 22 rhetorical and linguistic features identified in literature related to the language of persuasion for the purpose of annotating an existing data set labeled with propaganda techniques. To help human experts annotate natural language sentences with these features, RhetAnn, a web application, was specifically designed to minimize an otherwise considerable mental effort. Finally, a small set of annotated data was used to fine-tune GPT-3.5, a generative large language model (LLM), to annotate the remaining data while optimizing for financial cost and classification accuracy. This study demonstrates how combining a small number of human annotated examples with GPT can be an effective strategy for scaling the annotation process at a fraction of the cost of traditional annotation relying solely on human experts. The results are on par with the best performing model at the time of writing, namely GPT-4, at 10x less the cost. Our contribution is a set of features, their properties, definitions, and examples in a machine-readable format, along with the code for RhetAnn and the GPT prompts and fine-tuning procedures for advancing state-of-the-art interpretable propaganda technique detection.
Turning A Curse into A Blessing: Data-Aware Memory-Efficient Training of Graph Neural Networks by Dynamic Exiting
Authors: Yan Han, Kaiqi Chen, Shan Li, Ji Yan, Baoxu Shi, Lei Zhang, Fei Chen, Jaewon Yang, Yunpeng Xu, Xiaoqiang Luo, Qi He, Ying Ding, Zhangyang Wang
Venue: WWW
Year: 2024

Training Graph Neural Networks (GNNs) efficiently remains a challenge due to the high memory demands, especially during recursive neighborhood aggregation. Traditional sampling-based GNN training methods often overlook the data's inherent structure, such as the power-law distribution observed in most real-world graphs, which results in inefficient memory usage and processing. We introduce a novel framework, M emory-A ware D ynamic E xiting GNN (MADE-GNN )), which capitalizes on the power-law nature of graph data to enhance training efficiency. MADE-GNN is designed to be data-aware, dynamically adjusting the depth of feature aggregation based on the connectivity of each node. Specifically, it routes well-connected "head'' nodes through extensive aggregation while allowing sparsely connected "tail'' nodes to exit early, thus reducing memory consumption without sacrificing model performance. This approach not only addresses the challenge of memory-intensive GNN training but also turns the power-law distribution from a traditional "curse'' into a strategic "blessing''. By enabling partial weight sharing between the early-exit mechanism and the full model, MADE-GNN effectively improves the representation of cold-start nodes, leveraging the structural information from head nodes to enhance generalization across the network. Our extensive evaluations across multiple public benchmarks, including industrial-level graphs, show that MADE-GNN outperforms existing GNN training methods in both memory efficiency and performance, offering significant improvements particularly for tail nodes. This demonstrates MADE-GNN's potential as a versatile solution for GNN applications facing similar scalability and distribution challenges.
ContraMTD: An Unsupervised Malicious Network Traffic Detection Method based on Contrastive Learning
Authors: Xueying Han, Susu Cui, Jian Qin, Song Liu, Bo Jiang, Cong Dong, Zhigang Lu, Baoxu Liu
Venue: WWW
Year: 2024

Malicious traffic detection has been a focal point in the field of network security, and deep learning-based approaches are emerging as a new paradigm. However, most of them are supervised methods, which highly depend on well-labeled data, and fail to handle unknown or continuously evolving attacks. Unsupervised methods alleviate the need for labeled data, but existing methods are often limited to detecting anomalies either in vertical perspective through historical comparisons or in horizontal perspective by comparing with concurrent entities. Relying on data from a single perspective is unreliable, and it limits the model's accuracy and generalizability. In this paper, we propose a novel method ContraMTD based on contrastive learning, which comprehensively considers both vertical and horizontal perspectives. ContraMTD extracts local behavior features and global interaction features from normal network traffic by proposed SEC and DE-GAT respectively, then employs contrastive learning to learn the relationship, especially consistency between them, and finally detects malicious traffic through a multi-round scoring approach. We conduct extensive experiments on three datasets, including a self-collected dataset, and the results demonstrate that our method outperforms many state-of-the-art methods in the domain of unsupervised malicious traffic detection.