OpenRaiser/Intern-Atlas huggingface dataset:
SMUG: Sand Mixing for Unobserved Class Detection in Graph Few-Shot Learning
Authors: Chenxu Wang, Xichan Nie, Jinfeng Chen, Pinghui Wang, Junzhou Zhao, Xiaohong Guan
Venue: WWW
Year: 2024

Graph few-shot learning (GFSL) has achieved great success in node classification tasks with rare labels. However, graph few-shot classification (GFSC) models often encounter the problem of classifying test samples with unobserved (or unknown) classes due to the rareness of labels. We formulate this problem as out-of-distribution (OOD) sample detection in inductive graph few-shot learning. This paper presents SMUG, a novel GFSL framework that can detect unobserved classes. Since we have no ground-truth OOD samples in a practical training dataset, it is challenging for the GFSC model to retrieve knowledge about unknown classes from labeled samples. To address this difficulty, we propose a sand mixing scheme to introduce observed classes as artificial OOD samples into meta-tasks. We also develop two unsupervised OOD discriminators to identify OOD samples. Thus, we can assess the performance of OOD discriminators since we know the true classes of these artificial OOD samples. Subsequently, we design a novel training procedure to optimize the encoder based on the performance of the OOD discriminators and the GFSC model. It not only enables the GFSL model to distinguish OOD samples but also promotes the classification accuracy of normal samples. We conduct extensive experiments to evaluate the effectiveness of SMUG based on four benchmark datasets. Experimental results demonstrate that SMUG achieves superior performance over state-of-the-art approaches in OOD detection and node classification. The source code of this paper is available at https://github.com/Memepp/SMUG.
HINCare: An Intelligent Helper Recommender System for Elderly Care
Authors: Carrie Wang, Wentao Ning, Xiaoman Wu, Reynold Cheng
Venue: WWW
Year: 2024

In Hong Kong, the number of elderly citizens will reach one-third of the population within the next decade. To mitigate this problem, timebanking has received attention in recent years. In timebanking, an NGO helper earns time credits through providing voluntary services (e.g., household duties) to elders. These time credits can be used to acquire other services. Although timebanking has shown the promise of promoting mutual care in many countries, its potential has not been fully utilized, due to the lack of IT and data support. We thus develop HINCare, a software platform that supports timebanking for multiple NGOs. Besides providing convenience to NGO supervisors, helpers, and elders, HINCare makes use of a heterogeneous information network (HIN) for recommending suitable helpers to elders. This is the first time a graph-based recommender system is used for such purposes. Currently, HINCare is used by 12 NGOs to serve more than 5000 users in Hong Kong. In this demonstration, participants can play the role of helpers and elders in the HINCare environment.
Synslator: An Interactive Machine Translation Tool with Online Learning
Authors: Jiayi Wang, Ke Wang, Fengming Zhou, Chengyu Wang, Zhiyong Fu, Zeyu Feng, Yu Zhao, Yuqi Zhang
Venue: WWW
Year: 2024

Interactive Machine Translation (IMT) advances the computer-aided translation (CAT) paradigm, enabling collaboration between machine translation systems and human translators for high-quality outputs. This paper presents Synslator, a CAT tool designed for IMT and proficient in online learning with real-time translation memories. Synslator accommodates different CAT service deployments by integrating two neural translation models for online learning and a language model to boost translation fluency interactively. Our evaluations demonstrate the system's online learning effectiveness, showing a 13% increase in post-editing efficiency with Synslator's interactive features. A tutorial video is provided at: https://youtu.be/K0vRsb2lTt8.
Identifying VPN Servers through Graph-Represented Behaviors
Authors: Chenxu Wang, Jiangyi Yin, Zhao Li, Hongbo Xu, Zhongyi Zhang, Qingyun Liu
Venue: WWW
Year: 2024

Identifying VPN servers is a crucial task in various situations, such as geo-fraud detection, bot traffic analysis and network attack identification. Although numerous studies that focus on network traffic detection have achieved excellent performance in closed-world scenarios, particularly those methods based on deep learning, they may exhibit significant performance degradation due to changes in network environment. To mitigate this issue, a few studies have attempted to use methods based on active probing to detect VPN servers. However, these methods still have two limitations. They cannot handle situations without probing responses and are limited in applicability due to their focus on specific VPNs. In this work, we propose VPNChecker, which utilizes the graph-represented behaviors to detect VPN servers in real-world scenarios. VPNChecker outperforms existing methods in four offline datasets. The results from our datasets, containing multiple different VPNs, indicate that VPNChecker has better applicability. Furthermore, we deploy VPNChecker in an Internet Service Provider's (ISP) environment to evaluate its effectiveness. The results show that VPNChecker can improve the coverage of sophisticated detection engines and serve as a complement to existing methods.
Curriculum Learning: Theories, Approaches, Applications, Tools, and Future Directions in the Era of Large Language Models
Authors: Xin Wang, Yuwei Zhou, Hong Chen, Wenwu Zhu
Venue: WWW
Year: 2024

This tutorial focuses on curriculum learning (CL), an important topic in machine learning, which gains an increasing amount of attention in the research community. CL is a learning paradigm that enables machines to learn from easy data to hard data, imitating the meaningful procedure of human learning with curricula. As an easy-to-use plug-in, CL has demonstrated its power in improving the generalization capacity and convergence rate of various models in a wide range of scenarios such as computer vision, natural language processing, data mining, reinforcement learning, etc. Therefore, it is essential introducing CL to more scholars and researchers in the machine learning community. However, there have been no tutorials on CL so far, motivating the organization of our tutorial on CL at WWW 2024. To give a comprehensive tutorial on CL, we plan to organize it from the following aspects: (1) theories, (2) approaches, (3) applications, (4) tools and (5) future directions. First, we introduce the motivations, theories and insights behind CL. Second, we advocate novel, high-quality approaches, as well as innovative solutions to the challenging problems in CL. Then we present the applications of CL in various scenarios, followed by some relevant tools. In the end, we discuss open questions and the future direction in the era of large language models. We believe this topic is at the core of the scope of WWW and is attractive to the audience interested in machine learning from both academia and industry.
Large Multimodal Model Compression via Iterative Efficient Pruning and Distillation
Authors: Maolin Wang, Yao Zhao, Jiajia Liu, Jingdong Chen, Chenyi Zhuang, Jinjie Gu, Ruocheng Guo, Xiangyu Zhao
Venue: WWW
Year: 2024

The deployment of Large Multimodal Models (LMMs) within Ant Group has significantly advanced multimodal tasks in payment, security, and advertising, notably enhancing advertisement audition tasks in Alipay. However, the deployment of such sizable models introduces challenges, particularly in increased latency and carbon emissions, which are antithetical to the ideals of Green AI. This paper introduces a novel multi-stage compression strategy for our proprietary LLM, AntGMM. Our methodology pivots on three main aspects: employing small training sample sizes, addressing multi-level redundancy through multi-stage pruning, and introducing an advanced distillation loss design. In our research, we constructed a dataset, the Multimodal Advertisement Audition Dataset (MAAD), from real-world scenarios within Alipay, and conducted experiments to validate the reliability of our proposed strategy. Furthermore, the effectiveness of our strategy is evident in its operational success in Alipay's real-world multimodal advertisement audition for three months from September 2023. Notably, our approach achieved a substantial reduction in latency, decreasing it from 700ms to 90ms, while maintaining online performance with only a slight performance decrease. Moreover, our compressed model is estimated to reduce electricity consumption by approximately 75 million kWh annually compared to the direct deployment of AntGMM, demonstrating our commitment to green AI initiatives.
The 2nd Workshop on Recommendation with Generative Models
Authors: Wenjie Wang, Yang Zhang, Xinyu Lin, Fuli Feng, Weiwen Liu, Yong Liu, Xiangyu Zhao, Wayne Xin Zhao, Yang Song, Xiangnan He
Venue: WWW
Year: 2024

The rise of generative models has driven significant advancements in recommender systems, leaving unique opportunities for enhancing users' personalized recommendations. This workshop serves as a platform for researchers to explore and exchange innovative concepts related to the integration of generative models into recommender systems. It primarily focuses on five key perspectives: (i) improving recommender algorithms, (ii) generating personalized content, (iii) evolving the user-system interaction paradigm, (iv) enhancing trustworthiness checks, and (v) refining evaluation methodologies for generative recommendations. With generative models advancing rapidly, an increasing body of research is emerging in these domains, underscoring the timeliness and critical importance of this workshop. The related research will introduce innovative technologies to recommender systems and contribute to fresh challenges in both academia and industry. In the long term, this research direction has the potential to revolutionize the traditional recommender paradigms and foster the development of next-generation recommender systems.
Detecting and Understanding Self-Deleting JavaScript Code
Authors: Xinzhe Wang, Zeyang Zhuang, Wei Meng, James Cheng
Venue: WWW
Year: 2024

Self-deletion is a well-known strategy frequently utilized by malware to evade detection. Recently, this technique has found its way into client-side JavaScript code, significantly raising the complexity of JavaScript analysis. In this work, we systematically study the emerging client-side JavaScript self-deletion behavior on the web. We tackle various technical challenges associated with JavaScript dynamic analysis and introduce JSRay, a browser-based JavaScript runtime monitoring system designed to comprehensively study client-side script deletion. We conduct a large-scale measurement of one million popular websites, revealing that script self-deletion is prevalent in the real world. While our findings indicate that most developers employ self-deletion for legitimate purposes, we also discover that self-deletion has already been employed together with other anti-analysis techniques for cloaking suspicious operations in client-side JavaScript.
Quantifying Governance of Online Communities at Web Scale
Authors: Galen Cassebeer Weld
Venue: WWW
Year: 2024

Online communities are powerful tools to connect people and are used worldwide by billions of people. Nearly all online communities rely upon moderators or admins to govern the community in order to mitigate potential harms such as harassment, polarization, and deleterious effects on mental health. However, online communities are complex systems, and studying the impact of community governance empirically at scale is challenging because of the many aspects of community governance and outcomes that must be quantified. In this work, we develop methods to quantify the governance of online communities at web scale. We survey community members to build a comprehensive understanding of what it means to make communities 'better,' then assess existing governance practices and associate them with important outcomes to inform community moderators. We collaborate with communities to deploy our governance interventions to maximize the positive impact of our work, and, at every step of the way, we make our datasets and methods public to support further research on this important topic.
Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale
Authors: Wei Wen, Kuang-Hung Liu, Igor Fedorov, Xin Zhang, Hang Yin, Weiwei Chu, Kaveh Hassani, Mengying Sun, Jiang Liu, Xu Wang, Lin Jiang, Yuxin Chen, Buyun Zhang, Xi Liu, Dehua Cheng, Zhengxing Chen, Guang Zhao, Fangqiu Han, Jiyan Yang, Yuchen Hao, Liang Xiong, Wen-Yen Chen
Venue: WWW
Year: 2024

Neural Architecture Search (NAS) has demonstrated its efficacy in computer vision and potential for ranking systems. However, prior work focused on academic problems, which are evaluated at small scale under well-controlled fixed baselines. In industry system, such as ranking system in Meta, it is unclear whether NAS algorithms from the literature can outperform production baselines because of: (1) scale - Meta's ranking systems serve billions of users, (2) strong baselines - the baselines are production models optimized by hundreds to thousands of world-class engineers for years since the rise of deep learning, (3) dynamic baselines - engineers may have established new and stronger baselines during NAS search, and (4) efficiency - the search pipeline must yield results quickly in alignment with the productionization life cycle. In this paper, we present Rankitect, a NAS software framework for ranking systems at Meta. Rankitect seeks to build brand new architectures by composing low level building blocks from scratch. Rankitect implements and improves state-of-the-art (SOTA) NAS methods for comprehensive and fair comparison under the same search space, including sampling-based NAS, one-shot NAS, and Differentiable NAS (DNAS). We evaluate Rankitect by comparing to multiple production ranking models at Meta. We find that Rankitect can discover new models from scratch achieving competitive trade-off between Normalized Entropy loss and FLOPs. When utilizing search space designed by engineers, Rankitect can generate better models than engineers, achieving positive offline evaluation and online A/B test at Meta scale.
Revisiting 30 years of the Network Time Protocol
Authors: Simon S. Woo
Venue: WWW
Year: 2024

Since the inception of the Internet and WWW, providing the time among multiple nodes on the Internet has been one of the most critical challenges. David Mills is the pioneer to provide time on the Internet, inventing the Network Time Protocol (NTP), and synchronizing the clocks in computer systems. Now, the NTP is predominantly used on the Internet and WWW. In this paper, we revisit the NTP, and present the overview of the NTP. In particular, we highlight the advanced research effort, the SpaceNTP, to synchronize the clocks among assets and entities in space. The SpaceNTP designed for space environments will be the fundamental medium and enabling block to provide the future web services in space.
On the Feasibility of Simple Transformer for Dynamic Graph Modeling
Authors: Yuxia Wu, Yuan Fang, Lizi Liao
Venue: WWW
Year: 2024

Dynamic graph modeling is crucial for understanding complex structures in web graphs, spanning applications in social networks, recommender systems, and more. Most existing methods primarily emphasize structural dependencies and their temporal changes. However, these approaches often overlook detailed temporal aspects or struggle with long-term dependencies. Furthermore, many solutions overly complicate the process by emphasizing intricate module designs to capture dynamic evolutions. In this work, we harness the strength of the Transformer's self-attention mechanism, known for adeptly handling long-range dependencies in sequence modeling. Our approach offers a simple Transformer model, called SimpleDyG, tailored for dynamic graph modeling without complex modifications. We re-conceptualize dynamic graphs as a sequence modeling challenge and introduce a novel temporal alignment technique. This technique not only captures the inherent temporal evolution patterns within dynamic graphs but also streamlines the modeling process of their evolution. To evaluate the efficacy of SimpleDyG, we conduct extensive experiments on four real-world datasets from various domains. The results demonstrate the competitive performance of SimpleDyG in comparison to a series of state-of-the-art approaches despite its simple design.
Not All Asians are the Same: A Disaggregated Approach to Identifying Anti-Asian Racism in Social Media
Authors: Fan Wu, Sanyam Lakhanpal, Qian Li, Kookjin Lee, Doowon Kim, Heewon Chae, Kyounghee Hazel Kwon
Venue: WWW
Year: 2024

Recent policy initiatives have acknowledged the importance of disaggregating data pertaining to diverse Asian ethnic communities to gain a more comprehensive understanding of their current status and to improve their overall well-being. However, research on anti-Asian racism has thus far fallen short of properly incorporating data disaggregation practices. Our study addresses this gap by collecting 12-month-long data from X (formerly known as Twitter) that contain diverse sub-ethnic group representations within Asian communities. In this dataset, we break down anti-Asian toxic messages based on both temporal and ethnic factors and conduct a series of comparative analyses of toxic messages, targeting different ethnic groups. Using temporal persistence analysis, n-gram-based correspondence analysis, and topic modeling, this study provides compelling evidence that anti-Asian messages comprise various distinctive narratives. Certain messages targeting sub-ethnic Asian groups entail different topics that distinguish them from those targeting Asians in a generic manner or those aimed at major ethnic groups, such as Chinese and Indian. By introducing several techniques that facilitate comparisons of online anti-Asian hate towards diverse ethnic communities, this study highlights the importance of taking a nuanced and disaggregated approach for understanding racial hatred to formulate effective mitigation strategies.
Simple Multigraph Convolution Networks
Authors: Danyang Wu, Xinjie Shen, Jitao Lu, Jin Xu, Feiping Nie
Venue: WWW
Year: 2024

Existing multigraph convolution methods either ignore the cross-view interaction among multiple graphs, or induce extremely high computational cost due to standard cross-view polynomial operators. To alleviate this problem, this paper proposes a Simple MultiGraph Convolution Networks (SMGCN) which first extracts consistent cross-view topology from multigraphs including edge-level and subgraph-level topology, then performs polynomial expansion based on raw multigraphs and consistent topologies. In theory, SMGCN utilizes the consistent topologies in polynomial expansion rather than standard cross-view polynomial expansion, which performs credible cross-view spatial message-passing, follows the spectral convolution paradigm, and effectively reduces the complexity of standard polynomial expansion. In the simulations, experimental results demonstrate that SMGCN achieves state-of-the-art performance on ACM and DBLP multigraph benchmark datasets. Our codes are available at https://github.com/frinkleko/SMGCN.
Targeted Filter Bubbles Mitigating via Edges Insertion
Authors: Yanping Wu, Jinghao Wang, Renjie Sun, Chen Chen, Xiaoyang Wang, Ying Zhang
Venue: WWW
Year: 2024

The emergence of filter bubbles leads to various harms. To mitigate filter bubbles, some recent works select the seeds for different viewpoints to minimize the formation of bubbles under the influence propagation model. Different from these works where the diffusion networks remain unchanged, in this paper, we conduct the first attempt to mitigate filter bubbles via edge insertion. Besides, to be more generalized, we focus on mitigating filter bubbles for the given target node set since the audiences can be different for different scenarios. Specifically, we propose the concept of openness score for each target node, which serves as a metric to assess the likelihood of this node being influenced by multiple viewpoints simultaneously. Given a directed graph G, two seed sets, a positive integer k and a target node set, we aim to find k edges incident to the given seeds such that the total openness score is maximized. We prove the NP-hardness of problem studied. A baseline method is first presented by extending the greedy framework. To handle large graphs efficiently, we develop a sampling-based strategy. A data-dependent approximation method is developed with theoretical guarantees. Experiments over real social networks are conducted to demonstrate the advantages of proposed techniques.
Cardinality Counting in "Alcatraz": A Privacy-aware Federated Learning Approach
Authors: Nan Wu, Xin Yuan, Shuo Wang, Hongsheng Hu, Minhui Xue
Venue: WWW
Year: 2024

The task of cardinality counting, pivotal for data analysis, endeavors to quantify unique elements within datasets and has significant applications across various sectors like healthcare, marketing, cybersecurity, and web analytics. Current methods, categorized into deterministic and probabilistic, often fail to prioritize data privacy. Given the fragmentation of datasets across various organizations, there is an elevated risk of inadvertently disclosing sensitive information during collaborative data studies using state-of-the-art cardinality counting techniques. This study introduces an innovative privacy-centric solution for the cardinality counting dilemma, leveraging a federated learning framework. Our approach involves employing a locally differentially private data encoding for initial processing, followed by a privacy-aware federated K-means clustering strategy, ensuring that cardinality counting occurs across distinct datasets without necessitating data amalgamation. The efficacy of our methodology is underscored by promising results from tests on both real-world and simulated datasets, pointing towards a transformative approach to privacy-sensitive cardinality counting in contemporary data science.
MetaSplit: Meta-Split Network for Limited-Stock Product Recommendation
Authors: Wenhao Wu, Jialiang Zhou, Ailong He, Shuguang Han, Jufeng Chen, Bo Zheng
Venue: WWW
Year: 2024

Compared to business-to-consumer (B2C) e-commerce systems, consumer-to-consumer (C2C) e-commerce platforms usually encounter the limited-stock problem, that is, a product can only be sold one time in a C2C system. This poses several unique challenges for click-through rate (CTR) prediction. Due to limited user interactions for each product (i.e. item), the corresponding item embedding in the CTR model may not easily converge. This makes the conventional sequence modeling based approaches cannot effectively utilize user history information since historical user behaviors contain a mixture of items with different volume of stocks. Particularly, the attention mechanism in a sequence model tends to assign higher score to products with more accumulated user interactions, making limited-stock products being ignored and contribute less to the final output. To this end, we propose the Meta-Split Network (MSNet) to split user history sequence regarding to the volume of stock for each product, and adopt differentiated modeling approaches for different sequences. As for the limited-stock products, a meta-learning approach is applied to address the problem of inconvergence, which is achieved by designing meta scaling and shifting networks with ID and side information. In addition, traditional approach can hardly update item embedding once the product is consumed. Thereby, we propose an auxiliary loss that makes the parameters updatable even when the product is no longer in distribution. To the best of our knowledge, this is the first solution addressing the recommendation of limited-stock product. Experimental results on the production dataset and online A/B testing demonstrate the effectiveness of our proposed method.