OpenRaiser/Intern-Atlas huggingface dataset:
Attentive Partial Convolution for RGBD Image Inpainting
Authors: Ankan Dash, Guiling Wang, Tao Han
Venue: WWW
Year: 2024

In this work, we demonstrated the use of Partial Convolutions for RGBD image inpainting. We proposed the two models L-PConv and Attn-PConv. The baseline partial convolution model is outperformed by both of our proposed models, with the Attn-PConv model performing the best. The proposed Attn-PConv model is able to infill missing pixels with relatively less training time when compared to some other GAN-based models. As far as we know this is the first time a partial convolution model has been used successfully for RGBD image inpainting. The results( Image SSIM: 0.9787, Image PSNR: 30.9665, Depth SSIM: 0.9818, Depth PSNR: 35.7311) indicate that our model is successful in RGBD image inpainting. The addition of the additional loss terms and the Attentive Normalization techniques help improve the performance of the model significantly. We believe our model can be successfully used in AR-related applications where infilling missing pixels is performed frequently especially for both RGB and Depth images together. Beyond the scope of this study, we envision practical applications for our model in augmented reality, particularly in scenarios where frequent pixel infilling is required for both RGB and Depth images. In the future our research trajectory aims to incorporate higher resolution, aligning with the capabilities of modern cameras capable of capturing images at 4k resolution.
Efficiency of the Generalized Second-Price Auction for Value Maximizers
Authors: Yuan Deng, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni, Hanrui Zhang, Song Zuo
Venue: WWW
Year: 2024

We study the price of anarchy of the generalized second-price auction where bidders are value maximizers (i.e., autobidders). We show that in general the price of anarchy can be as bad as 0. For comparison, the price of anarchy of running VCG is 1/2 in the autobidding world. We further show a fined-grained price of anarchy with respect to the discount factors (i.e., the ratios of click probabilities between lower slots and the highest slot in each auction) in the generalized second-price auction, which highlights the qualitative relation between the smoothness of the discount factors and the efficiency of the generalized second-price auction.
AN-Net: an Anti-Noise Network for Anonymous Traffic Classification
Authors: Xianwen Deng, Yijun Wang, Zhi Xue
Venue: WWW
Year: 2024

Anonymous networks employ a triple proxy to transmit packets to enhance user privacy, causing traffic packets from all applications and web services to form a unified flow. The traditional approach of applying flow-level encrypted traffic classification methods to anonymous traffic (i.e., treating consecutive packets as a single flow) is hindered by irrelevant packet noise. Moreover, fluctuations in the network environment can introduce per-packet attribute noise and discrepancies between training and test data. How to extract robust patterns from consecutive packets replete with noise remains a key challenge. In this paper, we propose the Anti-Noise Network (AN-Net) to construct robust short-term representations for a single modality, effectively countering irrelevant packet noise. We also incorporate an enhanced multi-modal fusion approach to combat per-packet attribute noise. AN-Net achieves state-of-the-art performance across two anonymous traffic classification tasks and one VPN traffic classification task, notably elevating the F1 score of SJTU-AN21 to 94.39% (6.24%↑). Our code and dataset are available on https://github.com/SJTU-dxw/AN-Net.
Large Language Model Powered Agents in the Web
Authors: Yang Deng, An Zhang, Yankai Lin, Xu Chen, Ji-Rong Wen, Tat-Seng Chua
Venue: WWW
Year: 2024

Web applications serve as vital interfaces for users to access information, perform various tasks, and engage with content. Traditional web designs have predominantly focused on user interfaces and static experiences. With the advent of large language models (LLMs), there's a paradigm shift as we integrate LLM-powered agents into these platforms. These agents bring forth crucial human capabilities like memory and planning to make them behave like humans in completing various tasks, effectively enhancing user engagement and offering tailored interactions in web applications. In this tutorial, we delve into the cutting-edge techniques of LLM-powered agents across various web applications, such as web mining, social networks, recommender systems, and conversational systems. We will also explore the prevailing challenges in seamlessly incorporating these agents and hint at prospective research avenues that can revolutionize the way we interact with web platforms.
Coordinated Activity Modulates the Behavior and Emotions of Organic Users: A Case Study on Tweets about the Gaza Conflict
Authors: Priyanka Dey, Luca Luceri, Emilio Ferrara
Venue: WWW
Year: 2024

Social media has become a crucial conduit for the swift dissemination of information during global crises. However, this also paves the way for the manipulation of narratives by malicious actors. This research delves into the interaction dynamics between coordinated (malicious) entities and organic (regular) users on Twitter amidst the Gaza conflict. Through the analysis of approximately 3.5 million tweets from over 1.3 million users, our study uncovers that coordinated users significantly impact the information landscape, successfully disseminating their content across the network: a substantial fraction of their messages is adopted and shared by organic users. Furthermore, the study documents a progressive increase in organic users' engagement with coordinated content, which is paralleled by a discernible shift towards more emotionally polarized expressions in their subsequent communications. These results highlight the critical need for vigilance and a nuanced understanding of information manipulation on social media platforms.
Textual Context guided Vision Transformer with Rotated Multi-Head Attention for Sentiment Analysis
Authors: Chhavi Dhiman, Gaurav Kumar
Venue: WWW
Year: 2024

Social media multimodal sentiment analysis has proliferated the research attention of the research community, as it opens up various paradigms for social issues such as cyberbullying, hate speech, healthcare, politics, business analysis and many more. At the same time, it is an open problem to learn the intrinsic representation of multiple modalities in order to identify correlated patterns. In the proposed work a sentiment analysis framework is presented that defines Textual Context guided Vision Transformer with Rotated Multi-Head Attention, in order to exploit correlation between image-text pair and mine rich discriminatory features for multimodal sentiment analysis. A novel Rotated-Multi-head attention mechanism is defined that translates the visual or text embeddings in distinct feature space resulting in Adaptively Rotated Refined Embedding (ARREmb). To exhibit the performance of the proposed work, extensive experiments are carried out on three publicly available datasets-BG, Twitter and MVSA-single dataset, in terms of Precision, Recall, F1-score and accuracy. The experiments support superior performance of the proposed approach by laying out comparison with SOTA followed by ablation study.
Graph Coarsening via Convolution Matching for Scalable Graph Neural Network Training
Authors: Charles Dickens, Edward W. Huang, Aishwarya Reganti, Jiong Zhu, Karthik Subbian, Danai Koutra
Venue: WWW
Year: 2024

Graph summarization as a preprocessing step is an effective and complementary technique for scalable graph neural network (GNN) training. In this work, we propose the Coarsening Via Convolution Matching (ConvMatch) algorithm and a highly scalable variant, A-ConvMatch, for creating summarized graphs that preserve the output of graph convolution. We evaluate ConvMatch on six real-world link prediction and node classification graph datasets, and show it is efficient and preserves prediction performance while significantly reducing the graph size. Notably, ConvMatch achieves up to 95% of the prediction performance of GNNs on node classification while trained on graphs summarized down to 1% the size of the original graph. Furthermore, on link prediction tasks, ConvMatch consistently outperforms all baselines, achieving up to a 2X improvement.
Archiving and Temporal Analysis of Behavioral Web Data - Tales from the Inside
Authors: Stefan Dietze
Venue: WWW
Year: 2024

Behavioral web data such as social web activity streams, query logs or behavioral traces from web search and navigation are crucial to understand the temporal evolution of the web and the human interactions that produce web data and models trained on such data. Thus, behavioral web data empowers research in various fields, such as (temporal) information retrieval, computational social science or cognitive and behavioral modeling of users over time. On the other hand, archiving and using such kind of data is associated with a number of technical and non-technical challenges, e.g. legal and ethical concerns, fast decay of data over time, as well as dependency on 3rd party gatekeepers, such as Twitter/X or Google. We present case studies from many years of research into archiving and temporal analysis of behavioral web data, including large-scale social web archives such as TweetsKB, based on an archive of 14 bn tweets harvested continuously since 2013, and web search and navigation behavior tracked through user studies, longitudinal panels or crowdsourcing-based quasi experiments.
Divide, Conquer, and Coalesce: Meta Parallel Graph Neural Network for IoT Intrusion Detection at Scale
Authors: Hua Ding, Lixing Chen, Shenghong Li, Yang Bai, Pan Zhou, Zhe Qu
Venue: WWW
Year: 2024

This paper proposes Meta Parallel Graph Neural Network (MPGNN) to establish a scalable Network Intrusion Detection System (NIDS) for large-scale Internet of Things (IoT) networks. MPGNN leverages a meta-learning framework to optimize the parallelism of GNN-based NIDS. The core of MPGNN is a coalition formation policy that generates meta-knowledge for partitioning a massive graph into multiple coalitions/subgraphs in a way that maximizes the performance and efficiency of parallel coalitional NIDSs. We propose an offline reinforcement learning algorithm, called Graph-Embedded Adversarially Trained Actor-Critic (G-ATAC), to learn a coalition formation policy that jointly optimizes intrusion detection accuracy, communication overheads, and computational complexities of coalitional NIDSs. In particular, G-ATAC learns to capture the temporal dependencies of network states and coalition formation decisions over offline data, eliminating the need for expensive online interactions with large IoT networks. Given generated coalitions, MPGNN employs E-GraphSAGE to establish coalitional NIDSs which then collaborate via ensemble prediction to accomplish intrusion detection for the entire network. We evaluate MPGNN on two real-world datasets. The experimental results demonstrate the superiority of our method with substantial improvements in F1 score, surpassing the state-of-the-art methods by 0.38 and 0.29 for the respective datasets. Compared to the centralized NIDS, MPGNN reduces the training time of NIDS by 41.63% and 22.11%, while maintaining an intrusion detection performance comparable to centralized NIDS.
Large Language Models for Graph Learning
Authors: Yujuan Ding, Wenqi Fan, Xiao Huang, Qing Li
Venue: WWW
Year: 2024

Graphs are widely applied to encode entities with various relations in web applications such as social media and recommender systems. Meanwhile, graph learning-based technologies, such as graph neural networks, are demanding to support the analysis, understanding, and usage of the data in graph structures. Recently, the boom of language foundation models, especially Large Language Models (LLMs), has advanced several main research areas in artificial intelligence, such as natural language processing, graph mining, and recommender systems. The synergy between LLMs and graph learning holds great potential to prompt the research in both areas. For example, LLMs can facilitate existing graph learning models by providing high-quality textual features for entities and edges, or enhancing the graph data with encoded knowledge and information. It may also innovate with novel problem formulations on graph-related tasks. Due to the research significance as well as the potential, the convergent area of LLMs and graph learning has attracted considerable research attention. Therefore, we propose to hold the workshop Large Language Models for Graph Learning at WWW'24, in order to provide a venue to gather researchers in academia and practitioners in the industry to present the recent progress on relevant topics and exchange their critical insights.
FashionReGen: LLM-Empowered Fashion Report Generation
Authors: Yujuan Ding, Yunshan Ma, Wenqi Fan, Yige Yao, Tat-Seng Chua, Qing Li
Venue: WWW
Year: 2024

Fashion analysis refers to the process of examining and evaluating trends, styles, and elements within the fashion industry to understand and interpret its current state, generating fashion reports. It is traditionally performed by fashion professionals based on their expertise and experience, which requires high labour cost and may also produce biased results for relying heavily on a small group of people. In this paper, to tackle the Fashion Report Generation (FashionReGen) task, we propose an intelligent Fashion Analyzing and Reporting system based the advanced Large Language Models (LLMs), debbed as GPT-FAR. Specifically, it tries to deliver FashionReGen based on effective catwalk analysis, the proposed GPT-FAR system is equipped with several key procedures, namely, catwalk understanding, collective organization and analysis, and report generation. By posing and exploring such an open-ended, complex and domain-specific task of FashionReGen, it is able to test the general capability of LLMs in fashion domain. It also inspires the explorations of more high-level tasks with industrial significance in other domains. Video illustration and more materials of GPT-FAR can be found in https://github.com/CompFashion/FashionReGen.
A Longitudinal Study of Content Control Mechanisms
Authors: Michael Dinzinger, Michael Granitzer
Venue: WWW
Year: 2024

As generative AI continues to evolve, it becomes increasingly important for site owners to effectively communicate their conditions and preferences to web agents to maintain data sovereignty. This necessity underscores the importance of an ecosystem where the technical means to prevent unauthorized data mining and to set conditions on the usage of web resources are readily available. Our research focuses on the temporal development of such technical content control methods, examining two primary mechanisms: the regulation of web robots via the Robots Exclusion Protocol and the semantic annotation of web documents with licensing information. Through a longitudinal study, we analyze the implementation and recent modifications of robots.txt files, robot directives (such as noindex, nofollow, etc.), and license-related HTML annotations. This study is driven by the growing awareness among site owners regarding the control over their content in the face of the progression of AI, highlighting the critical need for effective web content control strategies to protect and appropriately manage the wealth of texts, images, videos, and other content populating the internet.
The Impact of Cluster Centroid and Text Review Embeddings on Recommendation Methods
Authors: Peter Dolog, Ylli Sadikaj, Yllka Velaj, Andreas Stephan, Benjamin Roth, Claudia Plant
Venue: WWW
Year: 2024

Recommendation systems often neglect global patterns that can be provided by clusters of similar items or even additional information such as text. Therefore, we study the impact of integrating clustering embeddings, review embeddings, and their combinations with embeddings obtained by a recommender system. Our work assesses the performance of this approach across various state-of-the-art recommender system algorithms. Our study highlights the improvement of recommendation performance through clustering, particularly evident when combined with review embeddings, and the enhanced performance of neural methods when incorporating review embeddings.
PyGDebias: A Python Library for Debiasing in Graph Learning
Authors: Yushun Dong, Zhenyu Lei, Zaiyi Zheng, Song Wang, Jing Ma, Alex Jing Huang, Chen Chen, Jundong Li
Venue: WWW
Year: 2024

Graph-structured data is ubiquitous among a plethora of real-world applications. However, as graph learning algorithms have been increasingly deployed to help decision-making, there has been rising societal concern in the bias these algorithms may exhibit. In certain high-stake decision-making scenarios, the decisions made may be life-changing for the involved individuals. Accordingly, abundant explorations have been made to mitigate the bias for graph learning algorithms in recent years. However, there still lacks a library to collectively consolidate existing debiasing techniques and help practitioners to easily perform bias mitigation for graph learning algorithms. In this paper, we present PyGDebias, an open-source Python library for bias mitigation in graph learning algorithms. As the first comprehensive library of its kind, PyGDebias covers 13 popular debiasing methods under common fairness notions together with 26 commonly used graph datasets. In addition, PyGDebias also comes with comprehensive performance benchmarks and well-documented API designs for both researchers and practitioners. To foster convenient accessibility, PyGDebias is released under a permissive BSD-license together with performance benchmarks, API documentation, and use examples at https://github.com/yushundong/PyGDebias.
ARES: Predictable Traffic Engineering under Controller Failures in SD-WANs
Authors: Songshi Dou, Li Qi, Zehua Guo
Venue: WWW
Year: 2024

Emerging web applications (e.g., video streaming and Web of Things applications) account for a large share of traffic in Wide Area Networks (WANs) and provide traffic with various Quality of Service (QoS) requirements. Software-Defined Wide Area Networks (SD-WANs) offer a promising opportunity to enhance the performance of Traffic Engineering (TE), which aims to enable differentiable QoS for numerous web applications. Nevertheless, SD-WANs are managed by controllers, and unpredictable controller failures may undermine flexible network management. Switches previously controlled by the failed controllers may become offline, and flows traversing these offline switches lose the path programmability to route flows on available forwarding paths. Thus, these offline flows cannot be routed/rerouted on previous paths to accommodate potential traffic variations, leading to severe TE performance degradation. Existing recovery solutions reassign offline switches to other active controllers to recover the degraded path programmability but fail to promise good TE performance since higher path programmability does not necessarily guarantee satisfactory TE performance. In this paper, we propose ARES to provide predictable TE performance under controller failures. We formulate an optimization problem to maintain predictable TE performance by jointly considering fine-grained flow-controller reassignment using P4 Runtime and flow rerouting and propose ARES to efficiently solve this problem. Extensive simulation results demonstrate that our problem formulation exhibits comparable load balancing performance to optimal TE solution without controller failures, and the proposed ARES significantly improves average load balancing performance by up to 43.36% with low computation time compared with existing solutions.
Content Moderation on Social Media in the EU: Insights From the DSA Transparency Database
Authors: Chiara Patricia Drolsbach, Nicolas Pröllochs
Venue: WWW
Year: 2024

The Digital Services Act (DSA) represents a major legislative framework that mandates large social media providers to file Statements of Reasons (SoRs) to the DSA Transparency Database whenever they remove or restrict access to certain content on their platforms in the EU. In this work, we empirically analyze this unique data source and provide an early look at content moderation decisions of social media platforms in the EU. Our empirical analysis based on more than 156 million SoRs reveals significant differences in content moderation practices and how large social media platforms implement their obligations under the DSA. Our findings have important implications for regulators, suggesting the need to lay out more specific rules that ensure common standards on how social media providers handle rule-breaking content on their platforms.
LightCS: Selecting Quadratic Feature Crosses in Linear Complexity
Authors: Zhaocheng Du, Junhao Chen, Qinglin Jia, Chuhan Wu, Jieming Zhu, Zhenhua Dong, Ruiming Tang
Venue: WWW
Year: 2024

Feature crosses, which represent joint features synthesized by two single features, are critical for deep recommender systems to model sophisticated feature relations. In practice, only a tiny fraction of feature crosses among massive possible ones are informative, while introducing irrelevant or noisy ones may increase online service latency and boost the risk of overfitting. Therefore, picking high-quality feature crosses is essential in practical recommender systems. However, even for selecting quadratic feature crosses, existing algorithms still incur either o(n2) time complexity or o(n2) space complexity, which is inefficient and unscalable in industrial scenarios. In this paper, we present an efficient and accurate quadratic feature cross selection method with both linear time and space complexity. Motivated by the idea of Quasi-Newton methods, we propose to use 2nd-order derivative matrix to evaluate all theoretically possible feature crosses concurrently without the need of constructing them explicitly, where an approximation of 2nd-order gradient is applied to guarantee both low time and space complexity. Furthermore, we decouple the feature crosses' novelty from single features' joint importance. Experiments on two public recommendation datasets and a private dataset validate the efficiency and effectiveness of our method, and it has also become a fundamental feature cross selection tool used by Huawei Ads Platform.
Exploiting Associations among Multi-Aspect Node Properties in Heterogeneous Graphs for Link Prediction
Authors: Chenguang Du, Hao Geng, Deqing Wang, Fuzhen Zhuang, Zhiqiang Zhang, Lanshan Zhang
Venue: WWW
Year: 2024

Recent years have witnessed the abundant emergence of heterogeneous graph neural networks (HGNNs) for link prediction. In heterogeneous graphs, different meta-paths connected to nodes reflect different aspects of the nodes' properties. Existing work fuses the multi-aspect properties of each node into a single vector representation, which makes them fail to capture fine-grained associations between multiple node properties. To this end, we propose a heterogeneous graph neural network with Multi-Aspect Node Association awareness, namely MANA. MANA leverages key associations among multi-aspect node properties to achieve link prediction. Specifically, to avoid the loss of effective association information for link prediction, we design a transformer-based Multi-Aspect Association Mining module to capture multi-aspect associations between nodes. Then, we introduce the Multi-Aspect Link Prediction module, empowering MANA to focus on the key associations among all, thus avoiding the negative impact of ineffective associations on the model's performance. We conduct extensive experiments on three widely used datasets from Heterogeneous Graph Benchmark (HGB). Experimental results show that our proposed method outperforms state-of-the-art baselines.