OpenRaiser/Intern-Atlas huggingface dataset:
Hierarchical Knowledge Graph Learning Enabled Socioeconomic Indicator Prediction in Location-Based Social Network
Authors: Zhilun Zhou, Yu Liu, Jingtao Ding, Depeng Jin, Yong Li
Venue: WWW
Year: 2023

Socioeconomic indicators reflect location status from various aspects such as demographics, economy, crime and land usage, which play an important role in the understanding of location-based social networks (LBSNs). Especially, several existing works leverage multi-source data for socioeconomic indicator prediction in LBSNs, which however fail to capture semantic information as well as distil comprehensive knowledge therein. On the other hand, knowledge graph (KG), which distils semantic knowledge from multi-source data, has been popular in recent LBSN research, which inspires us to introduce KG for socioeconomic indicator prediction in LBSNs. Specifically, we first construct a location-based KG (LBKG) to integrate various kinds of knowledge from heterogeneous LBSN data, including locations and other related elements like point of interests (POIs), business areas as well as various relationships between them, such as spatial proximity and functional similarity. Then we propose a hierarchical KG learning model to capture both global knowledge from LBKG and domain knowledge from several sub-KGs. Extensive experiments on three datasets demonstrate our model’s superiority over state-of-the-art methods in socioeconomic indicators prediction. Our code is released at: https://github.com/tsinghua-fib-lab/KG-socioeconomic-indicator-prediction.
Weighted Statistically Significant Pattern Mining
Authors: Tingfu Zhou, Zhenlian Qi, Wensheng Gan, Shicheng Wan, Guoting Chen
Venue: WWW
Year: 2023

Pattern discovery (aka pattern mining) is a fundamental task in the field of data science. Statistically significant pattern mining (SSPM) is the task of finding useful patterns that statistically occur more often from databases for one class than for another. The existing SSPM task does not consider the weight of each item. While in the real world, the significant level of different items/objects is various. Therefore, in this paper, we introduce the Weighted Statistically Significant Patterns Mining (WSSPM) problem and propose a novel WSSpm algorithm to successfully solve it. We present a new framework that effectively mines weighted statistically significant patterns by combining the weighted upper-bound model and the multiple hypotheses test. We also propose a new weighted support threshold that can satisfy the demand of WSSPM and prove its correctness and completeness. Besides, our weighted support threshold and modified weighted upper-bound can effectively shrink the mining range. Finally, experimental results on several real datasets show that the WSSpm algorithm performs well in terms of execution time and memory storage.
DGBCT: A Scalable Distributed Gradient Boosting Causal Tree at Alipay
Authors: Jun Zhou, Caizhi Tang, Qing Cui, Yi Ding, Longfei Li, Fei Wu
Venue: WWW
Year: 2023

Causal effect estimation has been increasingly emphasized in the past few years. To handle this problem, tree-based causal methods have been widely used due to their robustness and explainability. However, most of the existing methods are limited to running on a single machine, making it difficult to scale up to hundreds of millions of data in typical industrial scenarios. This paper proposes DGBCT, a Distributed Gradient Boosting Causal Tree to tackle such problem, and the contribution of this paper is three folds. First, we extend the original GBCT method to a multi-treatment setting and take the monotonic constraints into consideration, so that more typical industrial necessities can be resolved with our framework. Moreover, we implement DGBCT based on the ‘Controller-Coordinator-Worker’ framework, in which dual failover mechanism is achieved, and commendable flexibility is ensured. In addition, empirical results show that DGBCT significantly outperforms the state-of-the-art causal trees, and has a near-linear speedup as the number of workers grows. The system is currently deployed in Alipay1 to support the daily business tasks that involve hundreds of millions of users.
Opinion Maximization in Social Networks via Leader Selection
Authors: Xiaotian Zhou, Zhongzhi Zhang
Venue: WWW
Year: 2023

We study a leader selection problem for the DeGroot model of opinion dynamics in a social network with n nodes and m edges, in the presence of s0 = O(1) leaders with opinion 0. Concretely, we consider the problem of maximizing the average opinion in equilibrium by selecting k = O(1) leaders with opinion 1 from the remaining n − s0 nodes, which was previously proved to be NP-hard. A deterministic greedy algorithm was also proposed to approximately solve the problem, which has an approximation factor (1 − 1/e) and time complexity O(n3), and thus does not apply to large networks. In this paper, we first give an interpretation for the opinion of each node in equilibrium and the disagreement of the model from the perspective of resistor networks. We then develop a fast randomized greedy algorithm to solve the problem. To this end, we express the average opinion in terms of the pseudoinverse and Schur complement of Laplacian matrix for . The key ingredients of our randomized algorithm are Laplacian solvers and node sparsifiers, where the latter can preserve pairwise effective resistance by viewing Schur complement as random walks with average length l. For any error parameter ϵ > 0, at each iteration, the randomized algorithm selects a node that deviates from the local optimum marginal gain at most ϵ. The time complexity of the fast algorithm is O(mkllog nϵ− 2). Extensive experiments on various real networks show that the effectiveness of our randomized algorithm is similar to that of the deterministic algorithm, both of which are better than several baseline algorithms, and that our randomized algorithm is more efficient and scalable to large graphs with more than one million nodes.
Stability and Efficiency of Personalised Cultural Markets
Authors: Haiqing Zhu, Yun Kuen Cheung, Lexing Xie
Venue: WWW
Year: 2023

This work is concerned with the dynamics of online cultural markets, namely, attention allocation of many users on a set of digital goods with infinite supply. Such dynamics are important in shaping processes and outcomes in society, from trending items in entertainment, collective knowledge creation, to election outcomes. The outcomes of online cultural markets are susceptible to intricate social influence dynamics, particularly so when the community comprises consumers with heterogeneous interests. This has made formal analysis of these markets improbable. In this paper, we remedy this by establishing robust connections between influence dynamics and optimization processes, in trial-offer markets where the consumer preferences are modelled by multinomial logit. Among other results, we show that the proportional-response-esque influence dynamic is equivalent to stochastic mirror descent on a convex objective function, thus leading to a stable and predictable outcome. When all consumers are homogeneous, the objective function has a natural interpretation as a weighted sum of efficiency and diversity of the culture market. In simulations driven by real-world preferences collected from a large-scale recommender system, we observe that ranking strategies aligned with the underlying heterogeneous preferences are more stable, and achieves higher efficiency and diversity. More broadly, we see this work as the first step in connecting computational methods for classical markets to the problem area of online attention and recommender systems. We hope this result paves the way to posing and answering a diverse set of research questions in this area.
LINet: A Location and Intention-Aware Neural Network for Hotel Group Recommendation
Authors: Ruitao Zhu, Detao Lv, Yao Yu, Ruihao Zhu, Zhenzhe Zheng, Ke Bu, Quan Lu, Fan Wu
Venue: WWW
Year: 2023

Motivated by the collaboration with Fliggy1, a leading Online Travel Platform (OTP), we investigate an important but less explored research topic about optimizing the quality of hotel supply, namely selecting potential profitable hotels in advance to build up adequate room inventory. We formulate a WWW problem, i.e., within a specific time period (When) and potential travel area (Where), which hotels should be recommended to a certain group of users with similar travel intentions (Why). We identify three critical challenges in solving the WWW problem: user groups generation, travel data sparsity and utilization of hotel recommendation information (e.g., period, location and intention). To this end, we propose LINet, a Location and Intention-aware neural Network for hotel group recommendation. Specifically, LINet first identifies user travel intentions for user groups generalization, and then characterizes the group preferences by jointly considering historical user-hotel interaction and spatio-temporal features of hotels. For data sparsity, we develop a graph neural network, which employs long-term data, and further design an auxiliary loss function of location that efficiently exploits data within the same and across different locations. Both offline and online experiments demonstrate the effectiveness of LINet when compared with state-of-the-art methods. LINet has been successfully deployed on Fliggy to retrieve high quality hotels for business development, serving hundreds of hotel operation scenarios and thousands of hotel operators.
Self-supervised Pre-training and Semi-supervised Learning for Extractive Dialog Summarization
Authors: Yingying Zhuang, Jiecheng Song, Narayanan Sadagopan, Anurag Beniwal
Venue: WWW
Year: 2023

Language model pre-training has led to state-of-the-art performance in text summarization. While a variety of pre-trained transformer models are available nowadays, they are mostly trained on documents. In this study we introduce self-supervised pre-training to enhance the BERT model’s semantic and structural understanding of dialog texts from social media. We also propose a semi-supervised teacher-student learning framework to address the common issue of limited available labels in summarization datasets. We empirically evaluate our approach on extractive summarization task with the TWEETSUMM corpus, a recently introduced dialog summarization dataset from Twitter customer care conversations and demonstrate that our self-supervised pre-training and semi-supervised teacher-student learning are both beneficial in comparison to other pre-trained models. Additionally, we compare pre-training and teacher-student learning in various low data-resource settings, and find that pre-training outperforms teacher-student learning and the differences between the two are more significant when the available labels are scarce.
ContrastFaux: Sparse Semi-supervised Fauxtography Detection on the Web using Multi-view Contrastive Learning
Authors: Ruohan Zong, Yang Zhang, Lanyu Shang, Dong Wang
Venue: WWW
Year: 2023

The widespread misinformation on the Web has raised many concerns with serious societal consequences. In this paper, we study a critical type of online misinformation, namely fauxtography, where the image and associated text of a social media post jointly convey a questionable or false sense. In particular, we focus on a sparse semi-supervised fauxtography detection problem, which aims to accurately identify fauxtography by only using the sparsely annotated ground truth labels of social media posts. Our problem is motivated by the key limitation of current fauxtography detection approaches that often require a large amount of expensive and inefficient manual annotations to train an effective fauxtography detection model. We identify two key technical challenges in solving the problem: 1) it is non-trivial to train an accurate detection model given the sparse fauxtography annotations, and 2) it is difficult to extract the heterogeneous and complicated fauxtography features from the multi-modal social media posts for accurate fauxtography detection. To address the above challenges, we propose ContrastFaux, a multi-view contrastive learning framework that jointly explores the sparse fauxtography annotations and the cross-modal fauxtography feature similarity between the image and text in multi-modal posts to accurately detect fauxtography on social media. Evaluation results on two social media datasets demonstrate that ContrastFaux consistently outperforms state-of-the-art deep learning and semi-supervised learning fauxtography detection baselines by achieving the highest fauxtography detection accuracy.
Set in Stone: Analysis of an Immutable Web3 Social Media Platform
Authors: Wenrui Zuo, Aravindh Raman, Raul J. Mondragón, Gareth Tyson
Venue: WWW
Year: 2023

There has been growing interest in the so-called “Web3” movement. This loosely refers to a mix of decentralized technologies, often underpinned by blockchain technologies. Among these, Web3 social media platforms have begun to emerge. These store all social interaction data (e.g., posts) on a public ledger, removing the need for centralized data ownership and management. But this comes at a cost, which some argue is prohibitively expensive. As an exemplar within this growing ecosytem, we explore memo.cash, a microblogging service built on the Bitcoin Cash (BCH) blockchain. We gather data for 24K users, 317K posts, 2.57M user actions, which have facilitated $6.75M worth of transactions. A particularly unique feature is that users must pay BCH tokens for each interaction (e.g., posting, following). We study how this may impact the social makeup of the platform. We therefore study memo.cash as both a social network and a transaction platform.
Budget-Constrained Auctions with Unassured Priors: Strategic Equivalence and Structural Properties
Authors: Zhaohua Chen, Mingwei Yang, Chang Wang, Jicheng Li, Zheng Cai, Yukun Ren, Zhihua Zhu, Xiaotie Deng
Venue: WWW
Year: 2024

In today's online advertising markets, it is common for advertisers to set long-term budgets. Correspondingly, advertising platforms adopt budget control methods to ensure that advertisers' payments lie within their budgets. Most budget control methods rely on the value distributions of advertisers. However, due to the complex advertising landscape and potential privacy concerns, the platform hardly learns advertisers' true priors. Thus, it is crucial to understand how budget control auction mechanisms perform under unassured priors. This work answers this problem from multiple aspects. Specifically, we examine five budget-constrained parameterized mechanisms: bid-discount/pacing first-price/second-price auctions and the Bayesian revenue-optimal auction. We consider the unassured prior game among the seller and all buyers induced by these five mechanisms in the stochastic model. We restrict the parameterized mechanisms to satisfy the budget-extracting condition, which maximizes the seller's revenue by extracting buyers' budgets as effectively as possible. Our main result shows that the Bayesian revenue-optimal mechanism and the budget-extracting bid-discount first-price mechanism yield the same set of Nash equilibrium outcomes in the unassured prior game. This implies that simple mechanisms can be as robust as the optimal mechanism under unassured priors in the budget-constrained setting. In the symmetric case, we further show that all these five (budget-extracting) mechanisms share the same set of possible outcomes. We further dig into the structural properties of these mechanisms. We characterize sufficient and necessary conditions on the budget-extracting parameter tuple for bid-discount/pacing first-price auctions. Meanwhile, when buyers do not take strategic behaviors, we exploit the dominance relationships of these mechanisms by revealing their intrinsic structures. In summary, our results establish vast connections among budget-constrained auctions with unassured priors and explore their structural properties, particularly highlighting the advantages of first-price mechanisms.
Accelerating the Decentralized Federated Learning via Manipulating Edges
Authors: Mingyang Zhou, Gang Liu, Kezhong Lu, Rui Mao, Hao Liao
Venue: WWW
Year: 2024

Federated learning enables collaborative AI training across organizations without compromising data privacy. Decentralized federated learning (DFL) improves this by offering enhanced reliability and security through peer-to-peer (P2P) model sharing. However, DFL faces challenges in terms of slow convergence rate due to complex P2P graphs. To address this issue, we propose an efficient algorithm to accelerate DFL by introducing a limited number of k of edges into the P2P graphs. Specifically, we establish a connection between the convergence rate and the second smallest eigenvalue of the laplacian matrix of the P2P graph. We prove that finding the optimal set of edges to maximize this eigenvalue is an NP-complete problem. Our quantitative analysis shows the positive effect of strategic edge additions on improving this eigenvalue. Based on the analysis, we then propose an efficient algorithm to compute the best set of candidate edges to maximize the second smallest eigenvalue, and consequently the convergence rate is maximized. Our algorithm has a low time complexity of O(krn^2). Experimental results on diverse datasets validate the effectiveness of our proposed algorithms in accelerating DFL convergence.
Automating Website Registration for Studying GDPR Compliance
Authors: Karel Kubicek, Jakob Merane, Ahmed Bouhoula, David A. Basin
Venue: WWW
Year: 2024

Investigating how websites use sensitive user data is an active research area. However, research based on automated measurements has been limited to those websites that do not require user authentication. To overcome this limitation, we developed a crawler that automates website registrations and newsletter subscriptions and detects both security and privacy threats at scale. We demonstrate our crawler's capabilities by running it on 660k websites. We use this to identify security and privacy threats and to contextualize them within EU laws, namely the General Data Protection Regulation and ePrivacy Directive. Our methods detect private data collection over insecure HTTP connections and websites sending emails with user-provided passwords. We are also the first to apply machine learning to web forms, assessing violations of marketing consent collection requirements. Overall, we find that 37.2% of websites send marketing emails without proper user consent. This is mostly caused by websites failing both to verify and store consent adequately. Additionally, 1.8% of websites share users' email addresses with third parties without a transparent disclosure.
Large Language Models for Graphs: Progresses and Directions
Authors: Chao Huang, Xubin Ren, Jiabin Tang, Dawei Yin, Nitesh V. Chawla
Venue: WWW
Year: 2024

Graph neural networks (GNNs) have emerged as fundamental methods for handling structured graph data in various domains, including citation networks, molecule prediction, and recommender systems. They enable the learning of informative node or graph representations, which are crucial for tasks such as link prediction and node classification in the context of graphs. To achieve high-quality graph representation learning, certain essential factors come into play: clean labels, accurate graph structures, and sufficient initial node features. However, real-world graph data often suffer from noise and sparse labels, while different datasets have unique feature constructions. These factors significantly impact the generalization capabilities of graph neural networks, particularly when faced with unseen tasks. Recently, due to the efficent text processing and task generalization capability of large language models (LLMs), there has been a promising approach to address the challenges mentioned above by combining large language models with graph data. This tutorial offers an overview of incorporating large language models into the graph domain, accompanied by practical examples. The methods are categorized into three dimensions: utilizing LLMs as augmenters, predictors, and agents for graph learning tasks. We will delve into the current progress and future directions within this field. By introducing this emerging topic, our aim is to enhance the audience's understanding of LLM-based graph learning techniques, foster idea exchange, and encourage discussions that drive continuous advancements in this domain.
A Demonstration of Decentralized Search Over Solid Personal Online Datastores
Authors: Mohamed Ragab, Yury Savateev, Helen Oliver, Reza Moosaei, Thanassis Tiropanis, Alexandra Poulovassilis, Adriane Chapman, George Roussos
Venue: WWW
Year: 2024

In the modern Web landscape, data privacy and control are increasingly unattainable for users. Solid 1, a decentralized Web ecosystem, restores individual privacy and control by separating data from applications, allowing integration across applications while enabling users to control access. The growth in Solid's decentralized pods, and the escalating amounts of data stored in them, necessitate a decentralized search mechanism to query data within personal datastores, while respecting varying access constraints. This demo paper presents our decentralized search system (ESPRESSO) for querying RDF and non-RDF data in Solid datastores, tackling challenges like query propagation, data indexing, privacy, and results aggregation.
Unlocking the Potential of Health Data with Decentralised Search in Personal Health Datastores
Authors: Mohamed Ragab, Yury Savateev, Helen Oliver, Thanassis Tiropanis, Alexandra Poulovassilis, Adriane Chapman, George Roussos
Venue: WWW
Year: 2024

In the digital age, where health data and digital lives converge, data privacy and control are crucial. The advent of AI and Large Language Models (LLMs) brings advanced data analysis and healthcare predictions, but also privacy concerns. The ESPRESSO project 1 asserts that for AI to be trustworthy and effective in healthcare, it must prioritize user control over corporate interests. The shift towards decentralized personal online datastores (pods) and Solid 2 principles represents a new era of private, controllable Web interactions, balancing AI data protection and machine intelligence. This balance is particularly important for applications involving health data. However, decentralization poses challenges, particularly in secure, efficient data search and data retrieval, that need to be addressed first. We argue that a decentralized search system that provides a large-scale search across Solid pods, while considering data owners' control of their data and users' different access rights, is crucial for this new paradigm. In this paper, we describe how our current decentralized search system's prototype (ESPRESSO) helps to query structured and unstructured personal health data in Solid servers. The paper also describes a search scenario that shows how ESPRESSO can search health data combined with fitness personal data stored in different personal datastores
The 1st Workshop on Decentralised Search and Recommendation
Authors: Mohamed Ragab, Yury Savateev, Wenjie Wang, Reza Moosaei, Thanassis Tiropanis, Alexandra Poulovassilis, Adriane Chapman, Helen Oliver, George Roussos
Venue: WWW
Year: 2024

The DESERE Workshop, our First Workshop on Decentralised Search and Recommendation, offers a platform for researchers to explore and share innovative ideas on decentralised web services, mainly focusing on three major topics: (i) societal impact of decentralised systems: their effects on privacy, policy, and regulation; (ii) decen- tralising applications: algorithmic and performance challenges that arise from decentralisation; and (iii) infrastructure to support de- centralised systems and services: peer-to-peer networks, routing, and performance evaluation tools.