OpenRaiser/Intern-Atlas huggingface dataset:
Health CLIP: Depression Rate Prediction Using Health Related Features in Satellite and Street View Images
Authors: Tianjian Ouyang, Xin Zhang, Zhenyu Han, Yu Shang, Yong Li
Venue: WWW
Year: 2024

Mental health is a state of mental well-being that enables people to cope with the stresses of life, realize their abilities, learn well and work well, and contribute to their community. It has intrinsic and instrumental value and is integral to our well-being, and its correlation with environmental factors has been a subject of growing interest. As the pressure of society keeps growing, depression has become a severe problem in modern cities, and finding a way to estimate depression rate is of significance to relieve the problem. In this study, we introduce a Contrastive Language-Image Pretraining (CLIP) based novel approach to predict mental health indicators, especially depression rate, through satellite and street view images. Our methodology uses state-of-the-art Multimodal Large Language Model (MLLM), GPT4-vision, to generate health related captions for satellite and street view images, then we use the generated image-text pairs to fine-tune the CLIP model, making its image encoder extract health related features such as green spaces, sports fields, and infrastructral characteristics. The fine-tuning process is employed to bridge the semantic gap between textual descriptions and visual representations, enabling a comprehensive analysis of geo-tagged images. Consequently, our methodology achieves a notable R2 value of 0.565 on prediction of depression rate in New York City with the combination of satellite and street view images. The successful deployment of Health CLIP in a real-world scenario underscores the practical applicability of our approach.
(In)Security of File Uploads in Node.js
Authors: Harun Oz, Abbas Acar, Ahmet Aris, Güliz Seray Tuncay, Amin Kharraz, A. Selcuk Uluagac
Venue: WWW
Year: 2024

File upload is a critical feature incorporated by a myriad of web applications in an effort to enable users to share and manage their files conveniently. It has been used in many useful services such as file-sharing and social media. While file upload is an essential component of web applications, the lack of rigorous checks on the file name, type, and content of the uploaded files can result in security issues, often referred to as Unrestricted File Upload (UFU). In this study, we analyze the (in)security of popular file upload libraries and real-world applications in the Node.js ecosystem. To automate our analysis, we propose and implement NodeSEC- a tool designed to analyze file upload insecurities in Node.js applications and libraries. NodeSEC generates unique payloads and thoroughly evaluates the application's file upload security against 13 distinct UFU-type attacks. Utilizing NodeSEC, we analyze the most popular file upload libraries and real-world applications in the Node.js ecosystem. Our analysis results reveal that some real-world web applications are vulnerable to UFU attacks and disclose serious security bugs in file upload libraries. As of this writing, we received 19 CVEs and two US-CERT cases for the security issues that we reported. Our findings provide strong evidence that dynamic features of Node.js applications introduce security shortcomings and that web developers should be cautious when implementing file upload features in their applications. Finally, combining our responsible disclosure experience and root cause analysis, we identified the main causes of significant security weaknesses in file uploads in Node.js.
FASETS: Discovering Faceted Sets of Entities
Authors: Koninika Pal, Hiba Arnaout, Simon Razniewski, Gerhard Weikum
Venue: WWW
Year: 2024

Computing related entities for a given seed entity is an important task in exploratory search and comparative data analysis.Prior works, using the seed-based set expansion paradigm, have focused on the single aspect of identifying homogeneous sets with high pairwise relatedness. A few recent works discuss cluster-based approaches to tackle multi-faceted set expansion, however, they fail in harnessing the specificity of the clusters and generating an explanation for them. This paper poses the multi-faceted set expansion as an optimization problem, where the goal is to compute multiple groups of entities that convey different aspects in an explainable manner, with high similarity within each group and diversity across groups. To extend a seed entity, we collect a large pool of candidate entities and facets (e.g., categories)from Wikipedia and knowledge bases, and construct a candidate graph. We propose FASETS, an efficient algorithm for computing faceted groups of bounded size, based on random walks over the candidate graph. Our extensive evaluation shows the superiority of FASETS against prior baselines, with regard to ground-truth collected from crowdsourcing.
Predictive Relevance Uncertainty for Recommendation Systems
Authors: Charul Paliwal, Anirban Majumder, Sivaramakrishnan Kaveri
Venue: WWW
Year: 2024

Click-through Rate (CTR) module is the foundation block of recommendation system and used for search, content selection, advertising, video streaming etc. CTR is modelled as a classification problem and extensive research is done to improve the CTR models. However, uncertainty method for these models are still an unexplored area. In this work we analyse popular uncertainty methods in the context of recommendation system. We found that popular uncertainty models fails to capture the predictive uncertainty of the CTR model that exist unique to the recommendation models and is not prevalent in the traditional classification models. We empirical show why a different uncertainty measure is required for the recommendation system CTR prediction models. We propose PRU (Predictive Relevance Uncertainty), a single forward pass uncertainty approach for a sample as a distance from the predictive relevance samples of the training data. We show the efficacy of the proposed predictive relevance uncertainty (PRU) on selective prediction. Further, we demonstrate the utility of the proposed framework on the downstream task of OOD detection and active learning while maintaining the latency of a single pass deterministic model.
A Symbolic Rule Integration Framework with Logic Transformer for Inductive Relation Prediction
Authors: Yudai Pan, Jun Liu, Tianzhe Zhao, Lingling Zhang, Yun Lin, Jin Song Dong
Venue: WWW
Year: 2024

Relation prediction in knowledge graphs (KGs) aims at predicting missing relations in incomplete triples, whereas the dominant paradigm by KG embeddings has a limitation to predict the relation between unseen entities. This situation is called an inductive setting, which is more common in the real-world scenario. To handle this issue, implicit symbolic rules have shown great potential in capturing the inductive capability. However, it is still challenging to obtain precise representations of logic rules from KGs. The argument variability and predicate non-commutativity in symbolic rule integration make the modeling of component symbols difficult. To this end, we propose a novel inductive relation prediction model named SymRITa with a logic transformer integrating rules. SymRITa firstly extracts the subgraph, whose embeddings are captured by a graph network. Meanwhile, symbolic rule graphs in the subgraph can be generated. Then, the symbolic rules are modeled by a proposed logic transformer. Specifically, the input format based on the subgraph-based embeddings is to focus on the argument variability in symbolic rules. In addition, a conjunction attention mechanism in the logic transformer can resolve predicate non-commutativity in the symbolic rule integration process. Finally, the subgraph-based and symbol-based embeddings obtained from the previous steps are combined for the training regime, and prediction results as well as rules explaining the reasoning process are explicitly output. Extensive experiments on twelve inductive datasets show that SymRITa achieves outstanding effectiveness compared to state-of-the-art inductive baselines. Moreover, the logic rules with corresponding confidences provide an interpretable paradigm.
A User-State Based Interest Transfer Network for Cross-Domain Recommendation
Authors: Pingjun Pan, Jialu Wang, Tingting Zhou, Wenyu Yang, Hongxiang Chen
Venue: WWW
Year: 2024

Cross-domain recommendation (CDR) has emerged as a promising approach to improve click-through rate (CTR) in the target domain by effectively transferring user interests from the source domain. However, existing methods either use a uniform interest transfer function or focus on user-level personalized transfer functions, neglecting the fact that the transition of user states in the target domain also influence the interests in the source domain. To address this issue, we present User-State based Interest Transfer network (USIT), a novel method that takes into account the user state evolution. USIT contains two main components: a User-State Transition module (UST) and a State-Level Interests Transfer module (SLIT). UST models the evolution of user states by predicting the next state in the target domain. As the user's state evolves, SLIT adaptively weights the interests by interest-level mask attention in the source domain. Extensive offline experiments and online A/B tests demonstrate that our proposed USIT method significantly outperforms current state-of-the-art models in CDR scenarios. Currently, we have deployed it on NetEase Cloud Music, affecting millions of users.
A Similarity-based Approach for Efficient Large Quasi-clique Detection
Authors: Jiayang Pang, Chenhao Ma, Yixiang Fang
Venue: WWW
Year: 2024

Identifying dense subgraphs called quasi-cliques is pivotal in various graph mining tasks across domains like biology, social networks, and e-commerce. However, recent algorithms still suffer from efficiency issues when mining large quasi-cliques in massive and complex graphs. Our key insight is that vertices within a quasi-clique exhibit similar neighborhoods to some extent. Based on this, we introduce NBSim and FastNBSim, efficient algorithms that find near-maximum quasi-cliques by exploiting vertex neighborhood similarity. FastNBSim further uses MinHash approximations to reduce the time complexity for similarity computation. Empirical evaluation on 10 real-world graphs shows that our algorithms deliver up to three orders of magnitude speedup versus the state-of-the-art algorithms, while ensuring high-quality quasi-clique extraction.
Ad Laundering: How Websites Deceive Advertisers into Rendering Ads Next to Illicit Content
Authors: Emmanouil Papadogiannakis, Panagiotis Papadopoulos, Evangelos P. Markatos, Nicolas Kourtellis
Venue: WWW
Year: 2024

Providing online content monetized via ads to users is a lucrative business. But what if the content is pirated or illicit, thus harming the brand safety of the advertiser? In this paper, we are the first to investigate Ad Laundering: a technique with which bad actors deceive advertisers by hiding illicit content within evidently lawful websites to monetize the generated traffic. We develop a client-side detection methodology to detect and analyze websites performing ad laundering. We describe in detail the techniques these websites use to cloak content, and provide estimations for the ad revenues they are able to collect on a monthly basis. Finally, we attribute the generated revenue to different traffic channels and establish that even popular brands have their ads rendered next to undesirable content.
Hyperbolic Heterogeneous Graph Attention Networks
Authors: Jongmin Park, Seunghoon Han, Soohwan Jeong, Sungsu Lim
Venue: WWW
Year: 2024

Most previous heterogeneous graph embedding models represent elements in a heterogeneous graph as vector representations in a low-dimensional Euclidean space. However, because heterogeneous graphs inherently possess complex structures, such as hierarchical or power-law structures, distortions can occur when representing them in Euclidean space. To overcome this limitation, we propose Hyperbolic Heterogeneous Graph Attention Networks (HHGAT) that learn vector representations in hyperbolic spaces with metapath instances. We conducted experiments on three real-world heterogeneous graph datasets, demonstrating that HHGAT outperforms state-of-the-art heterogeneous graph embedding models in node classification and clustering tasks. This superior performance is attributed to HHGAT's ability to capture the complex structure of heterogeneous graphs effectively.
RealGraphGPU++: A High-Performance GPU-Based Graph Engine with Direct Storage-to-DM IO
Authors: Jeong-Min Park, Myung-Hwan Jang, Duck-Ho Bae, Sang-Wook Kim
Venue: WWW
Year: 2024

Recently, with the increasing size of real-world networks, graph engines have been studied extensively for efficient graph analysis. As one of the state-of-the-art single-machine-based graph engines, \textRealGraph ^\textGPU processes large-scale graphs very efficiently thanks to its well-designed architecture and the strong parallel-computing power of GPU. Via a preliminary analysis, we first observe \textRealGraph ^\textGPU has a good chance for more performance improvement in IOs between storage and GPU's device memory. This motivates us to present \textRealGraph ^\textGPU++ , a solution that substantially reduces IO time by establishing adirect data path between storage and device memory. Additionally, it employsasynchronous processing of CPU and GPU tasks to issue IO requests more frequently, thereby improving overall performance by achieving higher IO bandwidth. Experimental results on real-world datasets show that \textRealGraph ^\textGPU++ outperforms dramatically existing 11 state-of-the-art graph engines including \textRealGraph ^\textGPU .
RealGraphGPUWeb: A Convenient and Efficient GPU-Based Graph Analysis Platform on the Web
Authors: Jeong-Min Park, Myung-Hwan Jang, Sang-Wook Kim
Venue: WWW
Year: 2024

In this demo paper, we present RealGraphGPUWeb a web-based graph analysis platform with the following features: (1) easy to use user-friendly GUI, (2) high processing performance, (3) various graph algorithms and data formats supported, (4) high accessibility anywhere on the web, and (5) no coding requirements. In our demo, we show how a naive user (e.g., non-CS researcher) gets graph analysis results conveniently and efficiently with a few clicks through the web-based GUI inRealGraphGPUWeb. We also make the user feel the effect of performance improvement obtained by our optimization strategies employed in RealGraphGPUWeb. We believe that RealGraphGPUWeb could be a good platform not only for CS users but also for non-CS users who want to analyze big graphs for their applications easily and efficiently.
AI Deepfakes on the Web: The 'Wicked' Challenges for AI Ethics, Law and Technology
Authors: Jeannie Marie Paterson
Venue: WWW
Year: 2024

Advances in generative AI and the increasingly easy availability of tools for creating text, code, audio, and images have impacted almost all industry sectors, promising new efficiencies and changing work patterns. The darker side of this same technology is the problematic case of deepfakes created by AI and spread online to humiliate, manipulate, trick, or defraud ordinary individuals and public figures. Transparency, fairness, and beneficence are vital values of responsible and ethical AI. All of these values would preclude harmful uses of AI deep fakes. However, harmful deepfakes are usually the work of fraudsters with little regard for ethics and beyond the reach of the law. So, who should be responsible? Arguably, principles of responsible AI require tech companies and digital platforms to take responsibility for reducing harmful uses of deepfakes. These entities are gatekeepers to the creation and distribution of deepfakes. Therefore, they are ethically obligated to respond to the foreseeable consequential harms arising from generative AI. Increasingly, this is the response of lawmakers. Gatekeeper responsibility envisages that tech producers and platforms will proactively invest in technical solutions to harmful deepfakes, such as watermarking, finetuning, red teaming or automated content moderation, and proactive take-down responses. This response is compelling and might seem straightforward. As always, the details are more complex. The efficiency of the proposed technical responses is still emerging. They raise as yet unaddressed implications for smaller providers and the relations between tech companies and digital platforms. Moreover, even beginning to respond to online deepfakes requires social policy decisions that assess and weigh incommensurable considerations, including retaining trust on the Web, keeping vulnerable groups safe, preserving free speech and creativity, and not stifling the development of potentially beneficial technology. This presentation addresses these problematic choices in responding to the 'wicked' challenge of AI deepfakes on the Web. It proposes a networked response to the problem, embracing multiple relevant actors and influences.
The Web Data Commons Schema.org Table Corpora
Authors: Ralph Peeters, Alexander Brinkmann, Christian Bizer
Venue: WWW
Year: 2024

The research on table representation learning, data retrieval, and data integration in the context of data lakes requires large table corpora for the training and evaluation of the developed methods. Over the years, several large table corpora such as WikiTables, GitTables, or the Dresden Web Table Corpus have been published and are used by the research community. This paper complements the set of public table corpora with the Web Data Commons Schema.org table corpora, two table corpora consisting of 4.2 (Release 2020) and 5 million (Release 2023) relational tables describing products, events, local businesses, job postings, recipes, movies, books, as well as 37 further types of entities. The feature that distinguishes the corpora from all other publicly available large table corpora is that all tables that describe entities of a specific type use the same attributes to describe these entities, i.e. all tables use a shared schema, the schema.org vocabulary. The shared schema eases the integration of data from different sources and allows training processes to focus on specific types of entities or specific attributes. Altogether the tables contain ~653 million rows of data which have been extracted from the Common Crawl web corpus and have been grouped into separate tables for each class/host combination, i.e. all records of a specific class that originate from a specific website are put into a single table. This paper describes the creation of the WDC Schema.org Table Corpora, gives an overview of the content of the corpora, and discusses their use cases.
Memory Disagreement: A Pseudo-Labeling Measure from Training Dynamics for Semi-supervised Graph Learning
Authors: Hongbin Pei, Yuheng Xiong, Pinghui Wang, Jing Tao, Jialun Liu, Huiqi Deng, Jie Ma, Xiaohong Guan
Venue: WWW
Year: 2024

In the realm of semi-supervised graph learning, pseudo-labeling is a pivotal strategy to utilize both labeled and unlabeled nodes for model training. Currently, confidence score is the most frequently used pseudo-labeling measure, however, it suffers from poor calibration and issues in out-of-distribution data. In this paper, we propose memory disagreement (MoDis for short), a novel uncertainty measure for pseudo-labeling. We uncover that training dynamics offer significant insights into prediction uncertainty --- if a graph model makes consistent predictions for an unlabeled node throughout training, the corresponding predicted label is likely to be correct. Thus, the node should be suitable for pseudo-labeling. The basic idea is supported by recent studies on training dynamics. We implement MoDis as the entropy of an accumulated distribution that summarizes the disagreement of the model's predictions throughout training. We further enhance and analyze MoDis in case studies, which show nodes with low MoDis are suitable for pseudo-labeling as these nodes tend to be distant from boundaries in both graph and representation space. We design MoDis based pseudo-label selection algorithm and corresponding pseudo-labeling algorithm, which are applicable to various graph neural networks. We empirically validate MoDis on eight benchmark graph datasets. The experimental results show that pseudo labels given by MoDis have better quality in correctness and information gain, and the algorithm benefits various graph neural networks, achieving an average relative improvement of 3.11% and reaching up to 30.24% when compared to the wildly-used uncertainty measure, confidence score. Moreover, we demonstrate the efficacy of MoDis on out-of-distribution nodes.
RimiRec: Modeling Refined Multi-interest in Hierarchical Structure for Recommendation
Authors: Haolei Pei, Yuanyuan Xu, Yangping Zhu, Yuan Nie
Venue: WWW
Year: 2024

Industrial recommender systems usually consist of the retrieval stage and the ranking stage, to handle the billion-scale of users and items. The retrieval stage retrieves candidate items relevant to user interests for recommendations and has attracted much attention. Frequently, a user shows refined multi-interests in a hierarchical structure. For example, a user likes Conan and Kuroba Kaito, which are the roles in hierarchical structure"Animation, Japanese Animation, Detective Conan".However, most existing methods ignore this hierarchical nature, and simply average the fine-grained interest information. Therefore, we propose a novel two-stage approach to explicitly modeling refined multi-interest in a hierarchical structure for recommendation. In the first hierarchical multi-interest mining stage, the hierarchical clustering and transformer-based model adaptively generate circles or sub-circles that users are interested in. In the second stage, the partition of retrieval space allows the EBR models to deal only with items within each circle and accurately capture users' interests. Experimental results show that the proposed approach achieves state-of-the-art performance. Our framework has also been deployed at Lofter.
Concentration of Power and Participation in Online Governance: the Ecosystem of Decentralized Autonomous Organizations
Authors: Andrea Peña-Calvin, Javier Arroyo, Andrew Schwartz, Samer Hassan
Venue: WWW
Year: 2024

Blockchain technology enables a new form of online community: Decentralized Autonomous Organizations (DAOs), where members typically vote on proposals using tokens. Enthusiasts claim DAOs provide new opportunities for openness, horizontality, and democratization. However, this phenomenon is still under research, especially given the lack of quantitative studies. This paper presents the first census-like quantitative analysis of the whole ecosystem of DAOs, including 30K DAO communities on the main DAO platforms. This enables us to provide insights into the allegedly "democratic'' nature of DAOs, building metrics concerning their lifespan, participation, and power concentration. Most DAOs have a short lifespan and low participation. There is also a positive correlation between community size and voting power concentration. Like other online communities, DAOs seem to follow the iron law: becoming increasingly oligarchic as they grow. Still, a significant amount of DAOs of varying sizes defy this idea by being egalitarian by design.
Finding Dense and Persistently Expansive Subgraphs
Authors: Petros Petsinis, Charalampos E. Tsourakakis, Panagiotis Karras
Venue: WWW
Year: 2024

How can we detect a group of individuals whose connectivity persists and even strengthens over time? Despite extensive research on temporal networks, this practically pertinent question has been scantily investigated. In this paper, we formulate the problem of selecting a subset of nodes whose induced subgraph maximizes the overall edge count while abiding by time-aware spectral connectivity constraints. We solve the problem via a semidefinite programming (SDP) relaxation. Our experiments on a broad array of synthetic and real-world data establish the effectiveness of our method and deliver key insights on real-world temporal graphs.