OpenRaiser/Intern-Atlas huggingface dataset:
Demo: PhishChain: A Decentralized and Transparent System to Blacklist Phishing URLs
Authors: Shehan Vidyakeerthi, Mohamed Nabeel, Charith Elvitigala, Chamath Keppitiyagama
Venue: WWW
Year: 2022

Blacklists are a widely-used Internet security mechanism to protect Internet users from financial scams, malicious web pages, and other cyber attacks based on blacklisted URLs. This demo introduces PhishChain, a transparent and decentralized system for blacklisting phishing URLs. At present, public/private domain blacklists, such as PhishTank, CryptoScamDB, and APWG, are maintained by a centralized authority, but operate in a crowd sourcing fashion to create a manually verified blacklist periodically. In addition to being a single point of failure, the blacklisting process utilized by such systems is not transparent. We utilize the blockchain technology to support transparency and decentralization, where no single authority is controlling the blacklist and all operations are recorded in an immutable distributed ledger. Further, we design a page rank based truth discovery algorithm to assign a phishing score to each URL based on crowd sourced assessment of URLs. As an incentive for voluntary participation, we assign skill points to each user based on their participation in URL verification.
GraphZoo: A Development Toolkit for Graph Neural Networks with Hyperbolic Geometries
Authors: Anoushka Vyas, Nurendra Choudhary, Mehrdad Khatir, Chandan K. Reddy
Venue: WWW
Year: 2022

Hyperbolic spaces have recently gained prominence for representation learning in graph processing tasks such as link prediction and node classification. Several Euclidean graph models have been adapted to work in the hyperbolic space and the variants have shown a significant increase in performance. However, research and development in graph modeling currently involve several tedious tasks with a scope of standardization including data processing, parameter configuration, optimization tricks, and unavailability of public codebases. With the proliferation of new tasks such as knowledge graph reasoning and generation, there is a need in the community for a unified framework that eases the development and analysis of both Euclidean and hyperbolic graph networks, especially for new researchers in the field. To this end, we present a novel framework, GraphZoo, that makes learning, designing and applying graph processing pipelines/models systematic through abstraction over the redundant components. The framework contains a versatile library that supports several hyperbolic manifolds and an easy-to-use modular framework to perform graph processing tasks which aids researchers in different components, namely, (i) reproduce evaluation pipelines of state-of-the-art approaches, (ii) design new hyperbolic or Euclidean graph networks and compare them against the state-of-the-art approaches on standard benchmarks, (iii) add custom datasets for evaluation, (iv) add new tasks and evaluation criteria.
Discovering Top-k Profitable Patterns for Smart Manufacturing
Authors: Shicheng Wan, Jiahui Chen, Peifeng Zhang, Wensheng Gan, Tianlong Gu
Venue: WWW
Year: 2022

In the past, many studies were developed to discover useful knowledge from rich data for decision-making in wide applications in Internet of Things and Web of Things, such as smart manufacturing. Utility-driven pattern mining (UPM) technology is famous in knowledge discovering domain. However, one of the biggest issues of UPM is the setting of a suitable minimum utility threshold (minUtil). The higher minUtil is, the fewer interesting patterns are obtained. Conversely, the lower minUtil is, the more useless patterns are discovered. In this paper, we propose a solution for discovering top-k profitable patterns with average-utility measure, which can be applied to manufacturing. The average-utility of a pattern, w.r.t its corresponding length, can be used to fairly measure the pattern. The proposed new upper-bounds on average-utility are tighter than previous upper-bounds. Moreover, based on these upper-bounds, the novel PPT algorithm utilizes merging and projection techniques to greatly reduce the search space. By adopting several threshold raising strategies, the PPT algorithm can discover correct top-k patterns in a short time. We also implemented the efficiency and effectiveness of the algorithm on real and synthetic datasets. The experimental results reveal that the algorithm not only get a complete set of top-k interesting patterns, but also works better than the state-of-the-art algorithm in terms of runtime, memory consumption and scalability. Especially, the proposed algorithm performs very well on dense datasets.
Revisiting Email Forwarding Security under the Authenticated Received Chain Protocol
Authors: Chenkai Wang, Gang Wang
Venue: WWW
Year: 2022

Email authentication protocols such as SPF, DKIM, and DMARC are used to detect spoofing attacks, but they face key challenges when handling email forwarding scenarios. Recently in 2019, a new Authenticated Received Chain (ARC) protocol was introduced to support mail forwarding applications to preserve the authentication records. After 2 years, it is still not well understood how ARC is implemented, deployed, and configured in practice. In this paper, we perform an empirical analysis on ARC usage and examine how it affects spoofing detection decisions on popular email provides that support ARC. After analyzing an email dataset of 600K messages, we show that ARC is not yet widely adopted, but it starts to attract adoption from major email providers (e.g., Gmail, Outlook). Our controlled experiment shows that most email providers’ ARC implementations are done correctly. However, some email providers (Zoho) have misinterpreted the meaning of ARC results, which can be exploited by spoofing attacks. Finally, we empirically investigate forwarding-based “Hide My Email” services offered by iOS 15 and Firefox, and show their implementations break ARC and can be leveraged by attackers to launch more successful spoofing attacks against otherwise well-configured email receivers (e.g., Gmail).
Deep Partial Multiplex Network Embedding
Authors: Qifan Wang, Yi Fang, Anirudh Ravula, Ruining He, Bin Shen, Jingang Wang, Xiaojun Quan, Dongfang Liu
Venue: WWW
Year: 2022

Network embedding is an effective technique to learn the low-dimensional representations of nodes in networks. Real-world networks are usually with multiplex or having multi-view representations from different relations. Recently, there has been increasing interest in network embedding on multiplex data. However, most existing multiplex approaches assume that the data is complete in all views. But in real applications, it is often the case that each view suffers from the missing of some data and therefore results in partial multiplex data. In this paper, we present a novel Deep Partial Multiplex Network Embedding approach to deal with incomplete data. In particular, the network embeddings are learned by simultaneously minimizing the deep reconstruction loss with the autoencoder neural network, enforcing the data consistency across views via common latent subspace learning, and preserving the data topological structure within the same network through graph Laplacian. We further prove the orthogonal invariant property of the learned embeddings and connect our approach with the binary embedding techniques. Experiments on four multiplex benchmarks demonstrate the superior performance of the proposed approach over several state-of-the-art methods on node classification, link prediction and clustering tasks.
Multi-level Recommendation Reasoning over Knowledge Graphs with Reinforcement Learning
Authors: Xiting Wang, Kunpeng Liu, Dongjie Wang, Le Wu, Yanjie Fu, Xing Xie
Venue: WWW
Year: 2022

Knowledge graphs (KGs) have been widely used to improve recommendation accuracy. The multi-hop paths on KGs also enable recommendation reasoning, which is considered a crystal type of explainability. In this paper, we propose a reinforcement learning framework for multi-level recommendation reasoning over KGs, which leverages both ontology-view and instance-view KGs to model multi-level user interests. This framework ensures convergence to a more satisfying solution by effectively transferring high-level knowledge to lower levels. Based on the framework, we propose a multi-level reasoning path extraction method, which automatically selects between high-level concepts and low-level ones to form reasoning paths that better reveal user interests. Experiments on three datasets demonstrate the effectiveness of our method.
Using Schema.org and Solid for Linked Data-based Machine-to-Machine Sales Contract Conclusion
Authors: Xinni Wang, Christoph H.-J. Braun, Andreas Both, Tobias Käfer
Venue: WWW
Year: 2022

We present a demo in which two robotic arms, controlled by rule-based Linked Data agents, trade a good in Virtual Reality. The agents follow the necessary steps to conclude a sales contract under German law. To conclude the contract, the agents exchange messages between their Solid Pods. The data in the messages is modelled using suitable terms from Schema.org.
A Meta-learning based Stress Category Detection Framework on Social Media
Authors: Xin Wang, Lei Cao, Huijun Zhang, Ling Feng, Yang Ding, Ningyun Li
Venue: WWW
Year: 2022

Psychological stress has become a wider-spread and serious health issue in modern society. Detecting stressors that cause the stress could enable people to take effective actions to manage the stress. Previous work relied on the stressor dictionary built upon words from the stressor-related categories in the LIWC (Linguistic Inquiry and Word Count), and focused on stress categories that appear frequently on social media. In this paper, we build a meta-learning based stress category detection framework, which can learn how to distinguish a new stress category with very little data through learning on frequently appeared categories without relying on any lexicon. It is comprised of three modules, i.e., encoder module, induction module, and relation module. The encoder module focuses on learning category-relevant representation of each tweet with Dependency Graph Convolutional Network and tweet attention. The induction module deploys Mixture of Experts mechanism to integrate and summarize a representation for each category. The relation module is adopted to measure the correlation between each pair of query tweets and categories. Through the three modules and the meta-training process, we can then obtain a model which learns to learn how to identify stress categories and can directly be employed to a new category with little labelled data. Our experimental results show that the proposed framework can achieve 75.3 accuracy with 3 labeled data for the rarely appeared stress categories. We also build a stress category dataset consisting of 12 stress categories with 1,553 manually labeled stressful microblogs which can help train AI models to assist psychological stress diagnosis.
A Rapid Source Localization Method in the Early Stage of Large-scale Network Propagation
Authors: Zhen Wang, Dongpeng Hou, Chao Gao, Jiajin Huang, Qi Xuan
Venue: WWW
Year: 2022

Recently, the rapid diffusion of malicious information in online social networks causes great harm to our society. Therefore, it is of great significance to localize diffusion sources as early as possible to stem the spread of malicious information. This paper proposes a novel sensor-based method, called greedy full-order neighbor localization (denoted as GFNL), to solve this problem under a low infection propagation in line with the real world. More specifically, GFNL includes two main components, i.e., the greedy-based sensor deployment strategy (DS) and direction-path-based source estimation strategy (ES). In more detail, to ensure sensors can observe a propagation information as early as possible, a set of sensors is deployed in a network to minimize the geodesic distance (i.e., the distance of the shortest path) between the candidate set and the sensor set based on DS. Then when a fraction of sensors observe a propagation, ES infers the source based on the idea that the distance of the actual propagation path is proportional to the observed time. Compared with some state-of-the-art methods, comprehensive experiments have proved the superiority and robustness of our proposed GFNL.
An Invertible Graph Diffusion Neural Network for Source Localization
Authors: Junxiang Wang, Junji Jiang, Liang Zhao
Venue: WWW
Year: 2022

Localizing the source of graph diffusion phenomena, such as misinformation propagation, is an important yet extremely challenging task in the real world. Existing source localization models typically are heavily dependent on the hand-crafted rules and only tailored for certain domain-specific applications. Unfortunately, a large portion of the graph diffusion process for many applications is still unknown to human beings so it is important to have expressive models for learning such underlying rules automatically. Recently, there is a surge of research body on expressive models such as Graph Neural Networks (GNNs) for automatically learning the underlying graph diffusion. However, source localization is instead the inverse of graph diffusion, which is a typical inverse problem in graphs that is well-known to be ill-posed because there can be multiple solutions and hence different from the traditional (semi-)supervised learning settings. This paper aims to establish a generic framework of invertible graph diffusion models for source localization on graphs, namely Invertible Validity-aware Graph Diffusion (IVGD), to handle major challenges including 1) Difficulty to leverage knowledge in graph diffusion models for modeling their inverse processes in an end-to-end fashion, 2) Difficulty to ensure the validity of the inferred sources, and 3) Efficiency and scalability in source inference. Specifically, first, to inversely infer sources of graph diffusion, we propose a graph residual scenario to make existing graph diffusion models invertible with theoretical guarantees; second, we develop a novel error compensation mechanism that learns to offset the errors of the inferred sources. Finally, to ensure the validity of the inferred sources, a new set of validity-aware layers have been devised to project inferred sources to feasible regions by flexibly encoding constraints with unrolled optimization techniques. A linearization technique is proposed to strengthen the efficiency of our proposed layers. The convergence of the proposed IVGD is proven theoretically. Extensive experiments on nine real-world datasets demonstrate that our proposed IVGD outperforms state-of-the-art comparison methods significantly. We have released our code at https://github.com/xianggebenben/IVGD.
Causal Representation Learning for Out-of-Distribution Recommendation
Authors: Wenjie Wang, Xinyu Lin, Fuli Feng, Xiangnan He, Min Lin, Tat-Seng Chua
Venue: WWW
Year: 2022

Modern recommender systems learn user representations from historical interactions, which suffer from the problem of user feature shifts, such as an income increase. Historical interactions will inject out-of-date information into the representation in conflict with the latest user feature, leading to improper recommendations. In this work, we consider the Out-Of-Distribution (OOD) recommendation problem in an OOD environment with user feature shifts. To pursue high fidelity, we set additional objectives for representation learning as: 1) strong OOD generalization and 2) fast OOD adaptation. This work formulates and solves the problem from a causal view. We formulate the user feature shift as an intervention and OOD recommendation as post-intervention inference of the interaction probability. Towards the learning objectives, we embrace causal modeling of the generation procedure from user features to interactions. However, the unobserved user features cannot be ignored, which make the estimation of the interaction probability intractable. We thus devise a new Variational Auto-Encoder for causal modeling by incorporating an encoder to infer unobserved user features from historical interactions. We further perform counterfactual inference to mitigate the effect of out-of-date interactions. Moreover, a decoder is used to model the interaction generation procedure and perform post-intervention inference. Fast adaptation is inherent owing to the reuse of partial user representations. Lastly, we devise an extension to encode fine-grained causal relationships from user features to preference. Empirical results on three datasets validate the strong OOD generalization and fast adaptation abilities of the proposed method.
Will You Accept the AI Recommendation? Predicting Human Behavior in AI-Assisted Decision Making
Authors: Xinru Wang, Zhuoran Lu, Ming Yin
Venue: WWW
Year: 2022

Internet users make numerous decisions online on a daily basis. With the rapid advances in AI recently, AI-assisted decision making—in which an AI model provides decision recommendations and confidence, while the humans make the final decisions—has emerged as a new paradigm of human-AI collaboration. In this paper, we aim at obtaining a quantitative understanding of whether and when would human decision makers adopt the AI model’s recommendations. We define a space of human behavior models by decomposing the human decision maker’s cognitive process in each decision-making task into two components: the utility component (i.e., evaluate the utility of different actions) and the selection component (i.e., select an action to take), and we perform a systematic search in the model space to identify the model that fits real-world human behavior data the best. Our results highlight that in AI-assisted decision making, human decision makers’ utility evaluation and action selection are influenced by their own judgement and confidence on the decision-making task. Further, human decision makers exhibit a tendency to distort the decision confidence in utility evaluations. Finally, we also analyze the differences in humans’ adoption behavior of AI recommendations as the stakes of the decisions vary.
ClusterSCL: Cluster-Aware Supervised Contrastive Learning on Graphs
Authors: Yanling Wang, Jing Zhang, Haoyang Li, Yuxiao Dong, Hongzhi Yin, Cuiping Li, Hong Chen
Venue: WWW
Year: 2022

We study the problem of supervised contrastive (SupCon) learning on graphs. The SupCon loss has been recently proposed for classification tasks by pulling data points in the same class closer than those of different classes. However, it could be difficult for SupCon to handle datasets with large intra-class variances and high inter-class similarities. This issue is also challenging when it couples with graph structures. To address this, we present the cluster-aware supervised contrastive learning loss (ClusterSCL1) for graph learning tasks. The main idea of ClusterSCL is to retain the structural and attribute properties of a graph in the form of nodes’ cluster distributions during supervised contrastive learning. Specifically, ClusterSCL introduces the strategy of cluster-aware data augmentation and integrates it with the SupCon loss. Extensive experiments on several widely adopted graph benchmarks demonstrate the superiority of ClusterSCL over the cross-entropy, SupCon, and other graph contrastive objectives.
DiriE: Knowledge Graph Embedding with Dirichlet Distribution
Authors: Feiyang Wang, Zhongbao Zhang, Li Sun, Junda Ye, Yang Yan
Venue: WWW
Year: 2022

Knowledge graph embedding aims to learn representations of entities and relations in low-dimensional space. Recently, extensive studies combine the characteristics of knowledge graphs with different geometric spaces, including Euclidean space, complex space, hyperbolic space and others, which achieves significant progress in representation learning. However, existing methods are subject to at least one of the following limitations: 1) ignoring the uncertainty, 2) incapability of complex relation patterns. To address the above issues simultaneously, we propose a novel model named DiriE, which embeds entities as Dirichlet distributions and relations as multinomial distributions. DiriE employs Bayesian inference to measure the relations between entities and learns binary embeddings of knowledge graphs for modeling complex relation patterns. Additionally, we propose a two-step negative triple generation method that generates negative triples of both entities and relations. We conduct a solid theoretical analysis to demonstrate the effectiveness and robustness of our method, including the expressiveness of complex relation patterns and the ability to model uncertainty. Furthermore, extensive experiments show that our method outperforms state-of-the-art methods in link prediction on benchmark datasets.
Internalization of Privacy Externalities through Negotiation: Social costs of third-party web-analytic tools and the limits of the legal data protection framework
Authors: Nils Wehkamp
Venue: WWW
Year: 2022

Tools for web-analytics such as Google Analytics are implemented across the majority websites. For most cases, the usage is free of charge for the website-owners. However, to use those tools to their full potential, it is necessary to share the collected personal data of the users with the tool-provider. This paper examines if this constellation of data collection and sharing can be interpreted as an externality of consumption in the sense of welfare economic theory. As it is shown that this is the case, the further analysis examines if the current technical and legal framework allows for an internalization of this externality through means of negotiation. It is illustrated that an internalization through negotiation is highly unlikely to succeed, because of the existence of information asymmetries, transaction cost and improper means for the enforcement of rights of disposal. It is further argued that even if some of those issues are addressed by data protection laws, the legal framework does not ensure a market situation necessary for a successful internalization. As a result, the externalities caused by the data collection through third-party web-analytic tools continue to exist. This leads to an inefficient high usage of third-party tools for web-analytics by website-owners.
DREW: Efficient Winograd CNN Inference with Deep Reuse
Authors: Ruofan Wu, Feng Zhang, Jiawei Guan, Zhen Zheng, Xiaoyong Du, Xipeng Shen
Venue: WWW
Year: 2022

Deep learning has been used in various domains, including Web services. Convolutional neural networks (CNNs), which are deep learning representatives, are among the most popular neural networks in Web systems. However, CNN employs a high degree of computing. In comparison to the training phase, the inference process is more frequently done on low-power computing equipments. The limited computing resource and high computation pressure limit the effective use of CNN algorithms in industry. Fortunately, a minimal filtering algorithm called Winograd can reduce convolution calculations by minimizing multiplication operations. We find that Winograd convolution can be sped up further by deep reuse technique, which reuses the similar data and computation processes. In this paper, we propose a new inference method, called DREW, which combines deep reuse with Winograd for further accelerating CNNs. DREW handles three difficulties. First, it can detect the similarities from the complex minimal filtering patterns by clustering. Second, it reduces the online clustering cost in a reasonable range. Third, it provides an adjustable method in clustering granularity balancing the performance and accuracy. Experiments show that 1) DREW further accelerates the Winograd convolution by an average of 2.06 × speedup; 2) when DREW is applied to end-to-end Winograd CNN inference, it achieves 1.71 × the average performance speedup with no (<0.4%) accuracy loss; 3) DREW reduces the number of convolution operations to 11% of the original operations on average.