OpenRaiser/Intern-Atlas huggingface dataset:
Through the Lens of the Web Conference Series: A Look Into the History of the Web
Authors: Damien Graux, Fabrizio Orlandi
Venue: WWW
Year: 2022

During the last three decades, the Web has been growing considerably in terms of number of available resources, traffic, types of media, usages, etc. In parallel, with 30+ editions, the WebConf series (ex. WWW, soon-to-be ACM WebConf) has witnessed how academia has been dealing with the Web as an object of research. In this study, we focus on the small story within the great one of the Web. In particular, by analysing the WebConf’s accepted papers and yearly events, we review how the conference has evolved across these decades and “driven” the evolution of the Web.
Analysis of Arbitrary Content on Blockchain-Based Systems using BigQuery
Authors: Marcel Gregoriadis, Robert Muth, Martin Florian
Venue: WWW
Year: 2022

Blockchain-based systems have gained immense popularity as enablers of independent asset transfers and smart contract functionality. They have also, since as early as the first Bitcoin blocks, been used for storing arbitrary contents such as texts and images. On-chain data storage functionality is useful for a variety of legitimate use cases. It does, however, also pose a systematic risk. If abused, for example by posting illegal contents on a public blockchain, data storage functionality can lead to legal consequences for operators and users that need to store and distribute the blockchain, thereby threatening the operational availability of entire blockchain ecosystems. In this paper, we develop and apply a cloud-based approach for quickly discovering and classifying content on public blockchains. Our method can be adapted to different blockchain systems and offers insights into content-related usage patterns and potential cases of abuse. We apply our method on the two most prominent public blockchain systems—Bitcoin and Ethereum—and discuss our results. To the best of our knowledge, the presented study is the first to systematically analyze non-financial content stored on the Ethereum blockchain and the first to present a side-by-side comparison between different blockchains in terms of the quality and quantity of stored data.
On Reliability Scores for Knowledge Graphs
Authors: Thomas Grubb, Bill Andersen, Omar Alonso
Venue: WWW
Year: 2022

The Instacart KG is a central data store which contains facts regarding grocery products, ranging from taxonomic classifications to product nutritional information. With a view towards providing reliable and complete information for downstream applications, we propose an automated system for providing these facts with a score based on their reliability. This system passes data through a series of contextualized unit tests; the outcome of these tests are aggregated in order to provide a fact with a discrete score: reliable, questionable, or unreliable. These unit tests are written with explainability, scalability, and correctability in mind.
Mining with Rarity for Web Intelligence
Authors: Yijie Gui, Wensheng Gan, Yao Chen, Yongdong Wu
Venue: WWW
Year: 2022

Mining with rarity makes sense to take advantage of data mining for Web intelligence. In some scenarios, the rare patterns are meaningful in data intelligent systems. Interesting pattern discovery plays an important role in real-world applications. In this field, a great deal of work has been done. In general, a high-utility pattern may include frequent items and also rare items. Rare pattern discovery emerges gradually and helps policy-makers making related marketing strategies. However, the existing Apriori-like methods for discovering high-utility rare itemsets (HURIs) are not efficient. In this paper, we address the problem of mining with rarity and propose an efficient algorithm, named HURI-Miner, which uses the data structure called revised utility-list to find HURIs from a transaction database. Furthermore, we utilize several powerful pruning strategies to prune the search space and save the computational complexity. In the process of rare pattern mining, the HURIs are directly generated without the generate-and-test method. Finally, a series of experimental results show that this proposed method has superior effectiveness and efficiency.
DLWoT'22: 2nd International Workshop on Deep Learning for the Web of Things
Authors: Wenzhong Guo, Chin-Chen Chang, Eyhab Al-Masri, Chi-Hua Chen, Haishuai Wang, Qichun Zhang, K. Shankar
Venue: WWW
Year: 2022

In recent years, deep learning and web of things (WoT) have become hot topics. The relevant research issues in deep learning have been in increasingly investigated and published. Therefore, the title of this workshop is ”the 2nd International Workshop on Deep Learning for the Web of Things (DLWoT’22)” for the Web Conference 2022 (WWW’22). This workshop solicits papers on various disciplines, which include but are not limited to: (1) deep learning for massive IoT, (2) deep learning for critical IoT, (3) deep learning for enhancing IoT security, (4) deep learning for enhancing IoT privacy, (5) preprocessing of IoT data for AI modeling, and (6) deep learning for IoT applications (e.g., smart home, smart agriculture, interactive art, and so on). DLWoT’22 includes two tracks: (1) keynote speaker and (2) workshop papers. One keynote speaker is invited to give a talk, and 8 accepted workshop papers are presented.
QAnswer: Towards Question Answering Search over Websites
Authors: Kunpeng Guo, Clement Defretiere, Dennis Diefenbach, Christophe Gravier, Antoine Gourru
Venue: WWW
Year: 2022

Question Answering (QA) is increasingly used by search engines to provide results to their end-users, yet very few websites currently use QA technologies for their search functionality. To illustrate the potential of QA technologies for the website search practitioner, we demonstrate web searches that combine QA over knowledge graphs and QA over free text – each being usually tackled separately. We also discuss the different benefits and drawbacks of both approaches for web site searches. We use the case studies made of websites hosted by the Wikimedia Foundation (namely Wikipedia and Wikidata). Differently from a search engine (e.g. Google, Bing, etc), the data are indexed integrally, i.e. we do not index only a subset, and they are indexed exclusively, i.e. we index only data available on the corresponding website.
A Triangle Framework among Subgraph Isomorphism, Pharmacophore and Structure-function Relationship
Authors: Mengjiao Guo, Hui Zheng, Tengfei Ji, Hu Fa, Jing He
Venue: WWW
Year: 2022

Coronavirus disease 2019 (COVID-19) has gained utmost attention in the current time from academic research and industrial practices because it continues to rage in many countries. Pharmacophore models exploit molecule topological similarity as well as functional compound similarity so that they can be reliable via the application of the concept of bioisosterism. In this work, we analyze the targets for coronavirus protein and the structure of RNA virus variation, thereby complete the safety and pharmacodynamic action evaluation of small-molecule anti-coronavirus oral drugs. Common pharmacophore identifications could be converted into subgraph querying problems, due to chemical structures can also be converted to graphs, which is a knotty problem pressing for a solution. We adopt simplified representation pharmacophore graphs by reducing complete molecular structures to abstracts to detect isomorphic topological patterns and further to improve the substructure retrieval efficiency. Our threefold architecture subgraph isomorphism-based method retrieves query subgraphs over large graphs. First, by means of extracting a sequence of subgraphs to be matched and then comparing the number of vertex and edge between the potential isomorphic subgraphs and the query graph, we lower the computational scaling markedly. Afterwards, the directed vertex and edge matrix recording vertex and edge positional relation, directional relation and distance relation has been created. Then, on the basis of permutation theorem, we calculate the row sum of vertex and edge adjacency matrix of query graph and potential sample. Finally, according to equinumerosity theorem, we check the eigenvalues of the vertex and edge adjacency matrices of the two graphs are equinumerous. The topological distance could be calculated based on the graph isomorphism and the subgraph isomorphism can be implemented after the combination of the subgraph. The proposed quantitative structure–function relationships (QSFR) approach can be effectively applied for pharmacophoric abstract patterns identification. The framework of new drug development for covid-19 has been established based on this triangle.
To Trust or Not To Trust: How a Conversational Interface Affects Trust in a Decision Support System
Authors: Akshit Gupta, Debadeep Basu, Ramya Ghantasala, Sihang Qiu, Ujwal Gadiraju
Venue: WWW
Year: 2022

Trust is an important component of human-AI relationships and plays a major role in shaping the reliance of users on online algorithmic decision support systems. With recent advances in natural language processing, text and voice-based conversational interfaces have provided users with new ways of interacting with such systems. Despite the growing applications of conversational user interfaces (CUIs), little is currently understood about the suitability of such interfaces for decision support and how CUIs inspire trust among humans engaging with decision support systems. In this work, we aim to address this gap and answer the following question: to what extent can a conversational interface build user trust in decision support systems in comparison to a conventional graphical user interface? To this end, we built a text-based conversational interface, and a conventional web-based graphical user interface. These served as the means for users to interact with an online decision support system to help them find housing, given a fixed set of constraints. To understand how the accuracy of the decision support system moderates user behavior and trust across the two interfaces, we considered an accurate and inaccurate system. We carried out a 2 × 2 between-subjects study (N = 240) on the Prolific crowdsourcing platform. Our findings show that the conversational interface was significantly more effective in building user trust and satisfaction in the online housing recommendation system when compared to the conventional web interface. Our results highlight the potential impact of conversational interfaces for trust development in decision support systems.
Linking Streets in OpenStreetMap to Persons in Wikidata
Authors: Daria Gurtovoy, Simon Gottschalk
Venue: WWW
Year: 2022

Geographic web sources such as OpenStreetMap (OSM) and knowledge graphs such as Wikidata are often unconnected. An example connection that can be established between these sources are links between streets in OSM to the persons in Wikidata they were named after. This paper presents StreetToPerson, an approach for connecting streets in OSM to persons in a knowledge graph based on relations in the knowledge graph and spatial dependencies. Our evaluation shows that we outperform existing approaches by 26 percentage points. In addition, we apply StreetToPerson on all OSM streets in Germany, for which we identify more than 180,000 links between streets and persons.
From Indymedia to Tahrir Square: The Revolutionary Origins of Status Updates on Twitter
Authors: Harry Halpin, Evan Henshaw-Plath
Venue: WWW
Year: 2022

One of the most important developments in the history of the Web was the development of the status update. Although social media has been approached by a number of critical theorists as an instrument of control and surveillance, it should be remembered that social media began as liberatory technology harnessed by social movements. In this essay, we trace the origin of the status update for spreading news from protest-driven community networks like Indymedia and text messages for protest coordination via TxtMob. In fact, the use of status updates by Indymedia and the wider anti-globalization movement prefigured their usage in Tahrir Square and the Black Lives Matter movement in the USA. This historical link goes through Twitter itself, as the early Twitter engineers were veterans of Indymedia. There is still much to learn from Indymedia: Framing social media as invented and then harnessed by social movements may even provide innovative solutions to issues of content moderation and censorship. Exploring the origin of social media in social movements provides a perspective on the history of the Web from the tradition of the oppressed.
Short, Colorful, and Irreverent! A Comparative Analysis of New Users on WallstreetBets During the Gamestop Short-squeeze
Authors: Ehsan ul Haq, Tristan Braud, Lik-Hang Lee, Anish K. Vallapuram, Yue Yu, Gareth Tyson, Pan Hui
Venue: WWW
Year: 2022

WallStreetBets (WSB) is a Reddit community that primarily discusses high-risk options and stock trading. In January 2021, it attracted worldwide attention as one of the epicentres of a significant short squeeze on US markets. Following this event, the number of users and their activity increased exponentially. In this paper, we study the changes caused in the WSB community by such an increase in activity. We perform a comparative analysis between long-term users and newcomers and examine their respective writing styles, topics, and susceptibility to community feedback. We report a significant difference in the post length and the number of emojis between the regular and new users joining WSB. Newer users’ activity also closely follows the affected companies’ stock prices. Finally, although community feedback affects the choices of topics for all users, new users are less prone to select their subsequent message topics based on past community feedback.
QLUE: A Computer Vision Tool for Uniform Qualitative Evaluation of Web Pages
Authors: Waleed Hashmi, Moumena Chaqfeh, Lakshminarayanan Subramanian, Yasir Zaki
Venue: WWW
Year: 2022

The increasing complexity of the web has attracted a number of solutions to offer optimized versions of web pages that are lighter to process and faster to load. These solutions have been quantitatively evaluated to show significant speed-ups in load times and/or considerable savings in bandwidth/memory consumption. However, while these solutions often produce optimized versions from existing pages, they rarely evaluate the impact of their optimizations on the original content and functionality. Additionally, due to the lack of a unified metric to evaluate the similarity of the pages generated by these solutions in comparison to the original pages, it is not yet possible to fairly compare the results obtained from different user studies campaigns, unless recruiting the exact same users, which is extremely challenging. In this paper, we demonstrate the lack of qualitative evaluation metrics, and propose QLUE (QuaLitative Uniform Evaluation), a tool that automates the qualitative evaluation of web pages generated by web complexity solutions with respect to their original versions using computer vision. QLUE evaluates the content and the functionality of these pages separately using two metrics: QLUE’s Structural Similarity, to assess the former, and QLUE’s Functional Similarity to assess the latter—a task that is proven to be challenging for humans given the complex functional dependencies in modern pages. Our results show that QLUE computes comparable content and functional scores to those provided by humans. Specifically, 90% of a set of 100 pages were given a similarity score between 90% and 100% by human evaluators, while QLUE shows similar scores for more than 75% of the same pages. In terms of time complexity, QLUE shows that it is capable of evaluating an optimized web page in a few minutes.
Inflation Improves Graph Neural Networks
Authors: Dongxiao He, Rui Guo, Xiaobao Wang, Di Jin, Yuxiao Huang, Wenjun Wang
Venue: WWW
Year: 2022

Graph neural networks (GNNs) have gained significant success in graph representation learning and become the go-to approach for many graph-based tasks. Despite their effectiveness, the performance of GNNs is known to decline gradually as the number of layers increases. This attenuation is mainly caused by noise propagation, which refers to the useless or negative information propagated (directly or indirectly) from other nodes during the multi-layer graph convolution for node representation learning. This noise increases more severely as the layers of GNNs deepen, which is also a main reason of over-smoothing. In this paper, we propose a new convolution strategy for GNNs to address this problem via suppressing the noise propagation. Specifically, we first find that the feature propagation process of GNNs can be taken as a Markov chain. And then, based on the idea of Markov clustering, we introduce a new graph inflation layer (i.e., using a power function over the distribution) into GNNs to prevent noise propagating from local neighbourhoods to the whole graph with the increase of network layers. Our method is simple in design, which does not require any changes on the original basis and therefore can be easily extended. We conduct extensive experiments on real-world networks and have a stable improved performance as the network depth increases over existing GNNs.
A Cluster-Based Nearest Neighbor Matching Algorithm for Enhanced A/A Validation in Online Experimentation
Authors: Yan He, Lin Yu, Miao Chen, William Choi, Don Matheson
Venue: WWW
Year: 2022

Online controlled experiments are commonly used to measure how much value new features deployed to the products bring to the users. Although the experiment design is straightforward in theory, running large-scale online experiments can be quite complex. An essential step to run a rigorous experiment is to validate the balance between the buckets (a.k.a. the random samples) before it proceeds to the A/B phase. This step is called A/A validation and it serves to ensure that there is no pre-existing significant difference between the test and control buckets. In this paper, we propose a new matching algorithm to assign users to buckets and improve A/A balance. It has the capability to deal with massive user size and shows improved performance compared to existing methods.
GraphReformCD: Graph Reformulation for Effective Community Detection in Real-World Graphs
Authors: Jiwon Hong, Dong-Hyuk Seo, Jeewon Ahn, Sang-Wook Kim
Venue: WWW
Year: 2022

Community detection, one of the most important tools for graph analysis, finds groups of strongly connected nodes in a graph. However, community detection may suffer from misleading information in a graph, such as a nontrivial number of inter-community edges or an insufficient number of intra-community edges. In this paper, we propose GraphReformCD that reformulates a given graph into a new graph in such a way that community detection can be conducted more accurately. For the reformulation, it builds a k-nearest neighbor graph that gives a node k opportunities to connect itself to those nodes that are likely to belong to the same community together with the node. To find the nodes that belong to the same community, it employs the structural similarities such as Jaccard index and SimRank. To validate the effectiveness of our GraphReformCD, we perform extensive experiments with six real-world and four synthetic graphs. The results show that our GraphReformCD enables state-of-the-art methods to improve their accuracy significantly up to 40.6% in community detection.
Multi-Granularity Residual Learning with Confidence Estimation for Time Series Prediction
Authors: Min Hou, Chang Xu, Zhi Li, Yang Liu, Weiqing Liu, Enhong Chen, Jiang Bian
Venue: WWW
Year: 2022

Time-series prediction is of high practical value in a wide range of applications such as econometrics and meteorology, where the data are commonly formed by temporal patterns. Most prior works ignore the diversity of dynamic pattern frequency, i.e., different granularities, suffering from insufficient information exploitation. Thus, multi-granularity learning is still under-explored for time-series prediction. In this paper, we propose a Multi-granularity Residual Learning Framework (MRLF) for more effective time series prediction. For a given time series, intuitively, there are more or less semantic overlaps and validity differences among its representations of different granularities. Due to the information redundancy, straightforward methods that leverage multi-granularity data, such as concatenation or ensemble, can easily lead to the model being dominated by the redundant coarse-grained trend information. Therefore, we design a novel residual learning net to model the prior knowledge of the fine-grained data’s distribution through the coarse-grained one. Then, by calculating the residual between multi-granularity data, the redundant information be removed. Furthermore, to alleviate the side effect of validity differences, we introduce a self-supervised objective for confidence estimation, which delivers more effective optimization without the requirement of additional annotation efforts. Extensive experiments on the real-world datasets indicate that multi-granular information significantly improves the time series prediction performance, and our model is superior in capturing such information.
Unsupervised Post-Time Fake Social Message Detection with Recommendation-aware Representation Learning
Authors: Shao-Ping Hsiao, Yu-Che Tsai, Cheng-Te Li
Venue: WWW
Year: 2022

This paper deals with a more realistic scenario of fake message detection on social media, i.e., unsupervised post-time detection. Given a source message, our goal is to determine whether it is fake without using labeled data and without requiring user interacted with the given message. We present a novel learning framework, Recommendation-aware Message Representation (RecMR), to achieve the goal. The key idea is to learn user preferences and have them encoded into the representation of the source message through jointly training the tasks of user recommendation and binary detection. Experiments conducted on two real Twitter datasets exhibit the promising performance of RecMR, and show the effectiveness of recommended users in unsupervised detection.
Learning Privacy-Preserving Graph Convolutional Network with Partially Observed Sensitive Attributes
Authors: Hui Hu, Lu Cheng, Jayden Parker Vap, Mike Borowczak
Venue: WWW
Year: 2022

Recent studies have shown Graph Neural Networks (GNNs) are extremely vulnerable to attribute inference attacks. To tackle this challenge, existing privacy-preserving GNNs research assumes that the sensitive attributes of all users are known beforehand. However, due to different privacy preferences, some users (i.e., private users) may prefer not to reveal sensitive information that others (i.e., non-private users) would not mind disclosing. For example, in social networks, male users are typically less sensitive to their age information than female users. The age disclosure of male users can lead to the age information of female users in the network exposed. This is partly because social media users are connected, the homophily property and message-passing mechanism of GNNs can exacerbate individual privacy leakage. In this work, we study a novel and practical problem of learning privacy-preserving GNNs with partially observed sensitive attributes. In particular, we propose a novel privacy-preserving GCN model coined DP-GCN, which effectively protects private users’ sensitive information that has been revealed by non-private users in the same network. DP-GCN consists of two modules: First, Disentangled Representation Learning Module (DRL), which disentangles the original non-sensitive attributes into sensitive and non-sensitive latent representations that are orthogonal to each other. Second, Node Classification Module (NCL), which trains the GCN to classify unlabeled nodes in the graph with non-sensitive latent representations. Experimental results on five benchmark datasets demonstrate the effectiveness of DP-GCN in preserving private users’ sensitive information while maintaining high node classification accuracy.