OpenRaiser/Intern-Atlas huggingface dataset:
Analyzing the Differences between Professional and Amateur Esports through Win Probability
Authors: Peter Xenopoulos, William Robert Freeman, Cláudio T. Silva
Venue: WWW
Year: 2022

Estimating a team’s win probability at any given point of a game is a common task for any sport, including esports, and is important for valuing player actions, assessing profitable bets, and engaging fans with interesting metrics. Past studies of win probability in esports have relied on data extracted from matches held in well-structured and organized professional tournaments. In these tournaments, players play on set teams, oftentimes where players are well acquainted with all participants. However, there has been little study of win probability modeling in casual gaming environments – those where players are randomly matched – even though these environments form the bulk of gaming hours played. Using interpretable win probability models trained on large CSGO data sets, we improve upon the current state of the art in Counter-Strike: Global Offensive (CSGO) win probability prediction. We identify important features, such as team HP and equipment value, across different skill levels. We also find a small benefit to using Elo-based player skill estimates in predicting win probability. Furthermore, we discuss how our win probability models can be used to investigate the problem of player-leaving in competitive matchmaking.
GraphLearning'22: 1st International Workshop on Graph Learning
Authors: Feng Xia, Renaud Lambiotte, Charu Aggarwal
Venue: WWW
Year: 2022

The First Workshop on Graph Learning aims to bring together researchers and practitioners from academia and industry to discuss recent advances and core challenges of graph learning. This workshop will be established as a platform for multiple disciplines such as computer science, applied mathematics, physics, social sciences, data science, complex networks, and systems engineering. Core challenges in regard to theory, methodology, and applications of graph learning will be the main center of discussions at the workshop.
FIRE: Fast Incremental Recommendation with Graph Signal Processing
Authors: Jiafeng Xia, Dongsheng Li, Hansu Gu, Jiahao Liu, Tun Lu, Ning Gu
Venue: WWW
Year: 2022

Recommender systems are incremental in nature. Recent progresses in incremental recommendation rely on capturing the temporal dynamics of users/items from temporal interaction graphs, so that their user/item embeddings can evolve together with the graph structures. However, these methods are faced with two key challenges: 1) model training and/or updating are time-consuming and 2) new users/items cannot be effectively handled. To this end, we propose the fast incremental recommendation (FIRE) method from a graph signal processing perspective. FIRE is non-parametric which does not suffer from the time-consuming back-propagations as in previous learning-based methods, significantly improving the efficiency of model updating. In addition, we encode user/item temporal information and side information by designing new graph filters in FIRE, which can capture the temporal dynamics of users/items and address the cold-start issue for new users/items, respectively. Experimental studies on four popular datasets demonstrate that FIRE can improve the accuracy by a large margin and improve the model updating efficiency by at least 3X compared with the state-of-the-art incremental recommendation algorithms. The Code is available at https://github.com/Yaveng/FIRE.
Creating Signature-Based Views for Description Logic Ontologies with Transitivity and Qualified Number Restrictions
Authors: Yue Xiang, Xuan Wu, Chang Lu, Yizheng Zhao
Venue: WWW
Year: 2022

Developing ontologies for the Semantic Web is a time-consuming and error-prone task that typically requires the investment of considerable manpower and resources, as well as collaborative efforts. A potentially better idea is to reuse the “off-the-shelf” ontologies, whenever possible, somehow as per certain demands and requirements. A promising way to achieve ontology reuse is through creating views of ontologies, analogous to creating views of databases, with the resulting views focusing on specific topics and content of the original ontologies. This paper explores the problem of creating views of ontologies using a uniform interpolation approach. In particular, we develop a novel and practical uniform interpolation method for creating signature-based views for ontologies specified in the description logic , a very expressive description logic for which uniform interpolation has not been fully addressed. The method is terminating and sound, and computes uniform interpolants of -ontologies by eliminating from the input ontologies the names not used in the view using a forgetting procedure. This makes it the first and so far the only approach to eliminate both concept and (non-transitive) role names from -ontologies. Despite the inherent difficulty of uniform interpolation for this level of expressivity, an empirical evaluation with a prototypical implementation show very good success rates on a corpus of real-world ontologies, and demonstrates clear algorithmic advantage over the state-of-the-art system LETHE. This is extremely useful from the semantic web perspective, as it provides knowledge engineers with a powerful tool to create views of ontologies for ontology reuse.
Game of Hide-and-Seek: Exposing Hidden Interfaces in Embedded Web Applications of IoT Devices
Authors: Wei Xie, Jiongyi Chen, Zhenhua Wang, Chao Feng, Enze Wang, Yifei Gao, Baosheng Wang, Kai Lu
Venue: WWW
Year: 2022

Recent years have seen increased attacks targeting embedded web applications of IoT devices. An important target of such attacks is the hidden interface of embedded web applications, which employs no protection but exposes security-critical actions and sensitive information to illegitimate users. With the severity and the pervasiveness of this issue, it is crucial to identify the vulnerable hidden interfaces, shed light on best practices and raise public awareness. In this paper, we present, a new approach that automatically exposes hidden web interfaces of IoT devices. Specifically, constructs probing requests through firmware analysis to test physical devices, and narrows down the scope of identification by filtering out irrelevant requests and interfaces through differential analysis. It pinpoints hidden interfaces by attaching various device-setting parameters in the probing requests and matching keywords of sensitive information. Evaluated on 17 IoT devices, successfully identified 44 vulnerabilities, including 43 previously unknown ones. also demonstrates surprising efficiency: on average, it delivered 151438 probing requests, taking only 47 minutes on each target device.
From Discrimination to Generation: Knowledge Graph Completion with Generative Transformer
Authors: Xin Xie, Ningyu Zhang, Zhoubo Li, Shumin Deng, Hui Chen, Feiyu Xiong, Mosha Chen, Huajun Chen
Venue: WWW
Year: 2022

Knowledge graph completion aims to address the problem of extending a KG with missing triples. In this paper, we provide an approach GenKGC, which converts knowledge graph completion to sequence-to-sequence generation task with the pre-trained language model. We further introduce relation-guided demonstration and entity-aware hierarchical decoding for better representation learning and fast inference. Experimental results on three datasets show that our approach can obtain better or comparable performance than baselines and achieve faster inference speed compared with previous methods with pre-trained language models. We also release a new large-scale Chinese knowledge graph dataset OpenBG500 for research purpose1.
Prototype Feature Extraction for Multi-task Learning
Authors: Shen Xin, Yuhang Jiao, Cheng Long, Yuguang Wang, Xiaowei Wang, Sen Yang, Ji Liu, Jie Zhang
Venue: WWW
Year: 2022

Multi-task learning (MTL) has been widely utilized in various industrial scenarios, such as recommender systems and search engines. MTL can improve learning efficiency and prediction accuracy by exploiting commonalities and differences across tasks. However, MTL is sensitive to relationships among tasks and may have performance degradation in real-world applications, because existing neural-based MTL models often share the same network structures and original input features. To address this issue, we propose a novel multi-task learning model based on Prototype Feature Extraction (PFE) to balance task-specific objectives and inter-task relationships. PFE is a novel component to disentangle features for multiple tasks. To better extract features from original inputs before gating networks, we introduce a new concept, namely prototype feature center, to disentangle features for multiple tasks. The extracted prototype features fuse various features from different tasks to better learn inter-task relationships. PFE updates prototype feature centers and prototype features iteratively. Our model utilizes the learned prototype features and task-specific experts for MTL. We implement PFE on two public datasets. Empirical results show that PFE outperforms state-of-the-art MTL models by extracting prototype features. Furthermore, we deploy PFE in a real-world recommender system (one of the world’s top-tier short video sharing platforms) to showcase that PFE can be widely applied in industrial scenarios.
Compressive Sensing Approaches for Sparse Distribution Estimation Under Local Privacy
Authors: Zhongzheng Xiong, Jialin Sun, Xiaojun Mao, Jian Wang, Shan Ying, Zengfeng Huang
Venue: WWW
Year: 2022

Recent years, local differential privacy (LDP) has been adopted by many web service providers like Google [23], Apple [33] and Microsoft [15] to collect and analyse users’ data privately. In this paper, we consider the problem of discrete distribution estimation under local differential privacy constraints. Distribution estimation is one of the most fundamental estimation problems, which is widely studied in both non-private and private settings. In the local model, private mechanisms with provably optimal sample complexity are known. However, they are optimal only in the worst-case sense; their sample complexity is proportional to the size of the entire universe, which could be huge in practice. In this paper, we consider sparse or approximately sparse (e.g. highly skewed) distribution, and show that the number of samples needed could be significantly reduced. This problem has been studied recently [1], but they only consider strict sparse distributions and the high privacy regime. We propose new privatization mechanisms based on compressive sensing. Our methods work for approximately sparse distributions and medium privacy, and have optimal sample and communication complexity.
Does Evidence from Peers Help Crowd Workers in Assessing Truthfulness?
Authors: Jiechen Xu, Lei Han, Shaoyang Fan, Shazia Sadiq, Gianluca Demartini
Venue: WWW
Year: 2022

Misinformation has been rapidly spreading online. The current approach to deal with it is deploying expert fact-checkers that follow forensic processes to identify the veracity of statements. Unfortunately, such an approach does not scale well. To deal with this, crowdsourcing has been looked at as an opportunity to complement the work of trained journalists. In this work, we look at the effect of presenting the crowd with evidence from others while judging the veracity of statements. We implement various variants of the judgment task design to understand if and how the presented evidence may or may not affect the way of crowd workers judging truthfulness and their performance. Our results show that, in certain cases, the presented evidence may mislead crowd workers who would otherwise be more accurate if judging independently from others. Those who made correct use of the provided evidence, however, could benefit from it and generate better judgments.
Lie to Me: Abusing the Mobile Content Sharing Service for Fun and Profit
Authors: Guosheng Xu, Siyi Li, Hao Zhou, Shucen Liu, Yutian Tang, Li Li, Xiapu Luo, Xusheng Xiao, Guoai Xu, Haoyu Wang
Venue: WWW
Year: 2022

Online content sharing is a widely used feature in Android apps. In this paper, we observe a new Fake-Share attack that adversaries can abuse existing content sharing services to manipulate the displayed source of shared content to bypass the content review of targeted Online Social Apps (OSAs) and induce users to click on the shared fraudulent content. We show that seven popular content-sharing services (including WeChat, AliPay, and KakaoTalk) are vulnerable to such an attack. To detect this kind of attack and explore whether adversaries have leveraged it in the wild, we propose DeFash, a multi-granularity detection tool including static analysis and dynamic verification. The extensive in-the-lab and in-the-wild experiments demonstrate that DeFash is effective in detecting such attacks. We have identified 51 real-world apps involved in Fake-Share attacks. We have further harvested over 24K Sharing Identification Information (SIIs) that can be abused by attackers. It is hence urgent for our community to take actions to detect and mitigate this kind of attack.
Evidence-aware Fake News Detection with Graph Neural Networks
Authors: Weizhi Xu, Junfei Wu, Qiang Liu, Shu Wu, Liang Wang
Venue: WWW
Year: 2022

The prevalence and perniciousness of fake news has been a critical issue on the Internet, which stimulates the development of automatic fake news detection in turn. In this paper, we focus on the evidence-based fake news detection, where several evidences are utilized to probe the veracity of news (i.e., a claim). Most previous methods first employ sequential models to embed the semantic information and then capture the claim-evidence interaction based on different attention mechanisms. Despite their effectiveness, they still suffer from two main weaknesses. Firstly, due to the inherent drawbacks of sequential models, they fail to integrate the relevant information that is scattered far apart in evidences for veracity checking. Secondly, they neglect much redundant information contained in evidences that may be useless or even harmful. To solve these problems, we propose a unified Graph-based sEmantic sTructure mining framework, namely GET in short. Specifically, different from the existing work that treats claims and evidences as sequences, we model them as graph-structured data and capture the long-distance semantic dependency among dispersed relevant snippets via neighborhood propagation. After obtaining contextual semantic information, our model reduces information redundancy by performing graph structure learning. Finally, the fine-grained semantic representations are fed into the downstream claim-evidence interaction module for predictions. Comprehensive experiments have demonstrated the superiority of GET over the state-of-the-arts.
Dual-branch Density Ratio Estimation for Signed Network Embedding
Authors: Pinghua Xu, Yibing Zhan, Liu Liu, Baosheng Yu, Bo Du, Jia Wu, Wenbin Hu
Venue: WWW
Year: 2022

Signed network embedding (SNE) has received considerable attention in recent years. A mainstream idea of SNE is to learn node representations by estimating the ratio of sampling densities. Though achieving promising performance, these methods based on density ratio estimation are limited to the issues of confusing sample, expected error, and fixed priori. To alleviate the above-mentioned issues, in this paper, we propose a novel dual-branch density ratio estimation (DDRE) architecture for SNE. Specifically, DDRE 1) consists of a dual-branch network, dealing with the confusing sample; 2) proposes the expected matrix factorization without sampling to avoid the expected error; and 3) devises an adaptive cross noise sampling to alleviate the fixed priori. We perform sign prediction and node classification experiments on four real-world and three artificial datasets, respectively. Extensive empirical results demonstrate that DDRE not only significantly outperforms the methods based on density ratio estimation but also achieves competitive performance compared with other types of methods such as graph likelihood, generative adversarial networks, and graph convolutional networks. Code is publicly available at https://github.com/WHU-SNA/DDRE.
Word Embedding based Heterogeneous Entity Matching on Web of Things
Authors: Xingsi Xue, Jianhua Guo
Venue: WWW
Year: 2022

Web of Things (WoT) is capable of promoting the knowledge discovery and address interoperability problems of diverse Internet of Things (IoT) applications. However, due to the dynamic and diverse features of data entities on WoT, the heterogeneous entity matching has become arguably the greatest “new frontier” for WoT advancements. Currently, the data entities and the corresponding knowledge on WoT are generally modelled with the ontology, and therefore, matching heterogeneous data entities on WoT can be converted to the problem of matching ontologies. Ontology matching is a complex cognitive process, it is usually initially done manually by domain experts. To effectively distinguish the heterogeneous entities and determine high-quality ontology alignment, this work proposes a word embedding based matching technique. Our approach models the word’s semantic in the vector space, and use two vectors’ cosine angle to measure the corresponding words’ similarity. In addition, the word embedding approach does not depend on a specific knowledge base and retain the rich semantic information of words, which makes our proposal more robust. The experiment uses Ontology Alignment Evaluation Initiative (OAEI)’s benchmark for testing, and the experimental results show that our approach outperforms other advanced matching methods.
What Does Perception Bias on Social Networks Tell Us About Friend Count Satisfaction?
Authors: Shen Yan, Kristen M. Altenburger, Yi-Chia Wang, Justin Cheng
Venue: WWW
Year: 2022

Social network platforms have enabled large-scale measurement of user-to-user networks such as friendships. Less studied is user sentiment about their networks, such as a user’s satisfaction with their number of friends. We surveyed over 85,000 Facebook users about how satisfied they were with their number of friends on Facebook, connecting these responses to their on-platform activity. As suggested in prior work, we’d expect users who are not satisfied with their friend count to have a higher probability of experiencing the friendship paradox: “your friends have more friends than you”. However in our sample, among users with more than 3,500 friends, no user experiences the friendship paradox. Instead, we still observe that those users with more friends would prefer to have even more friends. The friendship paradox also contributes to local perception bias, defined as the difference between the average number of friends among a user’s friends and the average friend count in the population. Users with a positive perception bias – their friends have more friends than others – are less satisfied with their friend count. We then introduce a weighted perception bias metric that considers the fact that different friends have different effects on an individual’s perception. We find this new weighted perception bias better distinguishes friend count satisfaction outcomes for users with high friend count when compared to the original perception bias metric. We conclude with modeling the behavior interactions via a machine learning model, demonstrating the heterogeneity in the interactions across users with different perception biases. Altogether, these findings offer more insights on users’ friend count satisfaction, which may provide guidelines to improve the user experience and promote healthy interactions.
Efficient and Effective Similarity Search over Bipartite Graphs
Authors: Renchi Yang
Venue: WWW
Year: 2022

Similarity search over a bipartite graph aims to retrieve from the graph the nodes that are similar to each other, which finds applications in various fields such as online advertising, recommender systems etc. Existing similarity measures either (i) overlook the unique properties of bipartite graphs, or (ii) fail to capture high-order information between nodes accurately, leading to suboptimal result quality. Recently, Hidden Personalized PageRank (HPP) is applied to this problem and found to be more effective compared with prior similarity measures. However, existing solutions for HPP computation incur significant computational costs, rendering it inefficient especially on large graphs. In this paper, we first identify an inherent drawback of HPP and overcome it by proposing bidirectional HPP (BHPP). Then, we formulate similarity search over bipartite graphs as the problem of approximate BHPP computation, and present an efficient solution Approx-BHPP. Specifically, Approx-BHPP offers rigorous theoretical accuracy guarantees with optimal computational complexity by combining deterministic graph traversal with matrix operations in an optimized and non-trivial way. Moreover, our solution achieves significant gain in practical efficiency due to several carefully-designed optimizations. Extensive experiments, comparing BHPP against 8 existing similarity measures over 7 real bipartite graphs, demonstrate the effectiveness of BHPP on query rewriting and item recommendation. Moreover, Approx-BHPP outperforms baseline solutions often by up to orders of magnitude in terms of computational time on both small and large datasets.
A Graph Temporal Information Learning Framework for Popularity Prediction
Authors: Caipiao Yang, Peng Bao, Rong Yan, Jianian Li, Xuanya Li
Venue: WWW
Year: 2022

Effectively predicting the future popularity of online content has important implications in a wide range of areas, including online advertising, user recommendation, and fake news detection. Existing approaches mainly consider the popularity prediction task via path modeling or discrete graph modeling. However, most of them heavily exploit underlying diffusion structural and sequential information, while ignoring the temporal evolution information among different snapshots of cascades. In this paper, we propose a graph temporal information learning framework based on an improved graph convolutional network (GTGCN), which can capture both the temporal information governing the spread of information in a snapshot, and the inherent temporal dependencies among different snapshots. We validate the effectiveness of the GTGCN by applying it on a Sina Weibo dataset in the scenario of predicting retweet cascades. Experimental results demonstrate the superiority of our proposed method over the state-of-the-art approaches.
STAM: A Spatiotemporal Aggregation Method for Graph Neural Network-based Recommendation
Authors: Zhen Yang, Ming Ding, Bin Xu, Hongxia Yang, Jie Tang
Venue: WWW
Year: 2022

Graph neural network-based recommendation systems are blossoming recently, and its core component is aggregation methods that determine neighbor embedding learning. Prior arts usually focus on how to aggregate information from the perspective of spatial structure information, but temporal information about neighbors is left insufficiently explored. In this work, we propose a spatiotemporal aggregation method STAM to efficiently incorporate temporal information into neighbor embedding learning. STAM generates spatiotemporal neighbor embeddings from the perspectives of spatial structure information and temporal information, facilitating the development of aggregation methods from spatial to spatiotemporal. STAM utilizes the Scaled Dot-Product Attention to capture temporal orders of one-hop neighbors and employs multi-head attention to perform joint attention over different latent subspaces. We utilize STAM for GNN-based recommendation to learn users and items embeddings. Extensive experiments demonstrate that STAM brings significant improvements on GNN-based recommendation compared with spatial-based aggregation methods, e.g., 24% for MovieLens, 8% for Amazon, and 13% for Taobao in terms of MRR@20.