OpenRaiser/Intern-Atlas huggingface dataset:
Tracing the Factoids: the Anatomy of Information Re-organization in Wikipedia Articles
Authors: Amit Arjun Verma, Neeru Dubey, S. R. S. Iyengar, Simran Setia
Venue: WWW
Year: 2021

Wikipedia articles are known for their exhaustive knowledge and extensive collaboration. Users perform various tasks that include editing in terms of adding new facts or rectifying some mistakes, looking up new topics, or simply browsing. In this paper, we investigate the impact of gradual edits on the re-positioning and organization of the factual information in Wikipedia articles. Literature shows that in a collaborative system, a set of contributors are responsible for seeking, perceiving, and organizing the information. However, very little is known about the evolution of information organization on Wikipedia articles. Based on our analysis, we show that in a Wikipedia article, the crowd is capable of placing the factual information to its correct position, eventually reducing the knowledge gaps. We also show that the majority of information re-arrangement occurs in the initial stages of the article development and gradually decreases in the later stages. Our findings advance our understanding of the fundamentals of information organization on Wikipedia articles and can have implications for developers aiming to improve the content quality and completeness of Wikipedia articles.
Using Facebook Ads Data to Assess Gender Balance in STEM: Evidence from Brazil
Authors: Carolina C. Vieira, Marisa Vasconcelos
Venue: WWW
Year: 2021

Workforce diversification is essential to increase productivity in any world economy. In the context of the Fourth Industrial Revolution, that need is even more urgent since technological sectors are men-dominated. Despite the significant progress made towards gender inequality in the last decades, we are far from the ideal scenario. Changes towards equality are too slow and uneven across different world regions. Monitoring gender parity is essential to understand priorities and specificities in each world region. However, it is challenging because of the scarcity and the cost to obtain data, especially in less developed countries. In this paper we study how the Facebook Advertising Platform (Facebook Ads) can be used to assess gender imbalance in education, focusing on STEM (Science, Technology, Engineering, and Mathematics) areas, which are the main focus of the Fourth Revolution. As a case study, we apply our methodology to characterize Brazil in terms of gender balance in STEM as well as to correlate the results using Facebook Ads data with official Brazilian government numbers. Our results suggest that even considering a biased population where the majority is female, the proportion of men interested in some majors is higher than the proportion of women. Within STEM areas, we can identify two different patterns. Life Science and Math/Physical Sciences have female dominance, Environmental Science, Technology, and Engineering majors are still concentrated towards men. We also assess the impact of educational level and age on the interest in majors. The gender gap in STEM increases with the women’s educational level and age, as confirmed by official data in Brazil.
Modeling Human Motives and Emotions from Personal Narratives Using External Knowledge And Entity Tracking
Authors: Prashanth Vijayaraghavan, Deb Roy
Venue: WWW
Year: 2021

The ability to automatically understand and infer characters’ motivations and emotional states is key to better narrative comprehension. In this work, we propose a Transformer-based architecture, referred to as , to model characters’ motives and emotions from personal narratives. Towards this goal, we incorporate social commonsense knowledge about the mental states of people related to social events and employ dynamic state tracking of entities using an augmented memory module. Our model learns to produce contextual embeddings and explanations of characters’ mental states by integrating external knowledge along with prior narrative context and mental state encodings. We leverage weakly-annotated personal narratives and knowledge data to train our model and demonstrate its effectiveness on publicly available dataset containing annotations for character mental states. Further, we show that the learned mental state embeddings can be applied in downstream tasks such as empathetic response generation.
Learning Dynamic User Behavior Based on Error-driven Event Representation
Authors: Honglian Wang, Peiyan Li, Wujun Tao, Bailin Feng, Junming Shao
Venue: WWW
Year: 2021

Understanding the evolution of large graphs over time is of significant importance in user behavior understanding and prediction. Modeling user behavior with temporal networks has gained increasing attention in recent years since it allows capturing users’ dynamic preferences and predicting their next actions. Recently, some approaches have been proposed to model user behavior. However, these methods suffer from two problems: they work on static data, which ignores the dynamic evolution, or they model the whole behavior sequences directly by recurrent neural networks and thus suffer from noisy information. To tackle these problems, we propose a dynamic user behavior learning algorithm called LDBR. It views user behaviors as a set of dynamic events and uses recent event embedding to predict future user behavior and infer the current semantic labels. Specifically, we propose a new strategy to automatically learn a good event embedding in behavior sequence by introducing a smooth sampling strategy and minimizing the temporal link prediction error. It is hard to obtain real-world datasets with evolving labels. Thus in this paper, we provide a new dynamic network dataset with evolving labels called Arxiv and make it publicly available. Based on the Arxiv dataset, we conduct a case study to verify the quality of event embedding. Extensive experiments on temporal link prediction tasks further demonstrate the effectiveness of the LDBR model.
Graph Structure Estimation Neural Networks
Authors: Ruijia Wang, Shuai Mou, Xiao Wang, Wanpeng Xiao, Qi Ju, Chuan Shi, Xing Xie
Venue: WWW
Year: 2021

Graph Neural Networks (GNNs) have drawn considerable attention in recent years and achieved outstanding performance in many tasks. Most empirical studies of GNNs assume that the observed graph represents a complete and accurate picture of node relationship. However, this fundamental assumption cannot always be satisfied, since the real-world graphs from complex systems are error-prone and may not be compatible with the properties of GNNs. Therefore, GNNs solely relying on original graph may cause unsatisfactory results, one typical example of which is that GNNs perform well on graphs with homophily while fail on the disassortative situation. In this paper, we propose graph estimation neural networks GEN, which estimates graph structure for GNNs. Specifically, our GEN presents a structure model to fit the mechanism of GNNs by generating graphs with community structure, and an observation model that injects multifaceted observations into calculating the posterior distribution of graphs and is the first to incorporate multi-order neighborhood information. With above two models, the estimation of graph is implemented based on Bayesian inference to maximize the posterior probability, which attains mutual optimization with GNN parameters in an iterative framework. To comprehensively evaluate the performance of GEN, we perform a set of experiments on several benchmark datasets with different homophily and a synthetic dataset, where the experimental results demonstrate the effectiveness of our GEN and rationality of the estimated graph.
FastSNG: The Fastest Social Network Dataset Generator
Authors: Binbin Wang, Chaokun Wang, Hao Feng
Venue: WWW
Year: 2021

Large-scale social networks have become more and more popular with the rapid progress of social media. A number of social network analysis tasks have been developed to conduct on the real large-scale networks. However, the prohibitive cost of achieving the underlying large network, including time cost and data privacy, makes it hard to evaluate the performance of analysis algorithms on real-world social networks. In this paper, we present a tool called FastSNG, which generates heterogeneous social network datasets according to the user-defined configuration depicting the rich characteristics of the expected social network, such as community structures, attributes, and node degree distributions. Moreover, the generation algorithm of FastSNG adopts a degree distribution generation (D2G) model which is efficient to generate web-scale social network datasets. Finally, the tool provides user-friendly and succinct user interfaces for the interaction with general users.
Mixed-Curvature Multi-Relational Graph Neural Network for Knowledge Graph Completion
Authors: Shen Wang, Xiaokai Wei, Cícero Nogueira dos Santos, Zhiguo Wang, Ramesh Nallapati, Andrew O. Arnold, Bing Xiang, Philip S. Yu, Isabel F. Cruz
Venue: WWW
Year: 2021

Knowledge graphs (KGs) have gradually become valuable assets for many AI applications. In a KG, a node denotes an entity, and an edge (or link) denotes a relationship between the entities represented by the nodes. Knowledge graph completion infers and predicts missing edges in a KG automatically. Knowledge graph embeddings have shed light on addressing this task. Recent research embeds KGs in hyperbolic (negatively curved) space instead of conventional Euclidean (zero curved) space and is effective in capturing hierarchical structures. However, as multi-relational graphs, KGs are not structured uniformly and display intrinsic heterogeneous structures. They usually contain rich types of structures, such as hierarchical and cyclic typed structures. Embedding KGs in single-curvature space, such as Euclidean or hyperbolic space, overlooks the intrinsic heterogeneous structures of KGs, and therefore cannot accurately capture their structures. To address this issue, we propose Mixed-Curvature Multi-Relational Graph Neural Network (M2GNN), a generic approach that embeds multi-relational KGs in a mixed-curvature space for knowledge graph completion. Specifically, we define and construct a mixed-curvature space through a product manifold combining multiple single-curvature spaces (e.g., spherical, hyperbolic, or Euclidean) with the purpose of modeling a variety of structures. However, constructing a mixed-curvature space typically requires manually defining the fixed curvatures, which needs domain knowledge and additional data analysis. Improperly defined curvature space also cannot capture the structures of KGs accurately. To address this problem, we set mixed-curvatures as trainable parameters to better capture the underlying structures of the KGs. Furthermore, we propose a Graph Neural Updater by leveraging the heterogeneous relational context in mixed-curvature space to improve the quality of the embedding. Experiments on three KG datasets demonstrate that the proposed M2GNN can outperform its single geometry counterpart as well as state-of-the-art embedding methods on the KG completion task.
A Scalable, Adaptive and Sound Nonconvex Regularizer for Low-rank Matrix Learning
Authors: Yaqing Wang, Quanming Yao, James T. Kwok
Venue: WWW
Year: 2021

Matrix learning is at the core of many machine learning problems. A number of real-world applications such as collaborative filtering and text mining can be formulated as a low-rank matrix completion problems, which recovers incomplete matrix using low-rank assumptions. To ensure that the matrix solution has a low rank, a recent trend is to use nonconvex regularizers that adaptively penalize singular values. They offer good recovery performance and have nice theoretical properties, but are computationally expensive due to repeated access to individual singular values. In this paper, based on the key insight that adaptive shrinkage on singular values improve empirical performance, we propose a new nonconvex low-rank regularizer called ”nuclear norm minus Frobenius norm” regularizer, which is scalable, adaptive and sound. We first show it provably holds the adaptive shrinkage property. Further, we discover its factored form which bypasses the computation of singular values and allows fast optimization by general optimization algorithms. Stable recovery and convergence are guaranteed. Extensive low-rank matrix completion experiments on a number of synthetic and real-world data sets show that the proposed method obtains state-of-the-art recovery performance while being the fastest in comparison to existing low-rank matrix learning methods. 1
It's Not Just the Site, It's the Contents: Intra-domain Fingerprinting Social Media Websites Through CDN Bursts
Authors: Kailong Wang, Junzhe Zhang, Guangdong Bai, Ryan K. L. Ko, Jin Song Dong
Venue: WWW
Year: 2021

The website fingerprinting (or inter-domain WSF), enhanced by various machine learning techniques, has shown its power to identify websites a user has visited. To our best knowledge, a finer-grained problem of web page fingerprinting (or intra-domain WPF) has not been systematically studied by our research community. The WPF attackers, such as government agencies enforcing Internet censorship, are keen to identify the particular web pages (e.g., a political dissident’s social media page) visited by the target user. In this work, we investigate the intra-domain WPF among social media websites, against the realistic on-path passive attack scenario. We reveal that delivering large-size data such as images and videos via Content Delivery Networks (CDNs), which is a common practice in social media websites, makes intra-domain WPF highly feasible. The network traffic generated during rendering a social media page exhibits temporal and volumetric patterns that are sufficiently recognizable by machine learning algorithms. We characterize such patterns as CDN bursts, and use features extracted from them to empower classification algorithms to achieve a high classification accuracy (96%) and a low false positive rate (0.02%).
Chinese Wall or Swiss Cheese? Keyword filtering in the Great Firewall of China
Authors: Zachary Weinberg, Diogo Barradas, Nicolas Christin
Venue: WWW
Year: 2021

The Great Firewall of China (GFW) prevents Chinese citizens from accessing online content deemed objectionable by the Chinese government. One way it does this is to search for forbidden keywords in unencrypted packet streams. When it detects them, it terminates the offending stream by injecting TCP RST packets, and blocks further traffic between the same two hosts for a few minutes. We report on a detailed investigation of the GFW’s application-layer understanding of HTTP. Forbidden keywords are only detected in certain locations within an HTTP request. Requests that contain the English word “search” are inspected for a longer list of forbidden keywords than requests without this word. The firewall can be evaded by bending the rules of the HTTP specification. We observe censorship based on the cleartext TLS Server Name Indication (SNI), but we find no evidence for bulk decryption of HTTPS. We also report on changes since 2014 in the contents of the forbidden keyword list. Over 85% of the forbidden keywords have been replaced since 2014, with the surviving terms referring to perennially sensitive topics. The new keywords refer to recent events and controversies. The GFW’s keyword list is not kept in sync with the blocklists used by Chinese chat clients.
DAPter: Preventing User Data Abuse in Deep Learning Inference Services
Authors: Hao Wu, Xuejin Tian, Yuhang Gong, Xing Su, Minghao Li, Fengyuan Xu
Venue: WWW
Year: 2021

The data abuse issue has risen along with the widespread development of the deep learning inference service (DLIS). Specifically, mobile users worry about their input data being labeled to secretly train new deep learning models that are unrelated to the DLIS they subscribe to. This unique issue, unlike the privacy problem, is about the rights of data owners in the context of deep learning. However, preventing data abuse is demanding when considering the usability and generality in the mobile scenario. In this work, we propose, to our best knowledge, the first data abuse prevention mechanism called DAPter. DAPter is a user-side DLIS-input converter, which removes unnecessary information with respect to the targeted DLIS. The converted input data by DAPter maintains good inference accuracy and is difficult to be labeled manually or automatically for the new model training. DAPter’s conversion is empowered by our lightweight generative model trained with a novel loss function to minimize abusable information in the input data. Furthermore, adapting DAPter requires no change in the existing DLIS backend and models. We conduct comprehensive experiments with our DAPter prototype on mobile devices and demonstrate that DAPter can substantially raise the bar of the data abuse difficulty with little impact on the service quality and overhead.
Tweet Sentiment Analysis of the 2020 U.S. Presidential Election
Authors: Ethan Xia, Han Yue, Hongfu Liu
Venue: WWW
Year: 2021

In this paper, we conducted a tweet sentiment analysis of the 2020 U.S. Presidential Election between Donald Trump and Joe Biden. Specially, we identified the Multi-Layer Perceptron classifier as the methodology with the best performance on the Sanders Twitter benchmark dataset. We collected a sample of over 260,000 tweets related to the 2020 U.S. Presidential Election from the Twitter website via Twitter API, processed feature extraction, and applied Multi-Layer Perceptron to classify these tweets with a positive or negative sentiment. From the results, we concluded that (1) contrary to popular poll results, the candidates had a very close negative to positive sentiment ratio, (2) negative sentiment is more common and prominent than positive sentiment within the social media domain, (3) some key events can be detected by the trends of sentiment on social media, and (4) sentiment analysis can be used as a low-cost and easy alternative to gather political opinion.
Task Offloading Strategy of Internet of Vehicles Based on Stackelberg Game
Authors: Shuo Xiao, Shengzhi Wang, Zhenzhen Huang, Tianyu Wang, Wei Chen, Guopeng Zhang
Venue: WWW
Year: 2021

Moving vehicles generate a large amount of sensor data every second. To ensure automatic driving in a complex driving environment, it needs to fulfill a large amount of data transmission, storage, and processing in a short time. Real-time perception of traffic, target characteristics, and traffic density are important to achieve safe driving and a stable driving experience. However, it is very difficult to adjust the pricing strategy according to the actual demand of the network. In order to analyze the interaction between task vehicle and service vehicle, the Stackelberg game model is introduced. Considering the communication model, calculation model, optimization objectives, and delay constraints, this paper constructs the utility function of service vehicle and task vehicle based on the Stackelberg game model. Based on the utility function, we can obtain the optimal price strategy of service vehicles and the optimal purchase strategy of task vehicles.
Graph Topic Neural Network for Document Representation
Authors: Qianqian Xie, Jimin Huang, Pan Du, Min Peng, Jian-Yun Nie
Venue: WWW
Year: 2021

Graph Neural Networks (GNNs) such as GCN can effectively learn document representations via the semantic relation graph among documents and words. However, despite a few exceptions, most of the previous work in this line of research does not consider the underlying topical semantics inherited in document contents and the relation graph, making the representations less effective and hard to interpret. In a few recent studies trying to incorporate latent topics into GNNs, the topics have been learned independently from the relation graph modeling. Intuitively, topic extraction can benefit much from the information propagation of the relation graph structure - directly and indirectly connected documents and words have similar topics. In this paper, we propose a novel Graph Topic Neural Network (GTNN) model to mine latent topic semantics for interpretable document representation learning, taking into account the document-document, document-word, and word-word relationships in the graph. We also show that our model can be viewed as semi-amortized inference for relational topic model based on Poisson distribution, with high order correlations. We test our model in several settings: unsupervised, semi-supervised, and supervised representation learning, for both connected and unconnected documents. In all the cases, our model outperforms the state-of-the-art models for these tasks.
A Feature Analysis Tool for Batch RL Datasets
Authors: Ruiyang Xu, Zhengxing Chen
Venue: WWW
Year: 2021

Batch RL is concerned about learning a decision policy from a given dataset without interacting with the environment. Although research is actively conducted on learning-related issues (e.g., convergence speed, stability, and safety), empirical challenges before learning are largely ignored. Many RL practitioners face the challenge of determining whether a designed Markov Decision Process (MDP) is valid and meaningful. This study proposes a model-based method to check whether an MDP designed for a given dataset is well formulated through a heuristic-based feature analysis. We tested our method in constructed as well as more realistic environments. Our results show that our approach can identify potential problems of data. As far as we know, performing validity analysis on batch RL data is a novel direction, and we envision that our tool serves as a motivational example to help practitioners apply RL more easily.
Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation Network
Authors: Hongzuo Xu, Yijie Wang, Songlei Jian, Zhenyu Huang, Yongjun Wang, Ning Liu, Fei Li
Venue: WWW
Year: 2021

Outlier detection is an important task in many domains and is intensively studied in the past decade. Further, how to explain outliers, i.e., outlier interpretation, is more significant, which can provide valuable insights for analysts to better understand, solve, and prevent these detected outliers. However, only limited studies consider this problem. Most of the existing methods are based on the score-and-search manner. They select a feature subspace as interpretation per queried outlier by estimating outlying scores of the outlier in searched subspaces. Due to the tremendous searching space, they have to utilize pruning strategies and set a maximum subspace length, often resulting in suboptimal interpretation results. Accordingly, this paper proposes a novel Attention-guided Triplet deviation network for Outlier interpretatioN (ATON). Instead of searching a subspace, ATON directly learns an embedding space and learns how to attach attention to each embedding dimension (i.e., capturing the contribution of each dimension to the outlierness of the queried outlier). Specifically, ATON consists of a feature embedding module and a customized self-attention learning module, which are optimized by a triplet deviation-based loss function. We obtain an optimal attention-guided embedding space with expanded high-level information and rich semantics, and thus outlying behaviors of the queried outlier can be better unfolded. ATON finally distills a subspace of original features from the embedding module and the attention coefficient. With the good generality, ATON can be employed as an additional step of any black-box outlier detector. A comprehensive suite of experiments is conducted to evaluate the effectiveness and efficiency of ATON. The proposed ATON significantly outperforms state-of-the-art competitors on 12 real-world datasets and obtains good scalability w.r.t. both data dimensionality and data size.