OpenRaiser/Intern-Atlas huggingface dataset:
Meta-Generator Enhanced Multi-Domain Recommendation
Authors: Yingyi Zhang, Xianneng Li, Yahe Yu, Jian Tang, Huanfang Deng, Junya Lu, Yeyin Zhang, Qiancheng Jiang, Yunsen Xian, Liqian Yu, Han Liu
Venue: WWW
Year: 2023

Large-scale e-commercial platforms usually contain multiple business fields, which require industrial algorithms to characterize user intents across multiple domains. Numerous efforts have been made in user multi-domain intent modeling to achieve state-of-the-art performance. However, existing methods mainly focus on the domains having rich user information, which makes implementation to domains with sparse or rare user behavior meet with mixed success. Hence, in this paper, we propose a novel method named Meta-generator enhanced multi-Domain model (MetaDomain) to address the above issue. MetaDomain mainly includes two steps, 1) users’ multi-domain intent representation and 2) users’ multi-domain intent fusion. Specifically, in users’ multi-domain intent representation, we use the gradient information from a domain intent extractor to train the domain intent meta-generator, where the domain intent extractor has the input of users’ sequence feature and domain meta-generator has the input of users’ basic feature, hence the capability of generating users’ intent with sparse behavior. Afterward, in users’ multi-domain intent fusion, a domain graph is used to represent the high-order multi-domain connectivity. Extensive experiments have been carried out under a real-world industrial platform named Meituan. Both offline and rigorous online A/B tests under the billion-level data scale demonstrate the superiority of the proposed MetaDomain method over the state-of-the-art baselines. Furthermore comparing with the method using multi-domain sequence features, MetaDomain can reduce the serving latency by 20%. Currently, MetaDomain has been deployed in Meituan one of the largest worldwide Online-to-Offline(O2O) platforms.
RSGNN: A Model-agnostic Approach for Enhancing the Robustness of Signed Graph Neural Networks
Authors: Zeyu Zhang, Jiamou Liu, Xianda Zheng, Yifei Wang, Pengqian Han, Yupan Wang, Kaiqi Zhao, Zijian Zhang
Venue: WWW
Year: 2023

Signed graphs model complex relations using both positive and negative edges. Signed graph neural networks (SGNN) are powerful tools to analyze signed graphs. We address the vulnerability of SGNN to potential edge noise in the input graph. Our goal is to strengthen existing SGNN allowing them to withstand edge noises by extracting robust representations for signed graphs. First, we analyze the expressiveness of SGNN using an extended Weisfeiler-Lehman (WL) graph isomorphism test and identify the limitations to SGNN over triangles that are unbalanced. Then, we design some structure-based regularizers to be used in conjunction with an SGNN that highlight intrinsic properties of a signed graph. The tools and insights above allow us to propose a novel framework, Robust Signed Graph Neural Network (RSGNN), which adopts a dual architecture that simultaneously denoises the graph while learning node representations. We validate the performance of our model empirically on four real-world signed graph datasets, i.e., Bitcoin_OTC, Bitcoin_Alpha, Epinion and Slashdot, RSGNN can clearly improve the robustness of popular SGNN models. When the signed graphs are affected by random noise, our method outperforms baselines by up to 9.35% Binary-F1 for link sign prediction. Our implementation is available in PyTorch1.
Towards Model Robustness: Generating Contextual Counterfactuals for Entities in Relation Extraction
Authors: Mi Zhang, Tieyun Qian, Ting Zhang, Xin Miao
Venue: WWW
Year: 2023

The goal of relation extraction (RE) is to extract the semantic relations between/among entities in the text. As a fundamental task in information systems, it is crucial to ensure the robustness of RE models. Despite the high accuracy current deep neural models have achieved in RE tasks, they are easily affected by spurious correlations. One solution to this problem is to train the model with counterfactually augmented data (CAD) such that it can learn the causation rather than the confounding. However, no attempt has been made on generating counterfactuals for RE tasks. In this paper, we formulate the problem of automatically generating CAD for RE tasks from an entity-centric viewpoint, and develop a novel approach to derive contextual counterfactuals for entities. Specifically, we exploit two elementary topological properties, i.e., the centrality and the shortest path, in syntactic and semantic dependency graphs, to first identify and then intervene on the contextual causal features for entities. We conduct a comprehensive evaluation on four RE datasets by combining our proposed approach with a variety of RE backbones. Results prove that our approach not only improves the performance of the backbones but also makes them more robust in the out-of-domain test 1.
Multitask Peer Prediction With Task-dependent Strategies
Authors: Yichi Zhang, Grant Schoenebeck
Venue: WWW
Year: 2023

Peer prediction aims to incentivize truthful reports from agents whose reports cannot be assessed with any objective ground truthful information. In the multi-task setting where each agent is asked multiple questions, a sequence of mechanisms have been proposed which are truthful — truth-telling is guaranteed to be an equilibrium, or even better, informed truthful — truth-telling is guaranteed to be one of the best-paid equilibria. However, these guarantees assume agents’ strategies are restricted to be task-independent: an agent’s report on a task is not affected by her information about other tasks. We provide the first discussion on how to design (informed) truthful mechanisms for task-dependent strategies, which allows the agents to report based on all her information on the assigned tasks. We call such stronger mechanisms (informed) omni-truthful. In particular, we propose the joint-disjoint task framework, a new paradigm which builds upon the previous penalty-bonus task framework. First, we show a natural reduction from mechanisms in the penalty-bonus task framework to mechanisms in the joint-disjoint task framework that maps every truthful mechanism to an omni-truthful mechanism. Such a reduction is non-trivial as we show that current penalty-bonus task mechanisms are not, in general, omni-truthful. Second, for a stronger truthful guarantee, we design the matching agreement (MA) mechanism which is informed omni-truthful. Finally, for the MA mechanism in the detail-free setting where no prior knowledge is assumed, we show how many tasks are required to (approximately) retain the truthful guarantees.
Dynalogue: A Transformer-Based Dialogue System with Dynamic Attention
Authors: Rongjunchen Zhang, Tingmin Wu, Xiao Chen, Sheng Wen, Surya Nepal, Cécile Paris, Yang Xiang
Venue: WWW
Year: 2023

Businesses face a range of cyber risks, both external threats and internal vulnerabilities that continue to evolve over time. As cyber attacks continue to increase in complexity and sophistication, more organisations will experience them. For this reason, it is important that organisations seek timely consultancy from cyber professionals so that they can respond to and recover from cyber attacks as quickly as possible. However, huge surges in cyber attacks have long left cyber professionals short of what is required to cover the security needs. This problem is getting worse when an increasing number of people choose to work from home during the pandemic because this situation usually yields extra communication cost. In this paper, we propose to develop a cybersecurity-oriented dialogue system, called Dynalogue1, which can provide consultancy online as a cyber professional. For the first time, Dynalogue provides a promising solution to mitigate the need for cyber professionals via automatically generating problem-targeted conversions to victims of cyber attacks. In spite of many dialogue systems developed in the past, Dynalogue provides a distinct capability of handling long and complicated sentences that are common in cybersecurity-related conversations. It is challenging to have this capability because limited memory in dialogue systems can be hard to accommodate sufficient key information of long sentences. To overcome this challenge, Dynalogue utilises an attention mechanism that dynamically captures key semantics within a sentence instead of using fix window to cut off the sentence. To evaluate Dynalogue, we collect 67K real-world conversations (0.6M utterances) from Bleeping Computer2, which is one of the most popular cybersecurity consultancy websites in the world. The results suggest that Dynalogue outperforms all the existing dialogue systems with 1% ∼ 9% improvements on all different metrics. We further run Dynalogue on the public dataset WikiHow to validate its compatibility in other domains where conversations are also long and complicated. Dynalogue also outperforms all the other methods with at most 2.4% improvement.
Task-Specific Data Augmentation for Zero-shot and Few-shot Stance Detection
Authors: Jiarui Zhang, Shaojuan Wu, Xiaowang Zhang, Zhiyong Feng
Venue: WWW
Year: 2023

Various targets keep coming up on social media, and most of them lack labeled data. In this paper, we focus on zero-shot and few-shot stance detection, which aims to identify stances with few or even no training instances. In order to solve the lack of labeled data and implicit stance expression, we propose a self-supervised data augment approach based on coreference resolution. The method is specific for stance detection to generate more stable data and reduce the variance within and between classes to achieve a balance between validity and robustness. Considering the diversity of comments, we propose a novel multi-task stance detection framework of target-related fragment extraction and stance detection, which can enhance attention on target-related fragments and reduce the noise of other fragments. Experiments show that the proposed approach achieves state-of-the-art performance in zero-shot and few-shot stance detection.
Learning Long- and Short-term Representations for Temporal Knowledge Graph Reasoning
Authors: Mengqi Zhang, Yuwei Xia, Qiang Liu, Shu Wu, Liang Wang
Venue: WWW
Year: 2023

Temporal Knowledge graph (TKG) reasoning aims to predict missing facts based on historical TKG data. Most of the existing methods are incapable of explicitly modeling the long-term time dependencies from history and neglect the adaptive integration of the long- and short-term information. To tackle these problems, we propose a novel method that utilizes a designed Hierarchical Relational Graph Neural Network to learn the Long- and Short-term representations for TKG reasoning, namely HGLS. Specifically, to explicitly associate entities in different timestamps, we first transform the TKG into a global graph. Based on the built graph, we design a Hierarchical Relational Graph Neural Network that executes in two levels: The sub-graph level is to capture the semantic dependencies within concurrent facts of each KG. And the global-graph level aims to model the temporal dependencies between entities. Furthermore, we design a module to extract the long- and short-term information from the output of these two levels. Finally, the long- and short-term representations are fused into a unified one by Gating Integration for entity prediction. Extensive experiments on four datasets demonstrate the effectiveness of HGLS.
An Attentional Multi-scale Co-evolving Model for Dynamic Link Prediction
Authors: Guozhen Zhang, Tian Ye, Depeng Jin, Yong Li
Venue: WWW
Year: 2023

Dynamic link prediction is essential for a wide range of domains, including social networks, bioinformatics, knowledge bases, and recommender systems. Existing works have demonstrated that structural information and temporal information are two of the most important information for this problem. However, existing works either focus on modeling them independently or modeling the temporal dynamics of a single structural scale, neglecting the complex correlations among them. This paper proposes to model the inherent correlations among the evolving dynamics of different structural scales for dynamic link prediction. Following this idea, we propose an Attentional Multi-scale Co-evolving Network (AMCNet). Specifically, We model multi-scale structural information by a motif-based graph neural network with multi-scale pooling. Then, we design a hierarchical attention-based sequence-to-sequence model for learning the complex correlations among the evolution dynamics of different structural scales. Extensive experiments on four real-world datasets with different characteristics demonstrate that AMCNet significantly outperforms the state-of-the-art in both single-step and multi-step dynamic link prediction tasks.
TFE-GNN: A Temporal Fusion Encoder Using Graph Neural Networks for Fine-grained Encrypted Traffic Classification
Authors: Haozhen Zhang, Le Yu, Xi Xiao, Qing Li, Francesco Mercaldo, Xiapu Luo, Qixu Liu
Venue: WWW
Year: 2023

Encrypted traffic classification is receiving widespread attention from researchers and industrial companies. However, the existing methods only extract flow-level features, failing to handle short flows because of unreliable statistical properties, or treat the header and payload equally, failing to mine the potential correlation between bytes. Therefore, in this paper, we propose a byte-level traffic graph construction approach based on point-wise mutual information (PMI), and a model named Temporal Fusion Encoder using Graph Neural Networks (TFE-GNN) for feature extraction. In particular, we design a dual embedding layer, a GNN-based traffic graph encoder as well as a cross-gated feature fusion mechanism, which can first embed the header and payload bytes separately and then fuses them together to obtain a stronger feature representation. The experimental results on two real datasets demonstrate that TFE-GNN outperforms multiple state-of-the-art methods in fine-grained encrypted traffic classification tasks.
The Chameleon on the Web: an Empirical Study of the Insidious Proactive Web Defacements
Authors: Rui Zhao
Venue: WWW
Year: 2023

Web defacement is one of the major promotional channels for online underground economies. It regularly compromises benign websites and injects fraudulent content to promote illicit goods and services. It inflicts significant harm to websites’ reputations and revenues and may lead to legal ramifications. In this paper, we uncover proactive web defacements, where the involved web pages (i.e., landing pages) proactively deface themselves within browsers using JavaScript (i.e., control scripts). Proactive web defacements have not yet received attention from research communities, anti-hacking organizations, or law-enforcement officials. To detect proactive web defacements, we designed a practical tool, PACTOR. It runs in the browser and intercepts JavaScript API calls that manipulate web page content. It takes snapshots of the rendered HTML source code immediately before and after the intercepted API calls and detects proactive web defacements by visually comparing every two consecutive snapshots. Our two-month empirical study, using PACTOR, on 2,454 incidents of proactive web defacements shows that they can evade existing URL safety-checking tools and effectively promote the ranking of their landing pages using legitimate content/keywords. We also investigated the vendor network of proactive web defacements and reported all the involved domains to law-enforcement officials and URL-safety checking tools.
Are Mobile Advertisements in Compliance with App's Age Group?
Authors: Yanjie Zhao, Tianming Liu, Haoyu Wang, Yepang Liu, John C. Grundy, Li Li
Venue: WWW
Year: 2023

As smartphones and mobile apps permeate every aspect of people’s lives, children are accessing mobile devices at an increasingly younger age. The inescapable exposure of advertisements in mobile apps to children has grown alarmingly. Mobile advertisements are placed by advertisers and subsequently distributed by ad SDKs, under the rare control of app developers and app markets’ content ratings. Indeed, content that is objectionable and harmful to children’s mental health has been reported to appear in advertising, such as pornography. However, few studies have yet concentrated on automatically and comprehensively identifying such kid-unsuitable mobile advertising. In this paper, we first characterize the regulations for mobile ads relating to children. We then propose our novel automated dynamic analysis framework, named AdRambler, that attempts to collect ad content throughout the lifespan of mobile ads and identify their inappropriateness for child app users. Using AdRambler, we conduct a large-scale (25,000 mobile apps) empirical investigation and reveal the non-incidental presence of inappropriate ads in apps with child-included target audiences. We collected 11,270 ad views and identified 1,289 ad violations (from 775 apps) of child user regulations, with roughly half of the app promotions not in compliance with host apps’ content ratings. Our finding indicates that even certified ad SDKs could still propagate inappropriate advertisements. We further delve into the question of accountability for the presence of inappropriate advertising and provide concrete suggestions for all stakeholders to take action for the benefit of children.
mStore: Schema Mining based-RDF Data Storage
Authors: Guopeng Zheng, Tenglong Ren, Lulu Yang, Xiaowang Zhang, Zhiyong Feng
Venue: WWW
Year: 2023

The relationship-based approach is an efficient solution strategy for distributed RDF data management. The schema of tables can directly affect the system’s storage efficiency and query performance. Most current approaches are based on fixed schema(e.g., VP, ExtVP). When facing large-scale RDF datasets and complex SPARQL queries requiring many joins, such methods suffer from problems such as long pre-processing time and poor query performance. Schemas with Pareto Optimality between the system’s space consumption and query efficiency are needed but also hard to find. Therefore, we propose mStore, a prototype system with flexible schemas based on schema mining. The intuition behind our approach is that we want to divide the combinations of predicates with higher relevance into the same schema, which can replace costly joins with low-cost selects, improving the query performance. The results show that our system performs better on complex query workloads while reducing the pre-processing time overhead compared to systems with fixed schema partitioning strategies.
Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs
Authors: Xin Zheng, Miao Zhang, Chunyang Chen, Qin Zhang, Chuan Zhou, Shirui Pan
Venue: WWW
Year: 2023

Graph neural architecture search (NAS) has gained popularity in automatically designing powerful graph neural networks (GNNs) with relieving human efforts. However, existing graph NAS methods mainly work under the homophily assumption and overlook another important graph property, i.e., heterophily, which exists widely in various real-world applications. To date, automated heterophilic graph learning with NAS is still a research blank to be filled in. Due to the complexity and variety of heterophilic graphs, the critical challenge of heterophilic graph NAS mainly lies in developing the heterophily-specific search space and strategy. Therefore, in this paper, we propose a novel automated graph neural network on heterophilic graphs, namely Auto-HeG, to automatically build heterophilic GNN models with expressive learning abilities. Specifically, Auto-HeG incorporates heterophily into all stages of automatic heterophilic graph learning, including search space design, supernet training, and architecture selection. Through the diverse message-passing scheme with joint micro-level and macro-level designs, we first build a comprehensive heterophilic GNN search space, enabling Auto-HeG to integrate complex and various heterophily of graphs. With a progressive supernet training strategy, we dynamically shrink the initial search space according to layer-wise variation of heterophily, resulting in a compact and efficient supernet. Taking a heterophily-aware distance criterion as the guidance, we conduct heterophilic architecture selection in the leave-one-out pattern, so that specialized and expressive heterophilic GNN architectures can be derived. Extensive experiments illustrate the superiority of Auto-HeG in developing excellent heterophilic GNNs to human-designed models and graph NAS models.
Multi-Aspect Heterogeneous Graph Augmentation
Authors: Yuchen Zhou, Yanan Cao, Yongchao Liu, Yanmin Shang, Peng Zhang, Zheng Lin, Yun Yue, Baokun Wang, Xing Fu, Weiqiang Wang
Venue: WWW
Year: 2023

Data augmentation has been widely studied as it can be used to improve the generalizability of graph representation learning models. However, existing works focus only on the data augmentation on homogeneous graphs. Data augmentation for heterogeneous graphs remains under-explored. Considering that heterogeneous graphs contain different types of nodes and links, ignoring the type information and directly applying the data augmentation methods of homogeneous graphs to heterogeneous graphs will lead to suboptimal results. In this paper, we propose a novel Multi-Aspect Heterogeneous Graph Augmentation framework named MAHGA. Specifically, MAHGA consists of two core augmentation strategies: structure-level augmentation and metapath-level augmentation. Structure-level augmentation pays attention to network schema aspect and designs a relation-aware conditional variational auto-encoder that can generate synthetic features of neighbors to augment the nodes and the node types with scarce links. Metapath-level augmentation concentrates on metapath aspect, which constructs metapath reachable graphs for different metapaths and estimates the graphons of them. By sampling and mixing up based on the graphons, MAHGA yields intra-metapath and inter-metapath augmentation. Finally, we conduct extensive experiments on multiple benchmarks to validate the effectiveness of MAHGA. Experimental results demonstrate that our method improves the performances across a set of heterogeneous graph learning models and datasets.
A Practical Rule Learning Framework for Risk Management
Authors: Jun Zhou, Meng Li, Lu Yu, Longfei Li, Fei Wu
Venue: WWW
Year: 2023

Identifying the fraud risk of applications on the web platform is a critical challenge with both requirements of effectiveness and interpretability. In these high-stakes web applications especially in financial scenarios, decision rules have been extensively used due to the rising requirements for explainable artificial intelligence (XAI). In this work, we develop a rule learning framework with rule mining and rule refining modules for addressing the learning efficiency and class imbalance issues while making the decision rules more broadly and simply applicable to risk management scenarios. On four benchmark data-sets and two large-scale data-sets, the classification performance, interpretability, and scalability of the framework have been proved, achieving at least a 26.2% relative improvement over the state-of-the-art (SOTA) models. The system is currently being used by hundreds of millions of users and dealing with an enormous number of transactions in Ant Group, which is one of the largest mobile payment platforms in the world.