OpenRaiser/Intern-Atlas huggingface dataset:
Anti-FakeU: Defending Shilling Attacks on Graph Neural Network based Recommender Model
Authors: Xiaoyu You, Chi Li, Daizong Ding, Mi Zhang, Fuli Feng, Xudong Pan, Min Yang
Venue: WWW
Year: 2023

Graph neural network (GNN) based recommendation models are observed to be more vulnerable against carefully-designed malicious records injected into the system, i.e., shilling attacks, which manipulate the recommendation to common users and therefore impair user trust. In this paper, we for the first time conduct a systematic study on the vulnerability of GNN based recommendation model against the shilling attack. With the aid of theoretical analysis, we attribute the root cause of the vulnerability to its neighborhood aggregation mechanism, which could make the negative impact of attacks propagate rapidly in the system. To restore the robustness of GNN based recommendation model, the key factor lies in detecting malicious records in the system and preventing the propagation of misinformation. To this end, we construct a user-user graph to capture the patterns of malicious behaviors and design a novel GNN based detector to identify fake users. Furthermore, we develop a data augmentation strategy and a joint learning paradigm to train the recommender model and the proposed detector. Extensive experiments on benchmark datasets validate the enhanced robustness of the proposed method in resisting various types of shilling attacks and identifying fake users, e.g., our proposed method fully mitigating the impact of popularity attacks on target items up to , and improving the accuracy of detecting fake users on the Gowalla dataset by .
MaSS: Model-agnostic, Semantic and Stealthy Data Poisoning Attack on Knowledge Graph Embedding
Authors: Xiaoyu You, Beina Sheng, Daizong Ding, Mi Zhang, Xudong Pan, Min Yang, Fuli Feng
Venue: WWW
Year: 2023

Open-source knowledge graphs are attracting increasing attention. Nevertheless, the openness also raises the concern of data poisoning attacks, that is, the attacker could submit malicious facts to bias the prediction of knowledge graph embedding (KGE) models. Existing studies on such attacks adopt a clear-box setting and neglect the semantic information of the generated facts, making them fail to attack in real-world scenarios. In this work, we consider a more rigorous setting and propose a model-agnostic, semantic, and stealthy data poisoning attack on KGE models from a practical perspective. The main design of our work is to inject indicative paths to make the infected model predict certain malicious facts. With the aid of the proposed opaque-box path injection theory, we theoretically reveal that the attack success rate under the opaque-box setting is determined by the plausibility of triplets on the indicative path. Based on this, we develop a novel and efficient algorithm to search paths that maximize the attack goal, satisfy certain semantic constraints, and preserve certain stealthiness, i.e., the normal functionality of the target KGE will not be influenced although it predicts wrong facts given certain queries. Through extensive evaluation of benchmark datasets and 6 typical knowledge graph embedding models as the victims, we validate the effectiveness in terms of attack success rate (ASR) under opaque-box setting and stealthiness. For example, on FB15k-237, our attack achieves a ASR on DeepPath, with an average ASR over when attacking various KGE models under the opaque-box setting.
What Image do You Need? A Two-stage Framework for Image Selection in E-commerce
Authors: Sheng You, Chao Wang, Baohua Wu, Jingping Liu, Quan Lu, Guanzhou Han, Yanghua Xiao
Venue: WWW
Year: 2023

In e-commerce, images are widely used to display more intuitive information about items. Image selection significantly affects the user’s click-through rate (CTR). Most existing work considers the CTR as the target to find an appropriate image. However, these methods are challenging to deploy online efficiently. Also, the selected images may not relate to the item but are profitable to CTR, resulting in the undesirable phenomenon of enticing users to click on the item. To address these issues, we propose a novel two-stage pipeline method with content-based recall model and CTR-based ranking model. The first is realized as a joint method based on the title-image matching model and multi-modal knowledge graph embedding learning model. The second is a CTR-based visually aware scoring model, incorporating the entity textual information and entity images. Experimental results show the effectiveness and efficiency of our method in offline evaluations. After a month of online A/B testing on a travel platform Fliggy, the relative improvement of our method is 5% with respect to seller selection on CTCVR in the searching scenario, and our method further improves pCTR from 3.48% of human pick to 3.53% in the recommendation scenario.
Human-in-the-loop Regular Expression Extraction for Single Column Format Inconsistency
Authors: Shaochen Yu, Lei Han, Marta Indulska, Shazia Sadiq, Gianluca Demartini
Venue: WWW
Year: 2023

Format inconsistency is one of the most frequently appearing data quality issues encountered during data cleaning. Existing automated approaches commonly lack applicability and generalisability, while approaches with human inputs typically require specialized skills such as writing regular expressions. This paper proposes a novel hybrid human-machine system, namely “Data-Scanner-4C”, which leverages crowdsourcing to address syntactic format inconsistencies in a single column effectively. We first ask crowd workers to create examples from single-column data through “data selection” and “result validation” tasks. Then, we propose and use a novel rule-based learning algorithm to infer the regular expressions that propagate formats from created examples to the entire column. Our system integrates crowdsourcing and algorithmic format extraction techniques in a single workflow. Having human experts write regular expressions is no longer required, thereby reducing both the time as well as the opportunity for error. We conducted experiments through both synthetic and real-world datasets, and our results show how the proposed approach is applicable and effective across data types and formats.
Sedition Hunters: A Quantitative Study of the Crowdsourced Investigation into the 2021 U.S. Capitol Attack
Authors: Tianjiao Yu, Sukrit Venkatagiri, Ismini Lourentzou, Kurt Luther
Venue: WWW
Year: 2023

Social media platforms have enabled extremists to organize violent events, such as the 2021 U.S. Capitol Attack. Simultaneously, these platforms enable professional investigators and amateur sleuths to collaboratively collect and identify imagery of suspects with the goal of holding them accountable for their actions. Through a case study of Sedition Hunters, a Twitter community whose goal is to identify individuals who participated in the 2021 U.S. Capitol Attack, we explore what are the main topics or targets of the community, who participates in the community, and how. Using topic modeling, we find that information sharing is the main focus of the community. We also note an increase in awareness of privacy concerns. Furthermore, using social network analysis, we show how some participants played important roles in the community. Finally, we discuss implications for the content and structure of online crowdsourced investigations.
PipeEdge: A Trusted Pipelining Collaborative Edge Training based on Blockchain
Authors: Liang Yuan, Qiang He, Feifei Chen, Ruihan Dou, Hai Jin, Yun Yang
Venue: WWW
Year: 2023

Powered by the massive data generated by the blossom of mobile and Web-of-Things (WoT) devices, Deep Neural Networks (DNNs) have developed both in accuracy and size in recent years. Conventional cloud-based DNN training incurs rapidly-increasing data and model transmission overheads as well as privacy issues. Mobile edge computing (MEC) provides a promising solution by facilitating DNN model training on edge servers at the network edge. However, edge servers often suffer from constrained resources and need to collaborate on DNN training. Unfortunately, managed by different telecoms, edge servers cannot properly collaborate with each other without incentives and trust. In this paper, we introduce PipeEdge, a scheme that promotes collaborative edge training between edge servers by introducing incentives and trust based on blockchain. Under the PipeEdge scheme, edge servers can hire trustworthy workers for pipelined DNN training tasks based on model parallelism. We implement PipeEdge and evaluate it comprehensively with four different DNN models. The results show that it outperforms state-of-the-art schemes by up to 173.98% with negligible overheads.
Contrastive Fine-tuning on Few Shot Intent Detection with Topological Intent Tree
Authors: Wei Yuan, Martin Dimkovski, Aijun An
Venue: WWW
Year: 2023

We present a few-shot intent detection model for an enterprise’s conversational dialogue system. The model uses an intent topological tree to guide the search for the user intent using large language models (LLMs). The intents are resolved based on semantic similarities between user utterances and the text descriptions of the internal nodes of the intent tree or the intent examples in the leaf nodes of the tree. Our results show that an off-the-shelf language model can work reasonably well in a large enterprise deployment without fine-tuning, and its performance can be further improved with fine-tuning as more domain-specific data becomes available. We also show that the fine-tuned language model meets and outperforms the state-of-the-art (SOTA) results in resolving conversation intents without training classifiers. With the use of a topological intent tree, our model provides more interpretability to cultivate people’s trust in their decisions.
Identifying Topic and Cause for Sarcasm: An Unsupervised Knowledge-enhanced Prompt Method
Authors: Minjie Yuan, Qiudan Li, Xue Mao, Daniel Dajun Zeng
Venue: WWW
Year: 2023

Sarcasm is usually emotional and topical. Mining the characteristics of sarcasm semantics in different emotional tendencies and topic expressions helps gain insight into the sarcasm cause. Most of the existing work detect sarcasm or topic label based on a supervised learning framework, which requires heavy data annotation work. To overcome the above challenges, inspired by the multi-task learning framework, this paper proposes an unsupervised knowledge-enhanced prompt method. This method uses the similarity interaction mechanism to mine the hidden relationship between the sarcasm cause and topic, which integrates external knowledge, such as syntax and emotion, into the prompting and generation process. Additionally, it identifies the sarcasm cause and topic simultaneously. Experimental results on a real-world dataset verify the effectiveness of the proposed model.
Mental Health Coping Stories on Social Media: A Causal-Inference Study of Papageno Effect
Authors: Yunhao Yuan, Koustuv Saha, Barbara Keller, Erkki Tapio Isometsä, Talayeh Aledavood
Venue: WWW
Year: 2023

The Papageno effect concerns how media can play a positive role in preventing and mitigating suicidal ideation and behaviors. With the increasing ubiquity and widespread use of social media, individuals often express and share lived experiences and struggles with mental health. However, there is a gap in our understanding about the existence and effectiveness of the Papageno effect in social media, which we study in this paper. In particular, we adopt a causal-inference framework to examine the impact of exposure to mental health coping stories on individuals on Twitter. We obtain a Twitter dataset with ∼ 2M posts by ∼ 10K individuals. We consider engaging with coping stories as the Treatment intervention, and adopt a stratified propensity score approach to find matched cohorts of Treatment and Control individuals. We measure the psychosocial shifts in affective, behavioral, and cognitive outcomes in longitudinal Twitter data before and after engaging with the coping stories. Our findings reveal that, engaging with coping stories leads to decreased stress and depression, and improved expressive writing, diversity, and interactivity. Our work discusses the practical and platform design implications in supporting mental wellbeing.
OntoEval: an Automated Ontology Evaluation System
Authors: Antonio Zaitoun, Tomer Sagi, Katja Hose
Venue: WWW
Year: 2023

Developing semantically-aware web services requires comprehensive and accurate ontologies. Evaluating an existing ontology or adapting it is a labor-intensive and complex task for which no automated tools exist. Nevertheless, in this paper we propose a tool that aims at making this vision come true, i.e., we present a tool for the automated evaluation of ontologies that allows one to rapidly assess an ontology’s coverage of a domain and identify specific problems in the ontology’s structure. The tool evaluates the domain coverage and correctness of parent-child relations of a given ontology based on domain information derived from a text corpus representing the domain. The tool provides both overall statistics and detailed analysis of sub-graphs of the ontology. In the demo, we show how these features can be used for the iterative improvement of an ontology.
Automated Ontology Evaluation: Evaluating Coverage and Correctness using a Domain Corpus
Authors: Antonio Zaitoun, Tomer Sagi, Katja Hose
Venue: WWW
Year: 2023

Ontologies conceptualize domains and are a crucial part of web semantics and information systems. However, re-using an existing ontology for a new task requires a detailed evaluation of the candidate ontology as it may cover only a subset of the domain concepts, contain information that is redundant or misleading, and have inaccurate relations and hierarchies between concepts. Manual evaluation of large and complex ontologies is a tedious task. Thus, a few approaches have been proposed for automated evaluation, ranging from concept coverage to ontology generation from a corpus. Existing approaches, however, are limited by their dependence on external structured knowledge sources, such as a thesaurus, as well as by their inability to evaluate semantic relationships. In this paper, we propose a novel framework to automatically evaluate the domain coverage and semantic correctness of existing ontologies based on domain information derived from text. The approach uses a domain-tuned named-entity-recognition model to extract phrasal concepts. The extracted concepts are then used as a representation of the domain against which we evaluate the candidate ontology’s concepts. We further employ a domain-tuned language model to determine the semantic correctness of the candidate ontology’s relations. We demonstrate our automated approach on several large ontologies from the oceanographic domain and show its agreement with a manual evaluation by domain experts and its superiority over the state-of-the-art.
PARROT: Position-Aware Regularized Optimal Transport for Network Alignment
Authors: Zhichen Zeng, Si Zhang, Yinglong Xia, Hanghang Tong
Venue: WWW
Year: 2023

Network alignment is a critical steppingstone behind a variety of multi-network mining tasks. Most of the existing methods essentially optimize a Frobenius-like distance or ranking-based loss, ignoring the underlying geometry of graph data. Optimal transport (OT), together with Wasserstein distance, has emerged to be a powerful approach accounting for the underlying geometry explicitly. Promising as it might be, the state-of-the-art OT-based alignment methods suffer from two fundamental limitations, including (1) effectiveness due to the insufficient use of topology and consistency information and (2) scalability due to the non-convex formulation and repeated computationally costly loss calculation. In this paper, we propose a position-aware regularized optimal transport framework for network alignment named PARROT. To tackle the effectiveness issue, the proposed PARROT captures topology information by random walk with restart, with three carefully designed consistency regularization terms. To tackle the scalability issue, the regularized OT problem is decomposed into a series of convex subproblems and can be efficiently solved by the proposed constrained proximal point method with guaranteed convergence. Extensive experiments show that our algorithm achieves significant improvements in both effectiveness and scalability, outperforming the state-of-the-art network alignment methods and speeding up existing OT-based methods by up to 100 times.
PaGE-Link: Path-based Graph Neural Network Explanation for Heterogeneous Link Prediction
Authors: Shichang Zhang, Jiani Zhang, Xiang Song, Soji Adeshina, Da Zheng, Christos Faloutsos, Yizhou Sun
Venue: WWW
Year: 2023

Transparency and accountability have become major concerns for black-box machine learning (ML) models. Proper explanations for the model behavior increase model transparency and help researchers develop more accountable models. Graph neural networks (GNN) have recently shown superior performance in many graph ML problems than traditional methods, and explaining them has attracted increased interest. However, GNN explanation for link prediction (LP) is lacking in the literature. LP is an essential GNN task and corresponds to web applications like recommendation and sponsored search on web. Given existing GNN explanation methods only address node/graph-level tasks, we propose Path-based GNN Explanation for heterogeneous Link prediction (PaGE-Link) that generates explanations with connection interpretability, enjoys model scalability, and handles graph heterogeneity. Qualitatively, PaGE-Link can generate explanations as paths connecting a node pair, which naturally captures connections between the two nodes and easily transfer to human-interpretable explanations. Quantitatively, explanations generated by PaGE-Link improve AUC for recommendation on citation and user-item graphs by 9 - 35% and are chosen as better by 78.79% of responses in human evaluation.
Sustainable Grain Transportation in Ukraine Amidst War Utilizing KNARM and KnowWhereGraph
Authors: Yinglun Zhang, Antonina Broyaka, Jude Kastens, Allen M. Featherstone, Cogan Shimizu, Pascal Hitzler, Hande Küçük-McGinty
Venue: WWW
Year: 2023

In this work, we propose a sustainable path-finding application for grain transportation during the ongoing Russian military invasion in Ukraine. This application is to build a suite of algorithms to find possible optimal paths for transporting grain that remains in Ukraine. The application uses the KNowledge Acquisition and Representation Methodology(KNARM) and the KnowWhereGraph to achieve this goal. Currently, we are working towards creating an ontology that will allow for a more effective heuristic approach by incorporating the lessons learned from the KnowWhereGraph. The aim is to enhance the path-finding process and provide more accurate and efficient results. In the future, we will continue exploring and implementing new techniques that can further improve the sustainability of the path-finding applications with a knowledge graph backend for grain transportation through hazardous and adversarial environments. The code is available upon reviewer’s request. It can not be made public due to the sensitive nature of the data.
Time-manipulation Attack: Breaking Fairness against Proof of Authority Aura
Authors: Xinrui Zhang, Rujia Li, Qin Wang, Qi Wang, Sisi Duan
Venue: WWW
Year: 2023

As blockchain-based commercial projects and startups flourish, efficiency becomes one of the critical metrics in designing blockchain systems. Due to its high efficiency, Proof of Authority (PoA) Aura has become one of the most widely adopted consensus solutions for blockchains. Our research finds over 4,000 projects have used Aura and its variants. In this paper, we provide a rigorous analysis of Aura. We propose three types of time-manipulation attacks, where a malicious leader simply needs to modify the timestamp in its proposed block or delay it to extract extra benefits. These attacks can easily break the legal leader election, thus directly harming the fairness of the block proposal. We apply our attacks to a mature Aura project called OpenEthereum. By repeatedly conducting our attacks1 over 15 days, we find that an adversary can gain on average 200% mining rewards of their fair shares. Furthermore, such attacks can even indirectly break the finality of blocks and the safety of the system. Based on the deployment of Aura as of September 2022, the potentially affected market cap is up to 2.13 billion USD. As a by-product, we further discuss solutions to mitigate such issues and report our observations to official teams.
China's First Natural Language-based AI ChatBot Trader
Authors: James Y. Zhang, Zhi Li, Hao Fang, Jun Wu, Zhongnan Shen, Jing Zheng, Wei Chu, Weiping Duan, Peng Xu
Venue: WWW
Year: 2023

Repo (repurchase agreement) trading provides easy access to short-term financing secured by a pledge of collateral and plays an important role in the global financial system. However, repo traders face many tough challenges in their job, from managing complex financial transactions to keeping up with changing market trends and regulations in the complex financial transactions involved. Besides the difficult and tedious processes that take a lot of time and energy, repo traders need to keep up to date with various laws, regulations, and financial trends that may affect their job, worsened by the exposure to a variety of market risks. As the leader of the FinTech industry, Ant Group launched a new initiative to alleviate the affliction of the repo traders at MyBank1. By leveraging many existing platform technologies, such as AI ChatBot and forecasting platforms, and with the collective work of various engineering groups, we are able to create a ChatBot that communicates with other human traders in natural language and create electronic contracts based on the negotiated terms, equipped with proper trading strategies based on forecasting results. The fully automatic workflow not only frees our trader from tedious routines, but also reduces potential human errors. At the same time, it enables refined portfolio and risk management, while opening up the possibility to apply neural network-based trading strategies, and yielding greater returns comparing to traditional workflow reliant on human experiences. Our system has evolved beyond just providing services to our own traders, to now a fully commercialized product, covering other types of interbank trading.