Search Results for author: Quoc Viet Hung Nguyen

Found 45 papers, 14 papers with code

Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition

no code implementations22 May 2024 Xinyi Gao, Tong Chen, Wentao Zhang, Junliang Yu, Guanhua Ye, Quoc Viet Hung Nguyen, Hongzhi Yin

The increasing prevalence of large-scale graphs poses a significant challenge for graph neural network training, attributed to their substantial computational requirements.

Robust Federated Contrastive Recommender System against Model Poisoning Attack

no code implementations29 Mar 2024 Wei Yuan, Chaoqun Yang, Liang Qu, Guanhua Ye, Quoc Viet Hung Nguyen, Hongzhi Yin

In this paper, we introduce a novel contrastive learning framework designed to fully leverage the client's sparse data through embedding augmentation, referred to as CL4FedRec.

Contrastive Learning Model Poisoning +2

Adversarial Item Promotion on Visually-Aware Recommender Systems by Guided Diffusion

no code implementations25 Dec 2023 Lijian Chen, Wei Yuan, Tong Chen, Guanhua Ye, Quoc Viet Hung Nguyen, Hongzhi Yin

Visually-aware recommender systems have found widespread application in domains where visual elements significantly contribute to the inference of users' potential preferences.

Recommendation Systems

Hide Your Model: A Parameter Transmission-free Federated Recommender System

1 code implementation25 Nov 2023 Wei Yuan, Chaoqun Yang, Liang Qu, Quoc Viet Hung Nguyen, JianXin Li, Hongzhi Yin

Existing FedRecs generally adhere to a learning protocol in which a central server shares a global recommendation model with clients, and participants achieve collaborative learning by frequently communicating the model's public parameters.

Privacy Preserving Recommendation Systems

Budgeted Embedding Table For Recommender Systems

no code implementations23 Oct 2023 Yunke Qu, Tong Chen, Quoc Viet Hung Nguyen, Hongzhi Yin

Experiments have shown state-of-the-art performance on two real-world datasets when BET is paired with three popular recommender models under different memory budgets.

Recommendation Systems Representation Learning

Accelerating Scalable Graph Neural Network Inference with Node-Adaptive Propagation

no code implementations17 Oct 2023 Xinyi Gao, Wentao Zhang, Junliang Yu, Yingxia Shao, Quoc Viet Hung Nguyen, Bin Cui, Hongzhi Yin

To further accelerate Scalable GNNs inference in this inductive setting, we propose an online propagation framework and two novel node-adaptive propagation methods that can customize the optimal propagation depth for each node based on its topological information and thereby avoid redundant feature propagation.

Learning Compact Compositional Embeddings via Regularized Pruning for Recommendation

1 code implementation7 Sep 2023 Xurong Liang, Tong Chen, Quoc Viet Hung Nguyen, JianXin Li, Hongzhi Yin

In addition, we innovatively design a regularized pruning mechanism in CERP, such that the two sparsified meta-embedding tables are encouraged to encode information that is mutually complementary.

Recommendation Systems

Heterogeneous Decentralized Machine Unlearning with Seed Model Distillation

no code implementations25 Aug 2023 Guanhua Ye, Tong Chen, Quoc Viet Hung Nguyen, Hongzhi Yin

As some recent information security legislation endowed users with unconditional rights to be forgotten by any trained machine learning model, personalized IoT service providers have to put unlearning functionality into their consideration.

Machine Unlearning

Graph Condensation for Inductive Node Representation Learning

no code implementations29 Jul 2023 Xinyi Gao, Tong Chen, Yilong Zang, Wentao Zhang, Quoc Viet Hung Nguyen, Kai Zheng, Hongzhi Yin

To overcome this issue, we propose mapping-aware graph condensation (MCond), explicitly learning the one-to-many node mapping from original nodes to synthetic nodes to seamlessly integrate new nodes into the synthetic graph for inductive representation learning.

Representation Learning

Towards Few-shot Inductive Link Prediction on Knowledge Graphs: A Relational Anonymous Walk-guided Neural Process Approach

1 code implementation26 Jun 2023 Zicheng Zhao, Linhao Luo, Shirui Pan, Quoc Viet Hung Nguyen, Chen Gong

Previous methods are limited to transductive scenarios, where entities exist in the knowledge graphs, so they are unable to handle unseen entities.

Inductive Link Prediction Knowledge Graphs

Do as I can, not as I get

no code implementations17 Jun 2023 Shangfei Zheng, Hongzhi Yin, Tong Chen, Quoc Viet Hung Nguyen, Wei Chen, Lei Zhao

This paper proposes a model called TMR to mine valuable information from simulated data environments.

Knowledge Graphs Multi-modal Knowledge Graph +1

Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data

1 code implementation NeurIPS 2023 Xin Zheng, Miao Zhang, Chunyang Chen, Quoc Viet Hung Nguyen, Xingquan Zhu, Shirui Pan

Specifically, SFGC contains two collaborative components: (1) a training trajectory meta-matching scheme for effectively synthesizing small-scale graph-free data; (2) a graph neural feature score metric for dynamically evaluating the quality of the condensed data.

Graph Learning

Manipulating Visually-aware Federated Recommender Systems and Its Countermeasures

no code implementations14 May 2023 Wei Yuan, Shilong Yuan, Chaoqun Yang, Quoc Viet Hung Nguyen, Hongzhi Yin

Therefore, when incorporating visual information in FedRecs, all existing model poisoning attacks' effectiveness becomes questionable.

Collaborative Filtering Model Poisoning +2

Explicit Knowledge Graph Reasoning for Conversational Recommendation

no code implementations1 May 2023 Xuhui Ren, Tong Chen, Quoc Viet Hung Nguyen, Lizhen Cui, Zi Huang, Hongzhi Yin

Recent conversational recommender systems (CRSs) tackle those limitations by enabling recommender systems to interact with the user to obtain her/his current preference through a sequence of clarifying questions.

Attribute Recommendation Systems

Joint Semantic and Structural Representation Learning for Enhancing User Preference Modelling

no code implementations24 Apr 2023 Xuhui Ren, Wei Yuan, Tong Chen, Chaoqun Yang, Quoc Viet Hung Nguyen, Hongzhi Yin

Knowledge graphs (KGs) have become important auxiliary information for helping recommender systems obtain a good understanding of user preferences.

Knowledge Graphs Language Modelling +2

DREAM: Adaptive Reinforcement Learning based on Attention Mechanism for Temporal Knowledge Graph Reasoning

no code implementations8 Apr 2023 Shangfei Zheng, Hongzhi Yin, Tong Chen, Quoc Viet Hung Nguyen, Wei Chen, Lei Zhao

Although existing TKG reasoning methods have the ability to predict missing future events, they fail to generate explicit reasoning paths and lack explainability.

Knowledge Graphs Missing Elements +3

Manipulating Federated Recommender Systems: Poisoning with Synthetic Users and Its Countermeasures

no code implementations6 Apr 2023 Wei Yuan, Quoc Viet Hung Nguyen, Tieke He, Liang Chen, Hongzhi Yin

To reveal the real vulnerability of FedRecs, in this paper, we present a new poisoning attack method to manipulate target items' ranks and exposure rates effectively in the top-$K$ recommendation without relying on any prior knowledge.

Privacy Preserving Recommendation Systems

TinyAD: Memory-efficient anomaly detection for time series data in Industrial IoT

no code implementations7 Mar 2023 Yuting Sun, Tong Chen, Quoc Viet Hung Nguyen, Hongzhi Yin

With the prevalent deployment of the Industrial Internet of Things (IIoT), an enormous amount of time series data is collected to facilitate machine learning models for anomaly detection, and it is of the utmost importance to directly deploy the trained models on the IIoT devices.

Anomaly Detection Time Series +1

Semi-decentralized Federated Ego Graph Learning for Recommendation

no code implementations10 Feb 2023 Liang Qu, Ningzhi Tang, Ruiqi Zheng, Quoc Viet Hung Nguyen, Zi Huang, Yuhui Shi, Hongzhi Yin

In light of this, we propose a semi-decentralized federated ego graph learning framework for on-device recommendations, named SemiDFEGL, which introduces new device-to-device collaborations to improve scalability and reduce communication costs and innovatively utilizes predicted interacted item nodes to connect isolated ego graphs to augment local subgraphs such that the high-order user-item collaborative information could be used in a privacy-preserving manner.

Collaborative Filtering Graph Learning +2

Interaction-level Membership Inference Attack Against Federated Recommender Systems

no code implementations26 Jan 2023 Wei Yuan, Chaoqun Yang, Quoc Viet Hung Nguyen, Lizhen Cui, Tieke He, Hongzhi Yin

An interaction-level membership inference attacker is first designed, and then the classical privacy protection mechanism, Local Differential Privacy (LDP), is adopted to defend against the membership inference attack.

Attribute Federated Learning +3

Efficient Graph Neural Network Inference at Large Scale

no code implementations1 Nov 2022 Xinyi Gao, Wentao Zhang, Yingxia Shao, Quoc Viet Hung Nguyen, Bin Cui, Hongzhi Yin

Graph neural networks (GNNs) have demonstrated excellent performance in a wide range of applications.

Efficient On-Device Session-Based Recommendation

1 code implementation27 Sep 2022 Xin Xia, Junliang Yu, Qinyong Wang, Chaoqun Yang, Quoc Viet Hung Nguyen, Hongzhi Yin

Specifically, each item is represented by a compositional code that consists of several codewords, and we learn embedding vectors to represent each codeword instead of each item.

Knowledge Distillation Model Compression +1

A Survey of Machine Unlearning

1 code implementation6 Sep 2022 Thanh Tam Nguyen, Thanh Trung Huynh, Phi Le Nguyen, Alan Wee-Chung Liew, Hongzhi Yin, Quoc Viet Hung Nguyen

Specifically, as a category collection of cutting-edge studies, the intention behind this article is to serve as a comprehensive resource for researchers and practitioners seeking an introduction to machine unlearning and its formulations, design criteria, removal requests, algorithms, and applications.

Attribute Machine Unlearning

Model-Agnostic and Diverse Explanations for Streaming Rumour Graphs

no code implementations17 Jul 2022 Thanh Tam Nguyen, Thanh Cong Phan, Minh Hieu Nguyen, Matthias Weidlich, Hongzhi Yin, Jun Jo, Quoc Viet Hung Nguyen

Since the spread of rumours in social media is commonly modelled using feature-annotated graphs, we propose a query-by-example approach that, given a rumour graph, extracts the $k$ most similar and diverse subgraphs from past rumours.

Graph Representation Learning Rumour Detection

Time-aware Dynamic Graph Embedding for Asynchronous Structural Evolution

no code implementations1 Jul 2022 Yu Yang, Hongzhi Yin, Jiannong Cao, Tong Chen, Quoc Viet Hung Nguyen, Xiaofang Zhou, Lei Chen

Meanwhile, we treat each edge sequence as a whole and embed its ToV of the first vertex to further encode the time-sensitive information.

Dynamic graph embedding Graph Mining

Detecting Rumours with Latency Guarantees using Massive Streaming Data

no code implementations13 May 2022 Thanh Tam Nguyen, Thanh Trung Huynh, Hongzhi Yin, Matthias Weidlich, Thanh Thi Nguyen, Thai Son Mai, Quoc Viet Hung Nguyen

Today's social networks continuously generate massive streams of data, which provide a valuable starting point for the detection of rumours as soon as they start to propagate.

Rumour Detection

Thinking inside The Box: Learning Hypercube Representations for Group Recommendation

1 code implementation6 Apr 2022 Tong Chen, Hongzhi Yin, Jing Long, Quoc Viet Hung Nguyen, Yang Wang, Meng Wang

Such user and group preferences are commonly represented as points in the vector space (i. e., embeddings), where multiple user embeddings are compressed into one to facilitate ranking for group-item pairs.

Decision Making

Personalized On-Device E-health Analytics with Decentralized Block Coordinate Descent

no code implementations17 Dec 2021 Guanhua Ye, Hongzhi Yin, Tong Chen, Miao Xu, Quoc Viet Hung Nguyen, Jiangning Song

Actuated by the growing attention to personal healthcare and the pandemic, the popularity of E-health is proliferating.

Benchmarking Fairness +1

Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation

1 code implementation16 Dec 2021 Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Lizhen Cui, Quoc Viet Hung Nguyen

Contrastive learning (CL) recently has spurred a fruitful line of research in the field of recommendation, since its ability to extract self-supervised signals from the raw data is well-aligned with recommender systems' needs for tackling the data sparsity issue.

Contrastive Learning Recommendation Systems

A War Beyond Deepfake: Benchmarking Facial Counterfeits and Countermeasures

1 code implementation25 Nov 2021 Minh Tam Pham, Thanh Trung Huynh, Van Vinh Tong, Thanh Tam Nguyen, Thanh Thi Nguyen, Hongzhi Yin, Quoc Viet Hung Nguyen

In recent years, visual forgery has reached a level of sophistication that humans cannot identify fraud, which poses a significant threat to information security.

Benchmarking DeepFake Detection +2

PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotion

no code implementations21 Oct 2021 Shijie Zhang, Hongzhi Yin, Tong Chen, Zi Huang, Quoc Viet Hung Nguyen, Lizhen Cui

Evaluations on two real-world datasets show that 1) our attack model significantly boosts the exposure rate of the target item in a stealthy way, without harming the accuracy of the poisoned recommender; and 2) existing defenses are not effective enough, highlighting the need for new defenses against our local model poisoning attacks to federated recommender systems.

Federated Learning Model Poisoning +1

DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation

no code implementations7 May 2021 Lei Guo, Li Tang, Tong Chen, Lei Zhu, Quoc Viet Hung Nguyen, Hongzhi Yin

Shared-account Cross-domain Sequential recommendation (SCSR) is the task of recommending the next item based on a sequence of recorded user behaviors, where multiple users share a single account, and their behaviours are available in multiple domains.

Sequential Recommendation Transfer Learning

Entity Alignment for Knowledge Graphs with Multi-order Convolutional Networks

1 code implementation17 Nov 2020 Tam Thanh Nguyen, Thanh Trung Huynh, Hongzhi Yin, Vinh Van Tong, Darnbi Sakong, Bolong Zheng, Quoc Viet Hung Nguyen

Knowledge graphs (KGs) have become popular structures for unifying real-world entities by modelling the relationships between them and their attributes.

Ranked #7 on Entity Alignment on DBP15k zh-en (using extra training data)

Attribute Entity Alignment +1

Sequence-Aware Factorization Machines for Temporal Predictive Analytics

no code implementations7 Nov 2019 Tong Chen, Hongzhi Yin, Quoc Viet Hung Nguyen, Wen-Chih Peng, Xue Li, Xiaofang Zhou

As a widely adopted solution, models based on Factorization Machines (FMs) are capable of modelling high-order interactions among features for effective sparse predictive analytics.

Recommendation Systems

Deep Learning for Deepfakes Creation and Detection: A Survey

no code implementations25 Sep 2019 Thanh Thi Nguyen, Quoc Viet Hung Nguyen, Dung Tien Nguyen, Duc Thanh Nguyen, Thien Huynh-The, Saeid Nahavandi, Thanh Tam Nguyen, Quoc-Viet Pham, Cuong M. Nguyen

By reviewing the background of deepfakes and state-of-the-art deepfake detection methods, this study provides a comprehensive overview of deepfake techniques and facilitates the development of new and more robust methods to deal with the increasingly challenging deepfakes.

DeepFake Detection Face Swapping

Unsupervised Learning of Node Embeddings by Detecting Communities

no code implementations25 Sep 2019 Chi Thang Duong, Dung Hoang, Truong Giang Le Ba, Thanh Le Cong, Hongzhi Yin, Matthias Weidlich, Quoc Viet Hung Nguyen, Karl Aberer

We provide empirical evidence that the communities learned by DMC are meaningful and that the node embeddings are competitive in different node classification benchmarks.

Clustering Node Classification +1

Parallel Computation of Graph Embeddings

no code implementations6 Sep 2019 Chi Thang Duong, Hongzhi Yin, Thanh Dat Hoang, Truong Giang Le Ba, Matthias Weidlich, Quoc Viet Hung Nguyen, Karl Aberer

We therefore propose a framework for parallel computation of a graph embedding using a cluster of compute nodes with resource constraints.

Graph Embedding

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