Search Results for author: Bryan Hooi

Found 149 papers, 93 papers with code

ChineseHarm-Bench: A Chinese Harmful Content Detection Benchmark

1 code implementation12 Jun 2025 Kangwei Liu, Siyuan Cheng, Bozhong Tian, Xiaozhuan Liang, Yuyang Yin, Meng Han, Ningyu Zhang, Bryan Hooi, Xi Chen, Shumin Deng

In addition, we propose a knowledge-augmented baseline that integrates both human-annotated knowledge rules and implicit knowledge from large language models, enabling smaller models to achieve performance comparable to state-of-the-art LLMs.

VPI-Bench: Visual Prompt Injection Attacks for Computer-Use Agents

1 code implementation3 Jun 2025 Tri Cao, Bennett Lim, Yue Liu, Yuan Sui, Yuexin Li, Shumin Deng, Lin Lu, Nay Oo, Shuicheng Yan, Bryan Hooi

Each test case is a variant of a web platform, designed to be interactive, deployed in a realistic environment, and containing a visually embedded malicious prompt.

MLR-Bench: Evaluating AI Agents on Open-Ended Machine Learning Research

1 code implementation26 May 2025 Hui Chen, Miao Xiong, Yujie Lu, Wei Han, Ailin Deng, Yufei He, Jiaying Wu, Yibo Li, Yue Liu, Bryan Hooi

Recent advancements in AI agents have demonstrated their growing potential to drive and support scientific discovery.

scientific discovery

Efficient Reasoning via Chain of Unconscious Thought

1 code implementation26 May 2025 Ruihan Gong, Yue Liu, Wenjie Qu, Mingzhe Du, Yufei He, Yingwei Ma, Yulin Chen, Xiang Liu, Yi Wen, Xinfeng Li, Ruidong Wang, Xinzhong Zhu, Bryan Hooi, Jiaheng Zhang

Inspired by UTT, we propose a new reasoning paradigm, termed Chain of Unconscious Thought (CoUT), to improve the token efficiency of LRMs by guiding them to mimic human unconscious thought and internalize reasoning processes.

Adapting Precomputed Features for Efficient Graph Condensation

1 code implementation ICML 2025 Yuan Li, Jun Hu, Zemin Liu, Bryan Hooi, Jia Chen, Bingsheng He

To address this, Graph Condensation (GC) methods aim to compress large graphs into smaller, synthetic ones that are more manageable for GNN training.

Diversity

Safety in Large Reasoning Models: A Survey

no code implementations24 Apr 2025 Cheng Wang, Yue Liu, Baolong Bi, Duzhen Zhang, Zhongzhi Li, Junfeng Fang, Bryan Hooi

Large Reasoning Models (LRMs) have exhibited extraordinary prowess in tasks like mathematics and coding, leveraging their advanced reasoning capabilities.

Survey

Guiding VLM Agents with Process Rewards at Inference Time for GUI Navigation

no code implementations22 Apr 2025 Zhiyuan Hu, Shiyun Xiong, Yifan Zhang, See-Kiong Ng, Anh Tuan Luu, Bo An, Shuicheng Yan, Bryan Hooi

Recent advancements in visual language models (VLMs) have notably enhanced their capabilities in handling complex Graphical User Interface (GUI) interaction tasks.

FlowReasoner: Reinforcing Query-Level Meta-Agents

1 code implementation21 Apr 2025 Hongcheng Gao, Yue Liu, Yufei He, Longxu Dou, Chao Du, Zhijie Deng, Bryan Hooi, Min Lin, Tianyu Pang

This paper proposes a query-level meta-agent named FlowReasoner to automate the design of query-level multi-agent systems, i. e., one system per user query.

Reinforcement Learning (RL)

Geneshift: Impact of different scenario shift on Jailbreaking LLM

no code implementations10 Apr 2025 Tianyi Wu, Zhiwei Xue, Yue Liu, Jiaheng Zhang, Bryan Hooi, See-Kiong Ng

Despite achieving the promising attack success rate using dictionary-based evaluation, existing jailbreak attack methods fail to output detailed contents to satisfy the harmful request, leading to poor performance on GPT-based evaluation.

Do "New Snow Tablets" Contain Snow? Large Language Models Over-Rely on Names to Identify Ingredients of Chinese Drugs

1 code implementation3 Apr 2025 Sifan Li, Yujun Cai, Bryan Hooi, Nanyun Peng, Yiwei Wang

Traditional Chinese Medicine (TCM) has seen increasing adoption in healthcare, with specialized Large Language Models (LLMs) emerging to support clinical applications.

RAG Retrieval-augmented Generation

Text Speaks Louder than Vision: ASCII Art Reveals Textual Biases in Vision-Language Models

no code implementations2 Apr 2025 Zhaochen Wang, Yujun Cai, Zi Huang, Bryan Hooi, Yiwei Wang, Ming-Hsuan Yang

Vision-language models (VLMs) have advanced rapidly in processing multimodal information, but their ability to reconcile conflicting signals across modalities remains underexplored.

Prompt Engineering

How does Watermarking Affect Visual Language Models in Document Understanding?

no code implementations1 Apr 2025 Chunxue Xu, Yiwei Wang, Bryan Hooi, Yujun Cai, Songze Li

Visual Language Models (VLMs) have become foundational models for document understanding tasks, widely used in the processing of complex multimodal documents across domains such as finance, law, and academia.

document understanding

JudgeLRM: Large Reasoning Models as a Judge

no code implementations31 Mar 2025 Nuo Chen, Zhiyuan Hu, Qingyun Zou, Jiaying Wu, Qian Wang, Bryan Hooi, Bingsheng He

The rise of Large Language Models (LLMs) as evaluators offers a scalable alternative to human annotation, yet existing Supervised Fine-Tuning (SFT) for judges approaches often fall short in domains requiring complex reasoning.

Reinforcement Learning (RL)

Efficient Inference for Large Reasoning Models: A Survey

1 code implementation29 Mar 2025 Yue Liu, Jiaying Wu, Yufei He, Hongcheng Gao, Hongyu Chen, Baolong Bi, Jiaheng Zhang, Zhiqi Huang, Bryan Hooi

Large Reasoning Models (LRMs) significantly improve the reasoning ability of Large Language Models (LLMs) by learning to reason, exhibiting promising performance in complex task-solving.

Survey

Tricking Retrievers with Influential Tokens: An Efficient Black-Box Corpus Poisoning Attack

no code implementations27 Mar 2025 Cheng Wang, Yiwei Wang, Yujun Cai, Bryan Hooi

Retrieval-augmented generation (RAG) systems enhance large language models by incorporating external knowledge, addressing issues like outdated internal knowledge and hallucination.

Hallucination RAG +2

Process or Result? Manipulated Ending Tokens Can Mislead Reasoning LLMs to Ignore the Correct Reasoning Steps

no code implementations25 Mar 2025 Yu Cui, Bryan Hooi, Yujun Cai, Yiwei Wang

Recent reasoning large language models (LLMs) have demonstrated remarkable improvements in mathematical reasoning capabilities through long Chain-of-Thought.

Mathematical Reasoning

RGL: A Graph-Centric, Modular Framework for Efficient Retrieval-Augmented Generation on Graphs

1 code implementation25 Mar 2025 Yuan Li, Jun Hu, Jiaxin Jiang, Zemin Liu, Bryan Hooi, Bingsheng He

Recent advances in graph learning have paved the way for innovative retrieval-augmented generation (RAG) systems that leverage the inherent relational structures in graph data.

Abstract generation +5

MIRAGE: Multimodal Immersive Reasoning and Guided Exploration for Red-Team Jailbreak Attacks

no code implementations24 Mar 2025 Wenhao You, Bryan Hooi, Yiwei Wang, Youke Wang, Zong Ke, Ming-Hsuan Yang, Zi Huang, Yujun Cai

While safety mechanisms have significantly progressed in filtering harmful text inputs, MLLMs remain vulnerable to multimodal jailbreaks that exploit their cross-modal reasoning capabilities.

Visual Storytelling

Making Every Step Effective: Jailbreaking Large Vision-Language Models Through Hierarchical KV Equalization

no code implementations14 Mar 2025 Shuyang Hao, Yiwei Wang, Bryan Hooi, Jun Liu, Muhao Chen, Zi Huang, Yujun Cai

However, we identify a critical limitation: not every adversarial optimization step leads to a positive outcome, and indiscriminately accepting optimization results at each step may reduce the overall attack success rate.

Red Teaming

Words or Vision: Do Vision-Language Models Have Blind Faith in Text?

1 code implementation CVPR 2025 Ailin Deng, Tri Cao, Zhirui Chen, Bryan Hooi

Vision-Language Models (VLMs) excel in integrating visual and textual information for vision-centric tasks, but their handling of inconsistencies between modalities is underexplored.

Language Modeling Language Modelling +1

Navigating the Helpfulness-Truthfulness Trade-Off with Uncertainty-Aware Instruction Fine-Tuning

no code implementations17 Feb 2025 Tianyi Wu, Jingwei Ni, Bryan Hooi, Jiaheng Zhang, Elliott Ash, See-Kiong Ng, Mrinmaya Sachan, Markus Leippold

Instruction Fine-tuning (IFT) can enhance the helpfulness of Large Language Models (LLMs), but it may lower their truthfulness.

ReLearn: Unlearning via Learning for Large Language Models

1 code implementation16 Feb 2025 Haoming Xu, Ningyuan Zhao, Liming Yang, Sendong Zhao, Shumin Deng, Mengru Wang, Bryan Hooi, Nay Oo, Huajun Chen, Ningyu Zhang

Current unlearning methods for large language models usually rely on reverse optimization to reduce target token probabilities.

Data Augmentation Text Generation

Enhancing LLM Character-Level Manipulation via Divide and Conquer

no code implementations12 Feb 2025 Zhen Xiong, Yujun Cai, Bryan Hooi, Nanyun Peng, Zhecheng Li, Yiwei Wang

Large Language Models (LLMs) have demonstrated strong generalization capabilities across a wide range of natural language processing (NLP) tasks.

Code Generation

Lost in Edits? A $λ$-Compass for AIGC Provenance

no code implementations5 Feb 2025 Wenhao You, Bryan Hooi, Yiwei Wang, Euijin Choo, Ming-Hsuan Yang, Junsong Yuan, Zi Huang, Yujun Cai

Recent advancements in diffusion models have driven the growth of text-guided image editing tools, enabling precise and iterative modifications of synthesized content.

text-guided-image-editing

UniGraph2: Learning a Unified Embedding Space to Bind Multimodal Graphs

1 code implementation2 Feb 2025 Yufei He, Yuan Sui, Xiaoxin He, Yue Liu, Yifei Sun, Bryan Hooi

Multimodal graphs (MMGs) represent such graphs where each node is associated with features from different modalities, while the edges capture the relationships between these entities.

Graph Neural Network Mixture-of-Experts +2

CHiP: Cross-modal Hierarchical Direct Preference Optimization for Multimodal LLMs

1 code implementation28 Jan 2025 Jinlan Fu, Shenzhen Huangfu, Hao Fei, Xiaoyu Shen, Bryan Hooi, Xipeng Qiu, See-Kiong Ng

To address these challenges, in this work, we propose a Cross-modal Hierarchical Direct Preference Optimization (CHiP) to address these limitations.

Hallucination

Spatio-Temporal Foundation Models: Vision, Challenges, and Opportunities

no code implementations15 Jan 2025 Adam Goodge, Wee Siong Ng, Bryan Hooi, See Kiong Ng

Foundation models have revolutionized artificial intelligence, setting new benchmarks in performance and enabling transformative capabilities across a wide range of vision and language tasks.

Monte Carlo Tree Search for Comprehensive Exploration in LLM-Based Automatic Heuristic Design

1 code implementation15 Jan 2025 Zhi Zheng, Zhuoliang Xie, Zhenkun Wang, Bryan Hooi

Handcrafting heuristics for solving complex optimization tasks (e. g., route planning and task allocation) is a common practice but requires extensive domain knowledge.

Combinatorial Optimization Language Modeling +2

Modality-Independent Graph Neural Networks with Global Transformers for Multimodal Recommendation

1 code implementation18 Dec 2024 Jun Hu, Bryan Hooi, Bingsheng He, Yinwei Wei

Our results indicate that the optimal $K$ for certain modalities on specific datasets can be as low as 1 or 2, which may restrict the GNNs' capacity to capture global information.

Graph Learning Multi-modal Recommendation +1

Exploring Visual Vulnerabilities via Multi-Loss Adversarial Search for Jailbreaking Vision-Language Models

no code implementations CVPR 2025 Shuyang Hao, Bryan Hooi, Jun Liu, Kai-Wei Chang, Zi Huang, Yujun Cai

Despite inheriting security measures from underlying language models, Vision-Language Models (VLMs) may still be vulnerable to safety alignment issues.

Image Generation Safety Alignment

DRS: Deep Question Reformulation With Structured Output

1 code implementation27 Nov 2024 Zhecheng Li, Yiwei Wang, Bryan Hooi, Yujun Cai, Nanyun Peng, Kai-Wei Chang

Recent studies reveal that while LLMs can detect unanswerable questions, they struggle to assist users in reformulating these questions.

Question Answering

Are Anomaly Scores Telling the Whole Story? A Benchmark for Multilevel Anomaly Detection

no code implementations21 Nov 2024 Tri Cao, Minh-Huy Trinh, Ailin Deng, Quoc-Nam Nguyen, Khoa Duong, Ngai-Man Cheung, Bryan Hooi

However, existing models primarily operate in a binary setting, and the anomaly scores they produce are usually based on the deviation of data points from normal data, which may not accurately reflect practical severity.

Anomaly Detection

Partitioning Message Passing for Graph Fraud Detection

no code implementations16 Nov 2024 Wei Zhuo, Zemin Liu, Bryan Hooi, Bingsheng He, Guang Tan, Rizal Fathony, Jia Chen

Label imbalance and homophily-heterophily mixture are the fundamental problems encountered when applying Graph Neural Networks (GNNs) to Graph Fraud Detection (GFD) tasks.

Fraud Detection Inductive Bias

Think Carefully and Check Again! Meta-Generation Unlocking LLMs for Low-Resource Cross-Lingual Summarization

no code implementations26 Oct 2024 Zhecheng Li, Yiwei Wang, Bryan Hooi, Yujun Cai, Naifan Cheung, Nanyun Peng, Kai-Wei Chang

To resolve this question, we fully explore the potential of large language models on cross-lingual summarization task for low-resource languages through our four-step zero-shot method: Summarization, Improvement, Translation and Refinement (SITR) with correspondingly designed prompts.

Vulnerability of LLMs to Vertically Aligned Text Manipulations

no code implementations26 Oct 2024 Zhecheng Li, Yiwei Wang, Bryan Hooi, Yujun Cai, Zhen Xiong, Nanyun Peng, Kai-Wei Chang

Text classification involves categorizing a given text, such as determining its sentiment or identifying harmful content.

Few-Shot Learning text-classification +1

Can Knowledge Graphs Make Large Language Models More Trustworthy? An Empirical Study over Open-ended Question Answering

no code implementations10 Oct 2024 Yuan Sui, Yufei He, Zifeng Ding, Bryan Hooi

Recent works integrating Knowledge Graphs (KGs) have led to promising improvements in enhancing reasoning accuracy of Large Language Models (LLMs).

Hallucination Knowledge Graphs +1

FlipAttack: Jailbreak LLMs via Flipping

2 code implementations2 Oct 2024 Yue Liu, Xiaoxin He, Miao Xiong, Jinlan Fu, Shumin Deng, Bryan Hooi

Second, we verify the strong ability of LLMs to perform the text-flipping task, and then develop 4 variants to guide LLMs to denoise, understand, and execute harmful behaviors accurately.

ID$^3$: Identity-Preserving-yet-Diversified Diffusion Models for Synthetic Face Recognition

no code implementations26 Sep 2024 Shen Li, Jianqing Xu, Jiaying Wu, Miao Xiong, Ailin Deng, Jiazhen Ji, Yuge Huang, Wenjie Feng, Shouhong Ding, Bryan Hooi

This equivalence motivates an ID-preserving sampling algorithm, which operates over an adjusted gradient vector field, enabling the generation of fake face recognition datasets that approximate the distribution of real-world faces.

Diversity Image Generation +2

Con-ReCall: Detecting Pre-training Data in LLMs via Contrastive Decoding

1 code implementation5 Sep 2024 Cheng Wang, Yiwei Wang, Bryan Hooi, Yujun Cai, Nanyun Peng, Kai-Wei Chang

The training data in large language models is key to their success, but it also presents privacy and security risks, as it may contain sensitive information.

LongRecipe: Recipe for Efficient Long Context Generalization in Large Language Models

1 code implementation31 Aug 2024 Zhiyuan Hu, Yuliang Liu, Jinman Zhao, Suyuchen Wang, Yan Wang, Wei Shen, Qing Gu, Anh Tuan Luu, See-Kiong Ng, Zhiwei Jiang, Bryan Hooi

Large language models (LLMs) face significant challenges in handling long-context tasks because of their limited effective context window size during pretraining, which restricts their ability to generalize over extended sequences.

8k

Multimodal Large Language Models for Phishing Webpage Detection and Identification

1 code implementation12 Aug 2024 Jehyun Lee, Peiyuan Lim, Bryan Hooi, Dinil Mon Divakaran

In this work, we take steps to study the efficacy of large language models (LLMs), in particular the multimodal LLMs, in detecting phishing webpages.

When Text and Images Don't Mix: Bias-Correcting Language-Image Similarity Scores for Anomaly Detection

no code implementations24 Jul 2024 Adam Goodge, Bryan Hooi, Wee Siong Ng

Contrastive Language-Image Pre-training (CLIP) achieves remarkable performance in various downstream tasks through the alignment of image and text input embeddings and holds great promise for anomaly detection.

Anomaly Detection

Generalizing Graph Transformers Across Diverse Graphs and Tasks via Pre-Training on Industrial-Scale Data

no code implementations4 Jul 2024 Yufei He, Zhenyu Hou, Yukuo Cen, Feng He, Xu Cheng, Bryan Hooi

Extensive experiments have demonstrated that our framework can perform pre-training on real-world web-scale graphs with over 540 million nodes and 12 billion edges and generalizes effectively to unseen new graphs with different downstream tasks.

Decoder

FiDeLiS: Faithful Reasoning in Large Language Model for Knowledge Graph Question Answering

no code implementations22 May 2024 Yuan Sui, Yufei He, Nian Liu, Xiaoxin He, Kun Wang, Bryan Hooi

A distinctive feature of our approach is its blend of natural language planning with beam search to optimize the selection of reasoning paths.

Common Sense Reasoning Graph Question Answering +6

KnowPhish: Large Language Models Meet Multimodal Knowledge Graphs for Enhancing Reference-Based Phishing Detection

1 code implementation4 Mar 2024 Yuexin Li, Chengyu Huang, Shumin Deng, Mei Lin Lock, Tri Cao, Nay Oo, Hoon Wei Lim, Bryan Hooi

Phishing attacks have inflicted substantial losses on individuals and businesses alike, necessitating the development of robust and efficient automated phishing detection approaches.

Knowledge Graphs Language Modelling

Seeing is Believing: Mitigating Hallucination in Large Vision-Language Models via CLIP-Guided Decoding

2 code implementations23 Feb 2024 Ailin Deng, Zhirui Chen, Bryan Hooi

Large Vision-Language Models (LVLMs) are susceptible to object hallucinations, an issue in which their generated text contains non-existent objects, greatly limiting their reliability and practicality.

Hallucination Object +4

UniGraph: Learning a Unified Cross-Domain Foundation Model for Text-Attributed Graphs

1 code implementation21 Feb 2024 Yufei He, Yuan Sui, Xiaoxin He, Bryan Hooi

However, graph learning has predominantly focused on single-graph models, tailored to specific tasks or datasets, lacking the ability to transfer learned knowledge to different domains.

Graph Learning Representation Learning +1

Learning the Unlearned: Mitigating Feature Suppression in Contrastive Learning

1 code implementation19 Feb 2024 Jihai Zhang, Xiang Lan, Xiaoye Qu, Yu Cheng, Mengling Feng, Bryan Hooi

Self-Supervised Contrastive Learning has proven effective in deriving high-quality representations from unlabeled data.

Contrastive Learning

G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering

2 code implementations12 Feb 2024 Xiaoxin He, Yijun Tian, Yifei Sun, Nitesh V. Chawla, Thomas Laurent, Yann Lecun, Xavier Bresson, Bryan Hooi

Given a graph with textual attributes, we enable users to `chat with their graph': that is, to ask questions about the graph using a conversational interface.

Common Sense Reasoning Graph Classification +6

Towards A Unified View of Answer Calibration for Multi-Step Reasoning

1 code implementation15 Nov 2023 Shumin Deng, Ningyu Zhang, Nay Oo, Bryan Hooi

Large Language Models (LLMs) employing Chain-of-Thought (CoT) prompting have broadened the scope for improving multi-step reasoning capabilities.

Efficient Heterogeneous Graph Learning via Random Projection

1 code implementation23 Oct 2023 Jun Hu, Bryan Hooi, Bingsheng He

To achieve low information loss, we introduce a Relation-wise Neighbor Collection component with an Even-odd Propagation Scheme, which aims to collect information from neighbors in a finer-grained way.

Graph Learning Graph Neural Network +2

Primacy Effect of ChatGPT

1 code implementation20 Oct 2023 Yiwei Wang, Yujun Cai, Muhao Chen, Yuxuan Liang, Bryan Hooi

We have two main findings: i) ChatGPT's decision is sensitive to the order of labels in the prompt; ii) ChatGPT has a clearly higher chance to select the labels at earlier positions as the answer.

Natural Language Understanding Question Answering

Privacy in Large Language Models: Attacks, Defenses and Future Directions

no code implementations16 Oct 2023 Haoran Li, Yulin Chen, Jinglong Luo, Jiecong Wang, Hao Peng, Yan Kang, Xiaojin Zhang, Qi Hu, Chunkit Chan, Zenglin Xu, Bryan Hooi, Yangqiu Song

The advancement of large language models (LLMs) has significantly enhanced the ability to effectively tackle various downstream NLP tasks and unify these tasks into generative pipelines.

Fake News in Sheep's Clothing: Robust Fake News Detection Against LLM-Empowered Style Attacks

1 code implementation16 Oct 2023 Jiaying Wu, Jiafeng Guo, Bryan Hooi

To address this, we introduce SheepDog, a style-robust fake news detector that prioritizes content over style in determining news veracity.

Articles Fact Checking +1

Multimodal Graph Learning for Generative Tasks

1 code implementation11 Oct 2023 Minji Yoon, Jing Yu Koh, Bryan Hooi, Ruslan Salakhutdinov

We study three research questions raised by MMGL: (1) how can we infuse multiple neighbor information into the pretrained LMs, while avoiding scalability issues?

Graph Learning Text Generation

Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View

1 code implementation3 Oct 2023 Jintian Zhang, Xin Xu, Ningyu Zhang, Ruibo Liu, Bryan Hooi, Shumin Deng

This paper probes the collaboration mechanisms among contemporary NLP systems by melding practical experiments with theoretical insights.

Navigate

Prompt-and-Align: Prompt-Based Social Alignment for Few-Shot Fake News Detection

1 code implementation28 Sep 2023 Jiaying Wu, Shen Li, Ailin Deng, Miao Xiong, Bryan Hooi

Despite considerable advances in automated fake news detection, due to the timely nature of news, it remains a critical open question how to effectively predict the veracity of news articles based on limited fact-checks.

Articles Fake News Detection

Enhancing Large Language Model Induced Task-Oriented Dialogue Systems Through Look-Forward Motivated Goals

no code implementations16 Sep 2023 Zhiyuan Hu, Yue Feng, Yang Deng, Zekun Li, See-Kiong Ng, Anh Tuan Luu, Bryan Hooi

Recently, the development of large language models (LLMs) has been significantly enhanced the question answering and dialogue generation, and makes them become increasingly popular in current practical scenarios.

Dialogue Generation Language Modeling +4

A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions

1 code implementation26 Aug 2023 Zemin Liu, Yuan Li, Nan Chen, Qian Wang, Bryan Hooi, Bingsheng He

However, these methods often suffer from data imbalance, a common issue in graph data where certain segments possess abundant data while others are scarce, thereby leading to biased learning outcomes.

Graph Learning Link Prediction +1

Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs

1 code implementation22 Jun 2023 Miao Xiong, Zhiyuan Hu, Xinyang Lu, Yifei Li, Jie Fu, Junxian He, Bryan Hooi

To better break down the problem, we define a systematic framework with three components: prompting strategies for eliciting verbalized confidence, sampling methods for generating multiple responses, and aggregation techniques for computing consistency.

Arithmetic Reasoning Benchmarking +1

Unlocking the Potential of User Feedback: Leveraging Large Language Model as User Simulator to Enhance Dialogue System

1 code implementation16 Jun 2023 Zhiyuan Hu, Yue Feng, Anh Tuan Luu, Bryan Hooi, Aldo Lipani

This approach uses LLM as annotation-free user simulator to assess dialogue responses, combining them with smaller fine-tuned end-to-end TOD models.

Language Modeling Language Modelling +1

PoetryDiffusion: Towards Joint Semantic and Metrical Manipulation in Poetry Generation

1 code implementation14 Jun 2023 Zhiyuan Hu, Chumin Liu, Yue Feng, Anh Tuan Luu, Bryan Hooi

Controllable text generation is a challenging and meaningful field in natural language generation (NLG).

Denoising Rhythm +2

Proximity-Informed Calibration for Deep Neural Networks

1 code implementation NeurIPS 2023 Miao Xiong, Ailin Deng, Pang Wei Koh, Jiaying Wu, Shen Li, Jianqing Xu, Bryan Hooi

We examine the problem over 504 pretrained ImageNet models and observe that: 1) Proximity bias exists across a wide variety of model architectures and sizes; 2) Transformer-based models are relatively more susceptible to proximity bias than CNN-based models; 3) Proximity bias persists even after performing popular calibration algorithms like temperature scaling; 4) Models tend to overfit more heavily on low proximity samples than on high proximity samples.

Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning

3 code implementations31 May 2023 Xiaoxin He, Xavier Bresson, Thomas Laurent, Adam Perold, Yann Lecun, Bryan Hooi

With the advent of powerful large language models (LLMs) such as GPT or Llama2, which demonstrate an ability to reason and to utilize general knowledge, there is a growing need for techniques which combine the textual modelling abilities of LLMs with the structural learning capabilities of GNNs.

Ranked #3 on Node Property Prediction on ogbn-arxiv (using extra training data)

Decision Making General Knowledge +6

GraphCleaner: Detecting Mislabelled Samples in Popular Graph Learning Benchmarks

1 code implementation30 May 2023 Yuwen Li, Miao Xiong, Bryan Hooi

Label errors have been found to be prevalent in popular text, vision, and audio datasets, which heavily influence the safe development and evaluation of machine learning algorithms.

Dataset Generation Graph Learning

How Fragile is Relation Extraction under Entity Replacements?

1 code implementation22 May 2023 Yiwei Wang, Bryan Hooi, Fei Wang, Yujun Cai, Yuxuan Liang, Wenxuan Zhou, Jing Tang, Manjuan Duan, Muhao Chen

In principle, textual context determines the ground-truth relation and the RE models should be able to correctly identify the relations reflected by the textual context.

Benchmarking Causal Inference +2

Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering

2 code implementations10 May 2023 Mingqi Yang, Wenjie Feng, Yanming Shen, Bryan Hooi

Proposing an effective and flexible matrix to represent a graph is a fundamental challenge that has been explored from multiple perspectives, e. g., filtering in Graph Fourier Transforms.

Computational Efficiency Graph Learning +2

Great Models Think Alike: Improving Model Reliability via Inter-Model Latent Agreement

no code implementations2 May 2023 Ailin Deng, Miao Xiong, Bryan Hooi

To overcome this incoherence issue, we design a \emph{neighborhood agreement measure} between latent spaces and find that this agreement is surprisingly well-correlated with the reliability of a model's predictions.

model

TAP: A Comprehensive Data Repository for Traffic Accident Prediction in Road Networks

1 code implementation17 Apr 2023 Baixiang Huang, Bryan Hooi, Kai Shu

To bridge this gap, we have constructed a real-world graph-based Traffic Accident Prediction (TAP) data repository, along with two representative tasks: accident occurrence prediction and accident severity prediction.

Prediction severity prediction

Scalable Neural Network Training over Distributed Graphs

1 code implementation25 Feb 2023 Aashish Kolluri, Sarthak Choudhary, Bryan Hooi, Prateek Saxena

We present RETEXO, the first framework which eliminates the severe communication bottleneck in distributed GNN training while respecting any given data partitioning configuration.

Node Classification

Trust, but Verify: Using Self-Supervised Probing to Improve Trustworthiness

1 code implementation6 Feb 2023 Ailin Deng, Shen Li, Miao Xiong, Zhirui Chen, Bryan Hooi

Trustworthy machine learning is of primary importance to the practical deployment of deep learning models.

Out-of-Distribution Detection

Do We Really Need Graph Neural Networks for Traffic Forecasting?

no code implementations30 Jan 2023 Xu Liu, Yuxuan Liang, Chao Huang, Hengchang Hu, Yushi Cao, Bryan Hooi, Roger Zimmermann

Spatio-temporal graph neural networks (STGNN) have become the most popular solution to traffic forecasting.

Probabilistic Knowledge Distillation of Face Ensembles

no code implementations CVPR 2023 Jianqing Xu, Shen Li, Ailin Deng, Miao Xiong, Jiaying Wu, Jiaxiang Wu, Shouhong Ding, Bryan Hooi

Mean ensemble (i. e. averaging predictions from multiple models) is a commonly-used technique in machine learning that improves the performance of each individual model.

Face Image Quality Face Recognition +2

A Generalization of ViT/MLP-Mixer to Graphs

3 code implementations27 Dec 2022 Xiaoxin He, Bryan Hooi, Thomas Laurent, Adam Perold, Yann Lecun, Xavier Bresson

First, they capture long-range dependency and mitigate the issue of over-squashing as demonstrated on Long Range Graph Benchmark and TreeNeighbourMatch datasets.

Graph Classification Graph Regression +1

Expanding Small-Scale Datasets with Guided Imagination

1 code implementation NeurIPS 2023 Yifan Zhang, Daquan Zhou, Bryan Hooi, Kai Wang, Jiashi Feng

Specifically, GIF conducts data imagination by optimizing the latent features of the seed data in the semantically meaningful space of the prior model, resulting in the creation of photo-realistic images with new content.

Reachability-Aware Laplacian Representation in Reinforcement Learning

no code implementations24 Oct 2022 Kaixin Wang, Kuangqi Zhou, Jiashi Feng, Bryan Hooi, Xinchao Wang

In Reinforcement Learning (RL), Laplacian Representation (LapRep) is a task-agnostic state representation that encodes the geometry of the environment.

reinforcement-learning Reinforcement Learning +1

MGNNI: Multiscale Graph Neural Networks with Implicit Layers

1 code implementation15 Oct 2022 Juncheng Liu, Bryan Hooi, Kenji Kawaguchi, Xiaokui Xiao

Recently, implicit graph neural networks (GNNs) have been proposed to capture long-range dependencies in underlying graphs.

Graph Classification Graph Neural Network +1

Joint Triplet Loss Learning for Next New POI Recommendation

no code implementations25 Sep 2022 Nicholas Lim, Bryan Hooi, See-Kiong Ng, Yong Liang Goh

Sparsity of the User-POI matrix is a well established problem for next POI recommendation, which hinders effective learning of user preferences.

Triplet

Probing Spurious Correlations in Popular Event-Based Rumor Detection Benchmarks

1 code implementation19 Sep 2022 Jiaying Wu, Bryan Hooi

As social media becomes a hotbed for the spread of misinformation, the crucial task of rumor detection has witnessed promising advances fostered by open-source benchmark datasets.

Misinformation

Flashlight: Scalable Link Prediction with Effective Decoders

no code implementations17 Sep 2022 Yiwei Wang, Bryan Hooi, Yozen Liu, Tong Zhao, Zhichun Guo, Neil Shah

However, HadamardMLP lacks the scalability for retrieving top scoring neighbors on large graphs, since to the best of our knowledge, there does not exist an algorithm to retrieve the top scoring neighbors for HadamardMLP decoders in sublinear complexity.

Graph Learning Link Prediction +1

Neural PCA for Flow-Based Representation Learning

no code implementations23 Aug 2022 Shen Li, Bryan Hooi

Without exploiting any label information, the principal components recovered store the most informative elements in their \emph{leading} dimensions and leave the negligible in the \emph{trailing} ones, allowing for clear performance improvements of $5\%$-$10\%$ in downstream tasks.

Density Estimation Inductive Bias +1

A Scalable, Interpretable, Verifiable & Differentiable Logic Gate Convolutional Neural Network Architecture From Truth Tables

no code implementations18 Aug 2022 Adrien Benamira, Tristan Guérand, Thomas Peyrin, Trevor Yap, Bryan Hooi

We propose $\mathcal{T}$ruth $\mathcal{T}$able net ($\mathcal{TT}$net), a novel Convolutional Neural Network (CNN) architecture that addresses, by design, the open challenges of interpretability, formal verification, and logic gate conversion.

Fairness Logical Reasoning

ARES: Locally Adaptive Reconstruction-based Anomaly Scoring

1 code implementation15 Jun 2022 Adam Goodge, Bryan Hooi, See Kiong Ng, Wee Siong Ng

However, the anomaly scoring function is not adaptive to the natural variation in reconstruction error across the range of normal samples, which hinders their ability to detect real anomalies.

Anomaly Detection Dimensionality Reduction

GRAPHCACHE: Message Passing as Caching for Sentence-Level Relation Extraction

no code implementations Findings (NAACL) 2022 Yiwei Wang, Muhao Chen, Wenxuan Zhou, Yujun Cai, Yuxuan Liang, Bryan Hooi

GRAPHCACHE aggregates the features from sentences in the whole dataset to learn global representations of properties, and use them to augment the local features within individual sentences.

Graph Neural Network Relation +2

Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis

1 code implementation NAACL 2022 Yiwei Wang, Muhao Chen, Wenxuan Zhou, Yujun Cai, Yuxuan Liang, Dayiheng Liu, Baosong Yang, Juncheng Liu, Bryan Hooi

In this paper, we propose the CORE (Counterfactual Analysis based Relation Extraction) debiasing method that guides the RE models to focus on the main effects of textual context without losing the entity information.

counterfactual Relation +2

LPGNet: Link Private Graph Networks for Node Classification

1 code implementation6 May 2022 Aashish Kolluri, Teodora Baluta, Bryan Hooi, Prateek Saxena

In this paper, we present a new neural network architecture called LPGNet for training on graphs with privacy-sensitive edges.

Classification Node Classification

MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering

2 code implementations5 Apr 2022 Jun Hu, Bryan Hooi, Shengsheng Qian, Quan Fang, Changsheng Xu

Based on a Markov process that trades off two types of distances, we present Markov Graph Diffusion Collaborative Filtering (MGDCF) to generalize some state-of-the-art GNN-based CF models.

Collaborative Filtering Multi-modal Recommendation +1

EIGNN: Efficient Infinite-Depth Graph Neural Networks

1 code implementation NeurIPS 2021 Juncheng Liu, Kenji Kawaguchi, Bryan Hooi, Yiwei Wang, Xiaokui Xiao

Motivated by this limitation, we propose a GNN model with infinite depth, which we call Efficient Infinite-Depth Graph Neural Networks (EIGNN), to efficiently capture very long-range dependencies.

Information Extraction in Low-Resource Scenarios: Survey and Perspective

3 code implementations16 Feb 2022 Shumin Deng, Yubo Ma, Ningyu Zhang, Yixin Cao, Bryan Hooi

Information Extraction (IE) seeks to derive structured information from unstructured texts, often facing challenges in low-resource scenarios due to data scarcity and unseen classes.

Survey

The Geometry of Robust Value Functions

no code implementations30 Jan 2022 Kaixin Wang, Navdeep Kumar, Kuangqi Zhou, Bryan Hooi, Jiashi Feng, Shie Mannor

The key of this perspective is to decompose the value space, in a state-wise manner, into unions of hypersurfaces.

Time-Aware Neighbor Sampling for Temporal Graph Networks

no code implementations18 Dec 2021 Yiwei Wang, Yujun Cai, Yuxuan Liang, Henghui Ding, Changhu Wang, Bryan Hooi

In this work, we propose the TNS (Time-aware Neighbor Sampling) method: TNS learns from temporal information to provide an adaptive receptive neighborhood for every node at any time.

Node Classification

LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks

1 code implementation10 Dec 2021 Adam Goodge, Bryan Hooi, See Kiong Ng, Wee Siong Ng

This allows us to introduce learnability into local outlier methods, in the form of a neural network, for greater flexibility and expressivity: specifically, we propose LUNAR, a novel, graph neural network-based anomaly detection method.

Anomaly Detection Graph Neural Network +1

Adaptive Data Augmentation on Temporal Graphs

no code implementations NeurIPS 2021 Yiwei Wang, Yujun Cai, Yuxuan Liang, Henghui Ding, Changhu Wang, Siddharth Bhatia, Bryan Hooi

To address this issue, our idea is to transform the temporal graphs using data augmentation (DA) with adaptive magnitudes, so as to effectively augment the input features and preserve the essential semantic information.

Data Augmentation Node Classification

Structure-Aware Label Smoothing for Graph Neural Networks

no code implementations1 Dec 2021 Yiwei Wang, Yujun Cai, Yuxuan Liang, Wei Wang, Henghui Ding, Muhao Chen, Jing Tang, Bryan Hooi

Representing a label distribution as a one-hot vector is a common practice in training node classification models.

Classification Node Classification

Benefit-aware Early Prediction of Health Outcomes on Multivariate EEG Time Series

no code implementations11 Nov 2021 Shubhranshu Shekhar, Dhivya Eswaran, Bryan Hooi, Jonathan Elmer, Christos Faloutsos, Leman Akoglu

Given a cardiac-arrest patient being monitored in the ICU (intensive care unit) for brain activity, how can we predict their health outcomes as early as possible?

Decision Making EEG +2

SSMF: Shifting Seasonal Matrix Factorization

1 code implementation NeurIPS 2021 Koki Kawabata, Siddharth Bhatia, Rui Liu, Mohit Wadhwa, Bryan Hooi

In general, given a data stream of events with seasonal patterns that innovate over time, how can we effectively and efficiently forecast future events?

Data Compression

Deep Long-Tailed Learning: A Survey

1 code implementation9 Oct 2021 Yifan Zhang, Bingyi Kang, Bryan Hooi, Shuicheng Yan, Jiashi Feng

Deep long-tailed learning, one of the most challenging problems in visual recognition, aims to train well-performing deep models from a large number of images that follow a long-tailed class distribution.

Survey

Truth Table Deep Convolutional Neural Network, A New SAT-Encodable Architecture - Application To Complete Robustness

no code implementations29 Sep 2021 Adrien Benamira, Thomas Peyrin, Bryan Hooi

Moreover, the corresponding SAT conversion method intrinsically leads to formulas with a large number of variables and clauses, impeding interpretability as well as formal verification scalability.

Explainable Artificial Intelligence (XAI) Explanation Generation +1

When Do Contrastive Learning Signals Help Spatio-Temporal Graph Forecasting?

1 code implementation26 Aug 2021 Xu Liu, Yuxuan Liang, Chao Huang, Yu Zheng, Bryan Hooi, Roger Zimmermann

In view of this, one may ask: can we leverage the additional signals from contrastive learning to alleviate data scarcity, so as to benefit STG forecasting?

Contrastive Learning Data Augmentation +2

Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition

2 code implementations20 Jul 2021 Yifan Zhang, Bryan Hooi, Lanqing Hong, Jiashi Feng

Existing long-tailed recognition methods, aiming to train class-balanced models from long-tailed data, generally assume the models would be evaluated on the uniform test class distribution.

Image Classification Long-tail Learning +1

GraphAnoGAN: Detecting Anomalous Snapshots from Attributed Graphs

1 code implementation29 Jun 2021 Siddharth Bhatia, Yiwei Wang, Bryan Hooi, Tanmoy Chakraborty

Specifically, the generative model learns to approximate the distribution of anomalous samples from the candidate set of graph snapshots, and the discriminative model detects whether the sampled snapshot is from the ground-truth or not.

Graph Neural Network-Based Anomaly Detection in Multivariate Time Series

2 code implementations13 Jun 2021 Ailin Deng, Bryan Hooi

Given high-dimensional time series data (e. g., sensor data), how can we detect anomalous events, such as system faults and attacks?

Graph Neural Network Time Series +2

Sketch-Based Anomaly Detection in Streaming Graphs

1 code implementation8 Jun 2021 Siddharth Bhatia, Mohit Wadhwa, Kenji Kawaguchi, Neil Shah, Philip S. Yu, Bryan Hooi

This higher-order sketch has the useful property of preserving the dense subgraph structure (dense subgraphs in the input turn into dense submatrices in the data structure).

Anomaly Detection Intrusion Detection

MemStream: Memory-Based Streaming Anomaly Detection

1 code implementation7 Jun 2021 Siddharth Bhatia, Arjit Jain, Shivin Srivastava, Kenji Kawaguchi, Bryan Hooi

Given a stream of entries over time in a multi-dimensional data setting where concept drift is present, how can we detect anomalous activities?

Denoising Unsupervised Anomaly Detection

Mixup for Node and Graph Classification

1 code implementation1 Jun 2021 Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, Bryan Hooi

In this work, we propose the Mixup methods for two fundamental tasks in graph learning: node and graph classification.

Data Augmentation Graph Classification +2

Isconna: Streaming Anomaly Detection with Frequency and Patterns

2 code implementations4 Apr 2021 Rui Liu, Siddharth Bhatia, Bryan Hooi

Isconna does not actively explore or maintain pattern snippets; it instead measures the consecutive presence and absence of edge records.

Anomaly Detection

Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning

1 code implementation NeurIPS 2021 Yifan Zhang, Bryan Hooi, Dapeng Hu, Jian Liang, Jiashi Feng

In this paper, we investigate whether applying contrastive learning to fine-tuning would bring further benefits, and analytically find that optimizing the contrastive loss benefits both discriminative representation learning and model optimization during fine-tuning.

Contrastive Learning image-classification +5

Hypersphere Face Uncertainty Learning

no code implementations1 Jan 2021 Shen Li, Jianqing Xu, Xiaqing Xu, Pengcheng Shen, Shaoxin Li, Bryan Hooi

To address these issues, in this paper, we propose a novel framework for face uncertainty learning in hyperspherical space.

Face Verification

Dynamic Graph-Based Anomaly Detection in the Electrical Grid

1 code implementation30 Dec 2020 Shimiao Li, Amritanshu Pandey, Bryan Hooi, Christos Faloutsos, Larry Pileggi

Given sensor readings over time from a power grid, how can we accurately detect when an anomaly occurs?

Anomaly Detection

LSCALE: Latent Space Clustering-Based Active Learning for Node Classification

1 code implementation13 Dec 2020 Juncheng Liu, Yiwei Wang, Bryan Hooi, Renchi Yang, Xiaokui Xiao

We argue that the representation power in unlabelled nodes can be useful for active learning and for further improving performance of active learning for node classification.

Active Learning Clustering +2

Origin-Aware Next Destination Recommendation with Personalized Preference Attention

1 code implementation3 Dec 2020 Nicholas Lim, Bryan Hooi, See-Kiong Ng, Xueou Wang, Yong Liang Goh, Renrong Weng, Rui Tan

Next destination recommendation is an important task in the transportation domain of taxi and ride-hailing services, where users are recommended with personalized destinations given their current origin location.

Decoder

Autonomous Graph Mining Algorithm Search with Best Speed/Accuracy Trade-off

1 code implementation26 Nov 2020 Minji Yoon, Théophile Gervet, Bryan Hooi, Christos Faloutsos

We first define a unified framework UNIFIEDGM that integrates various message-passing based graph algorithms, ranging from conventional algorithms like PageRank to graph neural networks.

Bayesian Optimization Graph Mining +1

STP-UDGAT: Spatial-Temporal-Preference User Dimensional Graph Attention Network for Next POI Recommendation

no code implementations6 Oct 2020 Nicholas Lim, Bryan Hooi, See-Kiong Ng, Xueou Wang, Yong Liang Goh, Renrong Weng, Jagannadan Varadarajan

Next Point-of-Interest (POI) recommendation is a longstanding problem across the domains of Location-Based Social Networks (LBSN) and transportation.

Graph Attention

SHADOWCAST: Controllable Graph Generation with Explainability

no code implementations28 Sep 2020 Wesley Joon-Wie Tann, Ee-Chien Chang, Bryan Hooi

We introduce the problem of explaining graph generation, formulated as controlling the generative process to produce desired graphs with explainable structures.

Generative Adversarial Network Graph Generation

GraphCrop: Subgraph Cropping for Graph Classification

no code implementations22 Sep 2020 Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, Bryan Hooi

We present a new method to regularize graph neural networks (GNNs) for better generalization in graph classification.

Data Augmentation General Classification +2

Real-Time Anomaly Detection in Edge Streams

3 code implementations17 Sep 2020 Siddharth Bhatia, Rui Liu, Bryan Hooi, Minji Yoon, Kijung Shin, Christos Faloutsos

Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges in an online manner, for the purpose of detecting unusual behavior, using constant time and memory?

Anomaly Detection Anomaly Detection in Edge Streams

ExGAN: Adversarial Generation of Extreme Samples

1 code implementation17 Sep 2020 Siddharth Bhatia, Arjit Jain, Bryan Hooi

Hence, in this work, we propose ExGAN, a GAN-based approach to generate realistic and extreme samples.

Extreme Sample Generation

MSTREAM: Fast Anomaly Detection in Multi-Aspect Streams

1 code implementation17 Sep 2020 Siddharth Bhatia, Arjit Jain, Pan Li, Ritesh Kumar, Bryan Hooi

Given a stream of entries in a multi-aspect data setting i. e., entries having multiple dimensions, how can we detect anomalous activities in an unsupervised manner?

Group Anomaly Detection Intrusion Detection

Understanding and Resolving Performance Degradation in Graph Convolutional Networks

2 code implementations12 Jun 2020 Kuangqi Zhou, Yanfei Dong, Kaixin Wang, Wee Sun Lee, Bryan Hooi, Huan Xu, Jiashi Feng

In this work, we study performance degradation of GCNs by experimentally examining how stacking only TRANs or PROPs works.

Structural Patterns and Generative Models of Real-world Hypergraphs

no code implementations12 Jun 2020 Manh Tuan Do, Se-eun Yoon, Bryan Hooi, Kijung Shin

Graphs have been utilized as a powerful tool to model pairwise relationships between people or objects.

Social and Information Networks Physics and Society

SHADOWCAST: Controllable Graph Generation

no code implementations6 Jun 2020 Wesley Joon-Wie Tann, Ee-Chien Chang, Bryan Hooi

Given an observed graph and some user-specified Markov model parameters, ${\rm S{\small HADOW}C{\small AST}}$ controls the conditions to generate desired graphs.

Generative Adversarial Network Graph Generation

MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams

9 code implementations11 Nov 2019 Siddharth Bhatia, Bryan Hooi, Minji Yoon, Kijung Shin, Christos Faloutsos

Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges in an online manner, for the purpose of detecting unusual behavior, using constant time and memory?

Anomaly Detection in Edge Streams

Identifying through Flows for Recovering Latent Representations

2 code implementations ICLR 2020 Shen Li, Bryan Hooi, Gim Hee Lee

Yet, most deep generative models do not address the question of identifiability, and thus fail to deliver on the promise of the recovery of the true latent sources that generate the observations.

Representation Learning

Out-of-Core and Distributed Algorithms for Dense Subtensor Mining

1 code implementation4 Feb 2018 Kijung Shin, Bryan Hooi, Jisu Kim, Christos Faloutsos

Can we detect it when data are too large to fit in memory or even on a disk?

Databases Distributed, Parallel, and Cluster Computing Social and Information Networks H.2.8

HoloScope: Topology-and-Spike Aware Fraud Detection

1 code implementation6 May 2017 Shenghua Liu, Bryan Hooi, Christos Faloutsos

Hence, we propose HoloScope, which uses information from graph topology and temporal spikes to more accurately detect groups of fraudulent users.

Social and Information Networks

FairJudge: Trustworthy User Prediction in Rating Platforms

no code implementations30 Mar 2017 Srijan Kumar, Bryan Hooi, Disha Makhija, Mohit Kumar, Christos Faloutsos, V. S. Subrahamanian

We propose three metrics: (i) the fairness of a user that quantifies how trustworthy the user is in rating the products, (ii) the reliability of a rating that measures how reliable the rating is, and (iii) the goodness of a product that measures the quality of the product.

Fairness Prediction

BIRDNEST: Bayesian Inference for Ratings-Fraud Detection

no code implementations19 Nov 2015 Bryan Hooi, Neil Shah, Alex Beutel, Stephan Gunnemann, Leman Akoglu, Mohit Kumar, Disha Makhija, Christos Faloutsos

To combine these 2 approaches, we formulate our Bayesian Inference for Rating Data (BIRD) model, a flexible Bayesian model of user rating behavior.

Bayesian Inference Fraud Detection

Cannot find the paper you are looking for? You can Submit a new open access paper.