Search Results for author: Bryan Hooi

Found 96 papers, 62 papers with code

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

Furthermore, we propose to leverage deductive reasoning capabilities of LLMs as a better criterion to automatically guide the reasoning process in a stepwise and generalizable manner.

Common Sense Reasoning Graph Question Answering +4

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

no code implementations4 Mar 2024 Yuexin Li, Chengyu Huang, Shumin Deng, Mei Lin Lock, Tri Cao, Nay Oo, Bryan Hooi, Hoon Wei Lim

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

1 code implementation23 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 +3

UniGraph: Learning a Cross-Domain Graph Foundation Model From Natural Language

no code implementations21 Feb 2024 Yufei He, Bryan Hooi

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

Learning the Unlearned: Mitigating Feature Suppression in Contrastive Learning

no code implementations19 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

1 code implementation12 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 +4

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

no code implementations15 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 +1

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

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

no code implementations16 Oct 2023 Jiaying Wu, Bryan Hooi

Furthermore, SheepDog extracts content-focused veracity attributions from LLMs, where the news content is evaluated against a set of fact-checking rationales.

Fact Checking Fake News Detection

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.

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 Modelling +3

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 Modelling Large Language Model

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 Sentence +1

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

Decision Making General Knowledge +5

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.

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

3 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.

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.

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 (RL)

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.

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

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 Recommendation Systems +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

2 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.

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.

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 +4

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

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

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

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

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.

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

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

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