Search Results for author: Bryan Hooi

Found 71 papers, 43 papers with code

Explanations as Features: LLM-Based Features for Text-Attributed Graphs

1 code implementation31 May 2023 Xiaoxin He, Xavier Bresson, Thomas Laurent, Bryan Hooi

Most graph neural network (GNN) pipelines handle these text attributes by transforming them into shallow or hand-crafted features, such as skip-gram or bag-of-words features.

 Ranked #1 on Node Property Prediction on ogbn-arxiv (using extra training data)

General Knowledge Node Property Prediction +3

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 +1

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.

Graph Learning Graph Regression +1

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

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 implementation25 Nov 2022 Yifan Zhang, Daquan Zhou, Bryan Hooi, Kai Wang, Jiashi Feng

The two criteria are verified to be essential for effective dataset expansion: GIF-SD obtains 13. 5\% higher model accuracy on natural image datasets than unguided expansion with SD.

Zero-Shot Learning

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 Node Classification

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.


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.

Relation Extraction

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.

Relation Extraction

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

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.

Knowledge Extraction in Low-Resource Scenarios: Survey and Perspective

2 code implementations16 Feb 2022 Shumin Deng, Ningyu Zhang, Bryan Hooi

Knowledge Extraction (KE), aiming to extract structural information from unstructured texts, often suffers from data scarcity and emerging unseen types, i. e., low-resource scenarios.

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 Outlier Detection

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

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

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 Electroencephalogram (EEG) +1

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.

Explanation Generation Logical Reasoning

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

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

1 code implementation13 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?

Anomaly Detection Time Series Analysis

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 General Classification +1

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.

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.

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 +1

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

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

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

1 code implementation12 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.

Graph Generation

MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams

4 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.


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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