Search Results for author:
Found 71 papers, 43 papers with code
Explanations as Features: LLM-Based Features for Text-Attributed Graphs
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)
GraphCleaner: Detecting Mislabelled Samples in Popular Graph Learning Benchmarks
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.
SPEECH: Structured Prediction with Energy-Based Event-Centric Hyperspheres
Event-centric structured prediction involves predicting structured outputs of events.
How Fragile is Relation Extraction under Entity Replacements?
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.
Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering
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.
Ranked #1 on
Graph Regression
on ZINC
Great Models Think Alike: Improving Model Reliability via Inter-Model Latent Agreement
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
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.
RETEXO: Scalable Neural Network Training over Distributed Graphs
Graph neural networks offer a promising approach to supervised learning over graph data.
Trust, but Verify: Using Self-Supervised Probing to Improve Trustworthiness
Trustworthy machine learning is of primary importance to the practical deployment of deep learning models.
Do We Really Need Graph Neural Networks for Traffic Forecasting?
Spatio-temporal graph neural networks (STGNN) have become the most popular solution to traffic forecasting.
Probabilistic Knowledge Distillation of Face Ensembles
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.
A Generalization of ViT/MLP-Mixer to Graphs
First, they capture long-range dependency and mitigate the issue of over-squashing as demonstrated on Long Range Graph Benchmark and TreeNeighbourMatch datasets.
Ranked #1 on
Graph Classification
on Peptides-func
Birds of a Feather Trust Together: Knowing When to Trust a Classifier via Adaptive Neighborhood Aggregation
How do we know when the predictions made by a classifier can be trusted?
Expanding Small-Scale Datasets with Guided Imagination
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.
Reachability-Aware Laplacian Representation in Reinforcement Learning
In Reinforcement Learning (RL), Laplacian Representation (LapRep) is a task-agnostic state representation that encodes the geometry of the environment.
MGNNI: Multiscale Graph Neural Networks with Implicit Layers
Recently, implicit graph neural networks (GNNs) have been proposed to capture long-range dependencies in underlying graphs.
Construction and Applications of Billion-Scale Pre-Trained Multimodal Business Knowledge Graph
We release all the open resources (OpenBG benchmarks) derived from it for the community and report experimental results of KG-centric tasks.
Joint Triplet Loss Learning for Next New POI Recommendation
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
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
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.
Neural PCA for Flow-Based Representation Learning
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.
A Scalable, Interpretable, Verifiable & Differentiable Logic Gate Convolutional Neural Network Architecture From Truth Tables
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.
ARES: Locally Adaptive Reconstruction-based Anomaly Scoring
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.
GRAPHCACHE: Message Passing as Caching for Sentence-Level Relation Extraction
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.
Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis
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.
LPGNet: Link Private Graph Networks for Node Classification
In this paper, we present a new neural network architecture called LPGNet for training on graphs with privacy-sensitive edges.
Dangling-Aware Entity Alignment with Mixed High-Order Proximities
We study dangling-aware entity alignment in knowledge graphs (KGs), which is an underexplored but important problem.
EIGNN: Efficient Infinite-Depth Graph Neural Networks
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
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
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
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.
LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks
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.
Probabilistic Contrastive Loss for Self-Supervised Learning
This paper proposes a probabilistic contrastive loss function for self-supervised learning.
Structure-Aware Label Smoothing for Graph Neural Networks
Representing a label distribution as a one-hot vector is a common practice in training node classification models.
Adaptive Data Augmentation on Temporal Graphs
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.
Benefit-aware Early Prediction of Health Outcomes on Multivariate EEG Time Series
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?
SSMF: Shifting Seasonal Matrix Factorization
In general, given a data stream of events with seasonal patterns that innovate over time, how can we effectively and efficiently forecast future events?
Deep Long-Tailed Learning: A Survey
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
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.
When Do Contrastive Learning Signals Help Spatio-Temporal Graph Forecasting?
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?
Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition
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.
Ranked #6 on
Long-tail Learning
on iNaturalist 2018
Towards Better Laplacian Representation in Reinforcement Learning with Generalized Graph Drawing
It enables learning high-quality Laplacian representations that faithfully approximate the ground truth.
GraphAnoGAN: Detecting Anomalous Snapshots from Attributed Graphs
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.
Spherical Confidence Learning for Face Recognition
Probabilistic Face Embeddings (PFE) is the first attempt to address this dilemma.
Graph Neural Network-Based Anomaly Detection in Multivariate Time Series
Given high-dimensional time series data (e. g., sensor data), how can we detect anomalous events, such as system faults and attacks?
Sketch-Based Anomaly Detection in Streaming Graphs
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).
MemStream: Memory-Based Streaming Anomaly Detection
Given a stream of entries over time in a multi-dimensional data setting where concept drift is present, how can we detect anomalous activities?
Mixup for Node and Graph Classification
In this work, we propose the Mixup methods for two fundamental tasks in graph learning: node and graph classification.
Ranked #15 on
Node Classification
on Pubmed
Isconna: Streaming Anomaly Detection with Frequency and Patterns
Isconna does not actively explore or maintain pattern snippets; it instead measures the consecutive presence and absence of edge records.
Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning
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.
Hypersphere Face Uncertainty Learning
To address these issues, in this paper, we propose a novel framework for face uncertainty learning in hyperspherical space.
Dynamic Graph-Based Anomaly Detection in the Electrical Grid
Given sensor readings over time from a power grid, how can we accurately detect when an anomaly occurs?
LSCALE: Latent Space Clustering-Based Active Learning for Node Classification
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.
Origin-Aware Next Destination Recommendation with Personalized Preference Attention
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.
Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach
This allows us to detect sudden changes in the importance of any node.
Autonomous Graph Mining Algorithm Search with Best Speed/Accuracy Trade-off
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.
STP-UDGAT: Spatial-Temporal-Preference User Dimensional Graph Attention Network for Next POI Recommendation
Next Point-of-Interest (POI) recommendation is a longstanding problem across the domains of Location-Based Social Networks (LBSN) and transportation.
SHADOWCAST: Controllable Graph Generation with Explainability
We introduce the problem of explaining graph generation, formulated as controlling the generative process to produce desired graphs with explainable structures.
GraphCrop: Subgraph Cropping for Graph Classification
We present a new method to regularize graph neural networks (GNNs) for better generalization in graph classification.
MSTREAM: Fast Anomaly Detection in Multi-Aspect Streams
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?
Ranked #1 on
Intrusion Detection
on CIC-DDoS
Real-Time Anomaly Detection in Edge Streams
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?
ExGAN: Adversarial Generation of Extreme Samples
Hence, in this work, we propose ExGAN, a GAN-based approach to generate realistic and extreme samples.
Structural Patterns and Generative Models of Real-world Hypergraphs
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
In this work, we study performance degradation of GCNs by experimentally examining how stacking only TRANs or PROPs works.
SHADOWCAST: Controllable Graph Generation
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.
MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams
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?
Ranked #1 on
Anomaly Detection in Edge Streams
on Darpa
Identifying through Flows for Recovering Latent Representations
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.
Out-of-Core and Distributed Algorithms for Dense Subtensor Mining
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
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
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
To combine these 2 approaches, we formulate our Bayesian Inference for Rating Data (BIRD) model, a flexible Bayesian model of user rating behavior.
