Search Results for author:
Found 81 papers, 35 papers with code
Shapley-Guided Utility Learning for Effective Graph Inference Data Valuation
Graph Neural Networks (GNNs) have demonstrated remarkable performance in various graph-based machine learning tasks, yet evaluating the importance of neighbors of testing nodes remains largely unexplored due to the challenge of assessing data importance without test labels.
Towards Graph Foundation Models: A Transferability Perspective
We provide a structured perspective on current progress and identify potential pathways for advancing GFM generalization across diverse graph datasets and tasks.
Exploring Graph Tasks with Pure LLMs: A Comprehensive Benchmark and Investigation
In recent years, large language models (LLMs) have emerged as promising candidates for graph tasks, yet most studies focus primarily on performance benchmarks and fail to address their broader potential, including their ability to handle limited data, their transferability across tasks, and their robustness.
Unifying and Optimizing Data Values for Selection via Sequential-Decision-Making
Data selection has emerged as a crucial downstream application of data valuation.
Reinforcement Learning for Quantum Circuit Design: Using Matrix Representations
Quantum computing promises advantages over classical computing.
Extending Graph Condensation to Multi-Label Datasets: A Benchmark Study
As graph data grows increasingly complicate, training graph neural networks (GNNs) on large-scale datasets presents significant challenges, including computational resource constraints, data redundancy, and transmission inefficiencies.
Gradual Fine-Tuning with Graph Routing for Multi-Source Unsupervised Domain Adaptation
Multi-source unsupervised domain adaptation aims to leverage labeled data from multiple source domains for training a machine learning model to generalize well on a target domain without labels.
Multi-Source Unsupervised Domain Adaptation
Natural Language Inference
+2
A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness
Large language models (LLMs) have demonstrated emergent abilities in text generation, question answering, and reasoning, facilitating various tasks and domains.
Efficient Pointwise-Pairwise Learning-to-Rank for News Recommendation
Extensive experiments show that our approach outperforms the state-of-the-art methods on the MIND and Adressa news recommendation datasets.
Do Neural Scaling Laws Exist on Graph Self-Supervised Learning?
This paper examines existing SSL techniques for the feasibility of Graph SSL techniques in developing GFMs and opens a new direction for graph SSL design with the new evaluation prototype.
Structure-based Drug Design Benchmark: Do 3D Methods Really Dominate?
Currently, the field of structure-based drug design is dominated by three main types of algorithms: search-based algorithms, deep generative models, and reinforcement learning.
PDHG-Unrolled Learning-to-Optimize Method for Large-Scale Linear Programming
In this work, we propose an FOM-unrolled neural network (NN) called PDHG-Net, and propose a two-stage L2O method to solve large-scale LP problems.
Revisiting the Message Passing in Heterophilous Graph Neural Networks
Based on HTMP and empirical analysis, we reveal that the success of message passing in existing HTGNNs is attributed to implicitly enhancing the compatibility matrix among classes.
Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection
Recent advances have utilized Graph Neural Networks (GNNs) to learn effective node representations by aggregating information from neighborhoods.
Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmark
These datasets are thoughtfully designed to include relevant graph structures and bias information crucial for the fair evaluation of models.
Hill Function-based Model of Transcriptional Response: Impact of Nonspecific Binding and RNAP Interactions
Its attribute of fitting may result in a lack of an underlying physical picture, yet the fitting parameters can provide information about biochemical reactions, such as the number of transcription factors (TFs) and the binding energy between regulatory elements.
Overcoming Pitfalls in Graph Contrastive Learning Evaluation: Toward Comprehensive Benchmarks
The rise of self-supervised learning, which operates without the need for labeled data, has garnered significant interest within the graph learning community.
A Survey on Safe Multi-Modal Learning System
In the rapidly evolving landscape of artificial intelligence, multimodal learning systems (MMLS) have gained traction for their ability to process and integrate information from diverse modality inputs.
Active Learning for Graphs with Noisy Structures
Yet, the excessive cost of labeling large-scale graphs led to a focus on active learning on graphs, which aims for effective data selection to maximize downstream model performance.
A Survey to Recent Progress Towards Understanding In-Context Learning
In-Context Learning (ICL) empowers Large Language Models (LLMs) with the ability to learn from a few examples provided in the prompt, enabling downstream generalization without the requirement for gradient updates.
Position: Graph Foundation Models are Already Here
Graph Foundation Models (GFMs) are emerging as a significant research topic in the graph domain, aiming to develop graph models trained on extensive and diverse data to enhance their applicability across various tasks and domains.
A Survey on Graph Condensation
Analytics on large-scale graphs have posed significant challenges to computational efficiency and resource requirements.
Precedence-Constrained Winter Value for Effective Graph Data Valuation
Data valuation is essential for quantifying data's worth, aiding in assessing data quality and determining fair compensation.
Distance-Based Propagation for Efficient Knowledge Graph Reasoning
A new class of methods have been proposed to tackle this problem by aggregating path information.
Fast Graph Condensation with Structure-based Neural Tangent Kernel
The rapid development of Internet technology has given rise to a vast amount of graph-structured data.
LPFormer: An Adaptive Graph Transformer for Link Prediction
These methods perform predictions by using the output of an MPNN in conjunction with a "pairwise encoding" that captures the relationship between nodes in the candidate link.
Revisiting Link Prediction: A Data Perspective
We recognize three fundamental factors critical to link prediction: local structural proximity, global structural proximity, and feature proximity.
Multi-Instance Adversarial Attack on GNN-Based Malicious Domain Detection
In this work, we call the attack that manipulates several nodes in the DMG concurrently a multi-instance evasion attack.
Towards Fair Graph Neural Networks via Graph Counterfactual
Despite their great performance in modeling graphs, recent works show that GNNs tend to inherit and amplify the bias from training data, causing concerns of the adoption of GNNs in high-stake scenarios.
Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking
Furthermore, new and diverse datasets have also been created to better evaluate the effectiveness of these new models.
Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?
Recent studies on Graph Neural Networks(GNNs) provide both empirical and theoretical evidence supporting their effectiveness in capturing structural patterns on both homophilic and certain heterophilic graphs.
Heterogeneous Randomized Response for Differential Privacy in Graph Neural Networks
Graph neural networks (GNNs) are susceptible to privacy inference attacks (PIAs), given their ability to learn joint representation from features and edges among nodes in graph data.
Learning Representations for Hyper-Relational Knowledge Graphs
It motivates us to design a framework that utilizes multiple aggregators to learn representations for hyper-relational facts: one from the perspective of the base triple and the other one from the perspective of the qualifiers.
Enhancing Graph Contrastive Learning with Node Similarity
Graph contrastive learning (GCL) is a representative framework for self-supervised learning.
Trust-Aware Control of Automated Vehicles in Car-Following Interactions with Human Drivers
With the proposed approach, trust-aware AVs generate explicable plans by optimizing both predefined plans and explicability of the plans in the car-following interactions with the following human driver.
Inverse Resource Rational Based Stochastic Driver Behavior Model
An inverse resource rational-based stochastic inverse reinforcement learning approach (IRR-SIRL) is proposed to learn a distribution of the planning horizon and cost function of the human driver with a given series of human demonstrations.
Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective
In this paper, we propose a new perspective to look at the performance degradation of deep GNNs, i. e., feature overcorrelation.
Are Message Passing Neural Networks Really Helpful for Knowledge Graph Completion?
This suggests a conflation of scoring function design, loss function design, and MP in prior work, with promising insights regarding the scalability of state-of-the-art KGC methods today, as well as careful attention to more suitable MP designs for KGC tasks tomorrow.
Personalized Driving Behaviors and Fuel Economy over Realistic Commute Traffic: Modeling, Correlation, and Prediction
A Gaussian Process Regression model is further trained, validated, and tested under different traffic and vehicle conditions to predict fuel consumption based on drivers' personalized behaviors.
Graph Enhanced BERT for Query Understanding
In other words, GE-BERT can capture both the semantic information and the users' search behavioral information of queries.
Distributed Stochastic Model Predictive Control for Human-Leading Heavy-Duty Truck Platoon
The proposed DSMPC design integrates the stochastic driver behavior model of the human-driven leader truck with a distributed formation control design for the following automated trucks in the platoon.
Socially Compatible Control Design of Automated Vehicle in Mixed Traffic
In the car-following scenarios, automated vehicles (AVs) usually plan motions without considering the impacts of their actions on the following human drivers.
Graph Neural Networks with Adaptive Residual
Graph neural networks (GNNs) have shown the power in graph representation learning for numerous tasks.
Towards Feature Overcorrelation in Deeper Graph Neural Networks
In this paper, we observe a new issue in deeper GNNs, i. e., feature overcorrelation, and perform a thorough study to deepen our understanding on this issue.
Multimodality in Meta-Learning: A Comprehensive Survey
This survey provides a comprehensive overview of the multimodality-based meta-learning landscape in terms of the methodologies and applications.
Multi-Criteria Radio Spectrum Sharing With Subspace-Based Pareto Tracing
We perform an exploratory analysis of coexistence behavior by approximating active subspaces to identify low-dimensional structure in the optimization criteria, i. e., few linear combinations of parameters for simultaneously maximizing LAA and Wi-Fi throughputs.
Elastic Graph Neural Networks
While many existing graph neural networks (GNNs) have been proven to perform $\ell_2$-based graph smoothing that enforces smoothness globally, in this work we aim to further enhance the local smoothness adaptivity of GNNs via $\ell_1$-based graph smoothing.
Inverse Reinforcement Learning Based Stochastic Driver Behavior Learning
Compared to the deterministic baseline driver behavior model, the results reveal that the proposed stochastic driver behavior model can better replicate the driver's unique and rich driving strategies in a variety of traffic conditions.
Fuel-Economical Distributed Model Predictive Control for Heavy-Duty Truck Platoon
Simulation studies are conducted to investigate the fuel economy performance of the proposed control strategy and to validate the local and string stability of the platoon under a realistic traffic scenario.
Is Homophily a Necessity for Graph Neural Networks?
We find that this claim is not quite true, and in fact, GCNs can achieve strong performance on heterophilous graphs under certain conditions.
Automated Self-Supervised Learning for Graphs
Then we propose the AutoSSL framework which can automatically search over combinations of various self-supervised tasks.
Graph Feature Gating Networks
Graph neural networks (GNNs) have received tremendous attention due to their power in learning effective representations for graphs.
Personalized Adaptive Cruise Control and Impacts on Mixed Traffic
The proposed PACC design plans the motion of the SAV by minimizing the learned unique cost function considering the short preview information of the preceding human-driven vehicle.
Optimizing Unlicensed Band Spectrum Sharing With Subspace-Based Pareto Tracing
We perform an exploratory analysis of coexistence behavior by approximating active subspaces to identify low-dimensional structure in the optimization criteria, i. e., few linear combinations of parameters for simultaneously maximizing KPIs.
An $N/D$ study of the $S_{11}$ channel $πN$ scattering amplitude
Extensive dynamical $N/D$ calculations are made in the study of $S_{11}$ channel low energy $\pi$N scatterings, based on various phenomenological model inputs of left cuts at tree level.
Nuclear Theory High Energy Physics - Phenomenology
Node Similarity Preserving Graph Convolutional Networks
Specifically, to balance information from graph structure and node features, we propose a feature similarity preserving aggregation which adaptively integrates graph structure and node features.
A Unified View on Graph Neural Networks as Graph Signal Denoising
In this work, we establish mathematically that the aggregation processes in a group of representative GNN models including GCN, GAT, PPNP, and APPNP can be regarded as (approximately) solving a graph denoising problem with a smoothness assumption.
Customized Graph Neural Networks
Therefore, in this paper, we aim to develop customized graph neural networks for graph classification.
Graph Structure Learning for Robust Graph Neural Networks
A natural idea to defend adversarial attacks is to clean the perturbed graph.
Attacking Black-box Recommendations via Copying Cross-domain User Profiles
In this work, we present our framework CopyAttack, which is a reinforcement learning based black-box attack method that harnesses real users from a source domain by copying their profiles into the target domain with the goal of promoting a subset of items.
Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
In this survey, we review the state of the art algorithms for generating adversarial examples and the countermeasures against adversarial examples, for the three popular data types, i. e., images, graphs and text.
Deep Social Collaborative Filtering
Meanwhile, most of these models treat neighbors' information equally without considering the specific recommendations.
R-Transformer: Recurrent Neural Network Enhanced Transformer
Recurrent Neural Networks have long been the dominating choice for sequence modeling.
Ranked #1 on
Music Modeling
on Nottingham
Attacking Graph Convolutional Networks via Rewiring
Graph Neural Networks (GNNs) have boosted the performance of many graph related tasks such as node classification and graph classification.
Deep Adversarial Social Recommendation
Recent years have witnessed rapid developments on social recommendation techniques for improving the performance of recommender systems due to the growing influence of social networks to our daily life.
Automata Guided Skill Composition
Skills learned through (deep) reinforcement learning often generalizes poorly across tasks and re-training is necessary when presented with a new task.
Graph Convolutional Networks with EigenPooling
To apply graph neural networks for the graph classification task, approaches to generate the \textit{graph representation} from node representations are demanded.
Ranked #1 on
Graph Classification
on NC1
Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers
We consider worker skill estimation for the single-coin Dawid-Skene crowdsourcing model.
Graph Neural Networks for Social Recommendation
These advantages of GNNs provide great potential to advance social recommendation since data in social recommender systems can be represented as user-user social graph and user-item graph; and learning latent factors of users and items is the key.
Ranked #3 on
Recommendation Systems
on Epinions
(using extra training data)
Streaming Graph Neural Networks
Current graph neural network models cannot utilize the dynamic information in dynamic graphs.
Automata Guided Reinforcement Learning With Demonstrations
Tasks with complex temporal structures and long horizons pose a challenge for reinforcement learning agents due to the difficulty in specifying the tasks in terms of reward functions as well as large variances in the learning signals.
Signed Graph Convolutional Network
However, since previous GCN models have primarily focused on unsigned networks (or graphs consisting of only positive links), it is unclear how they could be applied to signed networks due to the challenges presented by negative links.
Ranked #2 on
Link Sign Prediction
on Epinions
Social and Information Networks Physics and Society
Linked Recurrent Neural Networks
Recurrent Neural Networks (RNNs) have been proven to be effective in modeling sequential data and they have been applied to boost a variety of tasks such as document classification, speech recognition and machine translation.
Multi-dimensional Graph Convolutional Networks
Convolutional neural networks (CNNs) leverage the great power in representation learning on regular grid data such as image and video.
Social and Information Networks
AUTOMATA GUIDED HIERARCHICAL REINFORCEMENT LEARNING FOR ZERO-SHOT SKILL COMPOSITION
An obstacle that prevents the wide adoption of (deep) reinforcement learning (RL) in control systems is its need for a large number of interactions with the environment in order to master a skill.
Deep Reinforcement Learning
Hierarchical Reinforcement Learning
+2
Automata-Guided Hierarchical Reinforcement Learning for Skill Composition
Skills learned through (deep) reinforcement learning often generalizes poorly across domains and re-training is necessary when presented with a new task.
Deep Reinforcement Learning
Hierarchical Reinforcement Learning
+2
A Policy Search Method For Temporal Logic Specified Reinforcement Learning Tasks
In this paper, we explore the use of temporal logic (TL) to specify tasks in reinforcement learning.
Crowdsourcing with Sparsely Interacting Workers
We then formulate a weighted rank-one optimization problem to estimate skills based on observations on an irreducible, aperiodic interaction graph.
Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning
In PU learning, a binary classifier is trained from positive (P) and unlabeled (U) data without negative (N) data.
Online Markov decision processes with policy iteration
The online Markov decision process (MDP) is a generalization of the classical Markov decision process that incorporates changing reward functions.
Bandit-Based Task Assignment for Heterogeneous Crowdsourcing
We consider a task assignment problem in crowdsourcing, which is aimed at collecting as many reliable labels as possible within a limited budget.
