Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories

2025/01/01 by Chen, Tianyu, Wang, Zeyu, Li, Lin +7 · 16 citations
Computer Science · #Advanced Graph Neural Networks #Domain Adaptation and Few-Shot Learning #Machine Learning and ELM

paper · doi:10.4230/lipics.ecoop.2025.6

openalex created_date 2019/07/30 · openalex publication_date 2025/01/01 · openalex updated_date 2026/07/31

Abstract

Functionality-specific vulnerabilities, which mainly occur in Application Programming Interfaces (APIs) with specific functionalities, are crucial for software developers to detect and avoid. When detecting individual functionality-specific vulnerabilities, the existing two categories of approaches are ineffective because they consider only the API bodies and are unable to handle diverse implementations of functionality-equivalent APIs. To effectively detect functionality-specific vulnerabilities, we propose APISS, the first approach to utilize API doc strings and signatures instead of API bodies. APISS first retrieves functionality-equivalent APIs for APIs with existing vulnerabilities and then migrates Proof-of-Concepts (PoCs) of the existing vulnerabilities for newly detected vulnerable APIs. To retrieve functionality-equivalent APIs, we leverage a Large Language Model for API embedding to improve the accuracy and address the effectiveness and scalability issues suffered by the existing approaches. To migrate PoCs of the existing vulnerabilities for newly detected vulnerable APIs, we design a semi-automatic schema to substantially reduce manual costs. We conduct a comprehensive evaluation to empirically compare APISS with four state-of-the-art approaches of detecting vulnerabilities and two state-of-the-art approaches of retrieving functionality-equivalent APIs. The evaluation subjects include 180 widely used Java repositories using 10 existing vulnerabilities, along with their PoCs. The results show that APISS effectively retrieves functionality-equivalent APIs, achieving a Top-1 Accuracy of 0.81 while the best of the baselines under comparison achieves only 0.55. APISS is highly efficient: the manual costs are within 10 minutes per vulnerability and the end-to-end runtime overhead of testing one candidate API is less than 2 hours. APISS detects 179 new vulnerabilities and receives 60 new CVE IDs, bringing high value to security practice.

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• Temporal Network Embedding with Micro- and Macro-dynamics
• Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
• TSAM: Temporal Link Prediction in Directed Networks based on Self-Attention Mechanism
• RWNE: A Scalable Random-Walk-Based Network Embedding Framework with Personalized Higher-Order Proximity Preserved
• Hierarchical Graph Matching Network for Graph Similarity Computation
• SoGCN: Second-Order Graph Convolutional Networks
• Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany
• Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity
• Parameter Prediction for Unseen Deep Architectures
• Exchange means change: An unsupervised single-temporal change detection framework based on intra- and inter-image patch exchange
• SeanNet: Semantic Understanding Network for Localization Under Object Dynamics
• GraphXCOVID: Explainable Deep Graph Diffusion Pseudo-Labelling for Identifying COVID-19 on Chest X-rays
• Attention-Driven Dynamic Graph Convolutional Network for Multi-Label Image Recognition
• LightCAKE: A Lightweight Framework for Context-Aware Knowledge Graph Embedding
• Contrastive Graph Neural Network Explanation
• Contrastive Neural Architecture Search with Neural Architecture Comparators
• Demand Prediction for Electric Vehicle Sharing
• A Proposal-based Approach for Activity Image-to-Video Retrieval
• Unsupervised Multi-Source Domain Adaptation for Person Re-Identification
• Single-Layer Graph Convolutional Networks For Recommendation
• MSR-GCN: Multi-Scale Residual Graph Convolution Networks for Human Motion Prediction
• GMAN: A Graph Multi-Attention Network for Traffic Prediction
• Reconstruction for Powerful Graph Representations
• Dual-embedding based Neural Collaborative Filtering for Recommender Systems
• A low discrepancy sequence on graphs
• Irregular Convolutional Auto-Encoder on Point Clouds
• Discovering Supply Chain Links with Augmented Intelligence
• Understanding Human Gaze Communication by Spatio-Temporal Graph Reasoning
• Same Object, Different Grasps: Data and Semantic Knowledge for Task-Oriented Grasping
• Towards Feature-free TSP Solver Selection: A Deep Learning Approach
• Anomaly Detection for Aggregated Data Using Multi-Graph Autoencoder
• A Novel Higher-order Weisfeiler-Lehman Graph Convolution
• Accurate Learning of Graph Representations with Graph Multiset Pooling
• Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations
• Fitting the Search Space of Weight-sharing NAS with Graph Convolutional Networks
• Learning Parametrised Graph Shift Operators
• Are Hyperbolic Representations in Graphs Created Equal?
• Revisiting Graph Convolutional Network on Semi-Supervised Node Classification from an Optimization Perspective
• SIRI: Spatial Relation Induced Network For Spatial Description Resolution
• Infinitely Wide Graph Convolutional Networks: Semi-supervised Learning via Gaussian Processes
• Spectral Embedding of Graph Networks
• Adversarial Attacks on Graph Classification via Bayesian Optimisation
• Adversarial Examples on Graph Data: Deep Insights into Attack and Defense
• On the Equivalence of Decoupled Graph Convolution Network and Label Propagation
• Totally Deep Support Vector Machines
• Label-GCN: An Effective Method for Adding Label Propagation to Graph Convolutional Networks
• Atomistic Graph Neural Networks for metals: Application to bcc iron
• SAG-VAE: End-to-end Joint Inference of Data Representations and Feature Relations
• 3D Hand Pose Estimation via Regularized Graph Representation Learning
• Beltrami Flow and Neural Diffusion on Graphs
• Reasoning Visual Dialogs with Structural and Partial Observations
• STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems
• A Two-Step Graph Convolutional Decoder for Molecule Generation
• SPAN: Subgraph Prediction Attention Network for Dynamic Graphs
• Sparse Graph Attention Networks
• VersaGNN: a Versatile accelerator for Graph neural networks
• DialoGraph: Incorporating Interpretable Strategy-Graph Networks into Negotiation Dialogues
• Spectral Clustering with Graph Neural Networks for Graph Pooling
• TME-BNA: Temporal Motif-Preserving Network Embedding with Bicomponent Neighbor Aggregation
• Graph-based Pyramid Global Context Reasoning with a Saliency-aware Projection for COVID-19 Lung Infections Segmentation
• A Fair Comparison of Graph Neural Networks for Graph Classification
• Sequence-guided protein structure determination using graph convolutional and recurrent networks
• I-GCN: Robust Graph Convolutional Network via Influence Mechanism
• Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters
• Principled Simplicial Neural Networks for Trajectory Prediction
• SimPool: Towards Topology Based Graph Pooling with Structural Similarity Features
• Dynamic Relevance Learning for Few-Shot Object Detection
• GNNSampler: Bridging the Gap between Sampling Algorithms of GNN and Hardware
• Graph Neural Network for Traffic Forecasting: A Survey
• Batch Virtual Adversarial Training for Graph Convolutional Networks
• Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer
• Subgraph Neural Networks
• Topology-Aware Correlations Between Relations for Inductive Link Prediction in Knowledge Graphs
• Sampling methods for efficient training of graph convolutional networks: A survey
• Learning to Adapt Invariance in Memory for Person Re-identification
• A Novel GCN based Indoor Localization System with Multiple Access Points
• Learning to Represent Programs with Heterogeneous Graphs
• Decentralized Inference with Graph Neural Networks in Wireless Communication Systems
• Semi-Supervised Node Classification by Graph Convolutional Networks and Extracted Side Information
• NGAT4Rec: Neighbor-Aware Graph Attention Network For Recommendation
• Edge-Enhanced Global Disentangled Graph Neural Network for Sequential Recommendation
• Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation
• Tri-graph Information Propagation for Polypharmacy Side Effect Prediction
• Subgraph Federated Learning with Missing Neighbor Generation
• Detecting Beneficial Feature Interactions for Recommender Systems
• Answering Any-hop Open-domain Questions with Iterative Document Reranking
• Graph Attentional Autoencoder for Anticancer Hyperfood Prediction
• EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks
• GLAM: Graph Learning by Modeling Affinity to Labeled Nodes for Graph Neural Networks
• Point Cloud Processing via Recurrent Set Encoding
• GFCN: A New Graph Convolutional Network Based on Parallel Flows
• Cross-Domain Facial Expression Recognition: A Unified Evaluation Benchmark and Adversarial Graph Learning
• Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks
• Detecting Human-Object Interactions with Action Co-occurrence Priors
• Stable Prediction on Graphs with Agnostic Distribution Shift
• A Survey on The Expressive Power of Graph Neural Networks
• Learning to Evolve on Dynamic Graphs
• Degree-Quant: Quantization-Aware Training for Graph Neural Networks
• Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation
• Graph Warp Module: an Auxiliary Module for Boosting the Power of Graph Neural Networks in Molecular Graph Analysis
• Homogeneous Network Embedding for Massive Graphs via Reweighted Personalized PageRank
• GPS-Net: Graph Property Sensing Network for Scene Graph Generation
• Discourse-level Relation Extraction via Graph Pooling
• Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
• Convolutional Geometric Matrix Completion
• Interpreting and Unifying Graph Neural Networks with An Optimization Framework
• FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing
• Simple and Powerful Architecture for Inductive Recommendation Using Knowledge Graph Convolutions
• Disentangled Representation Learning for 3D Face Shape
• Predicting Hydroxyl Mediated Nucleophilic Degradation and Molecular Stability of RNA Sequences through the Application of Deep Learning Methods
• MAN: Moment Alignment Network for Natural Language Moment Retrieval via Iterative Graph Adjustment
• Regional optimization of new-energy bus charging stations using a hybrid model integrating graph convolutional networks and simulated annealing
• R-VGAE: Relational-variational Graph Autoencoder for Unsupervised Prerequisite Chain Learning
• Enhancing Social Relation Inference with Concise Interaction Graph and Discriminative Scene Representation
• Hierarchical Message-Passing Graph Neural Networks
• Graph Convolutional Memory using Topological Priors
• Semi-supervised Semantic Segmentation with Directional Context-aware Consistency
• StructureNet: Hierarchical Graph Networks for 3D Shape Generation
• Deep Learning on Graphs: A Survey
• Tiered Graph Autoencoders with PyTorch Geometric for Molecular Graphs
• Fundamental tenis : resep meraih kemenangan / Tony Mottram
• Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization
• Investigating ADR mechanisms with knowledge graph mining and explainable AI
• Equivariant Subgraph Aggregation Networks
• Attack Graph Convolutional Networks by Adding Fake Nodes
• An End-to-end Framework for Unconstrained Monocular 3D Hand Pose Estimation
• Smoothness Sensor: Adaptive Smoothness-Transition Graph Convolutions for Attributed Graph Clustering
• NODIS: Neural Ordinary Differential Scene Understanding
• Hierarchical Inter-Message Passing for Learning on Molecular Graphs
• Certified Robustness of Graph Classification against Topology Attack with Randomized Smoothing
• A comparative study of similarity-based and GNN-based link prediction approaches
• Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classification
• TEGDetector: A Phishing Detector that Knows Evolving Transaction Behaviors
• From Anchor Generation to Distribution Alignment: Learning a Discriminative Embedding Space for Zero-Shot Recognition
• Independence Promoted Graph Disentangled Networks
• Neural Trees for Learning on Graphs
• Webly Supervised Image Classification with Self-Contained Confidence
• GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation
• Reward Propagation Using Graph Convolutional Networks
• Continuous Graph Flow
• Hierarchical Human Parsing with Typed Part-Relation Reasoning
• Sparse Nonnegative Matrix Factorization for Multiple Local Community Detection
• Local Augmentation for Graph Neural Networks
• Flex-Convolution (Million-Scale Point-Cloud Learning Beyond Grid-Worlds)
• ISTD-GCN: Iterative Spatial-Temporal Diffusion Graph Convolutional Network for Traffic Speed Forecasting
• WOAD: Weakly Supervised Online Action Detection in Untrimmed Videos
• Geometric Graph Representations and Geometric Graph Convolutions for Deep Learning on Three-Dimensional (3D) Graphs
• Information Obfuscation of Graph Neural Networks
• Walk2Map: Extracting Floor Plans from Indoor Walk Trajectories
• Machine learning in NMR spectroscopy
• Dual Graph Embedding for Object-Tag LinkPrediction on the Knowledge Graph
• Label Contrastive Coding based Graph Neural Network for Graph Classification
• Hierarchical Attention Networks for Medical Image Segmentation
• Bottom-Up Temporal Action Localization with Mutual Regularization
• Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks
• Inverse Graph Identification: Can We Identify Node Labels Given Graph Labels?
• Neural PathSim for Inductive Similarity Search in Heterogeneous Information Networks
• Towards Improved Model Design for Authorship Identification: A Survey on Writing Style Understanding
• ECNU-SenseMaker at SemEval-2020 Task 4: Leveraging Heterogeneous Knowledge Resources for Commonsense Validation and Explanation
• Enhanced Network Embeddings via Exploiting Edge Labels
• Trajectory Prediction using Equivariant Continuous Convolution
• Privacy-Preserving Representation Learning on Graphs: A Mutual Information Perspective
• Dual Side Deep Context-aware Modulation for Social Recommendation
• Aiding Medical Diagnosis Through the Application of Graph Neural Networks to Functional MRI Scans
• BGADAM: Boosting based Genetic-Evolutionary ADAM for Neural Network Optimization
• Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning
• Multi-Modal Graph Neural Network for Joint Reasoning on Vision and Scene Text
• Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs
• Improving Deep Learning Models via Constraint-Based Domain Knowledge: a Brief Survey
• Hybrid Micro/Macro Level Convolution for Heterogeneous Graph Learning
• Entity Linking Meets Deep Learning: Techniques and Solutions
• InfoGCL: Information-Aware Graph Contrastive Learning
• An Empirical Study of Graph Contrastive Learning
• Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks
• Program Classification Using Gated Graph Attention Neural Network for Online Programming Service
• GraphTCN: Spatio-Temporal Interaction Modeling for Human Trajectory Prediction
• p-Laplacian Based Graph Neural Networks
• Infant Cry Classification with Graph Convolutional Networks
• A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices
• Efficient data augmentation using graph imputation neural networks
• PanRep: Graph neural networks for extracting universal node embeddings in heterogeneous graphs
• DIG: A Turnkey Library for Diving into Graph Deep Learning Research
• Weisfeiler-Lehman Embedding for Molecular Graph Neural Networks
• Distance-wise Prototypical Graph Neural Network in Node Imbalance Classification
• Wasserstein Weisfeiler-Lehman Graph Kernels
• Image Annotation based on Deep Hierarchical Context Networks
• Classifying Wikipedia in a fine-grained hierarchy: what graphs can contribute
• Open Knowledge Enrichment for Long-tail Entities
• Inducing Optimal Attribute Representations for Conditional GANs
• Learnable Structural Semantic Readout for Graph Classification
• GRecX: An Efficient and Unified Benchmark for GNN-based Recommendation
• stMMR: accurate and robust spatial domain identification from spatially resolved transcriptomics with multimodal feature representation
• GraphNAS: Graph Neural Architecture Search with Reinforcement Learning
• Dependency-Guided LSTM-CRF for Named Entity Recognition
• Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity
• Graph Convolutional Networks for Temporal Action Localization
• An Ontology-Aware Framework for Audio Event Classification
• Jointly embedding the local and global relations of heterogeneous graph for rumor detection
• Syndrome-aware Herb Recommendation with Multi-Graph Convolution Network
• SStaGCN: Simplified stacking based graph convolutional networks
• How Faithful are Self-Explainable GNNs?
• Multi_CycGT: A Deep Learning-Based Multimodal Model for Predicting the Membrane Permeability of Cyclic Peptides
• A Heterogeneous Dynamical Graph Neural Networks Approach to Quantify Scientific Impact
• A Study of Joint Graph Inference and Forecasting
• Adversarial Privacy Preserving Graph Embedding against Inference Attack
• Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining
• Structured Neural Summarization
• SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction
• Temporal Extension Module for Skeleton-Based Action Recognition
• The Deep Latent Position Topic Model for Clustering and Representation of Networks with Textual Edges
• Walk Message Passing Neural Networks and Second-Order Graph Neural Networks
• Graph Neural Networks Exponentially Lose Expressive Power for Node Classification
• Towards a Taxonomy of Graph Learning Datasets
• Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning
• Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds
• Embedding Symbolic Knowledge into Deep Networks
• A Lightweight Graph Transformer Network for Human Mesh Reconstruction from 2D Human Pose
• The Graph-Based Behavior-Aware Recommendation for Interactive News
• Conditional Attention Networks for Distilling Knowledge Graphs in Recommendation
• Fashion Retrieval via Graph Reasoning Networks on a Similarity Pyramid
• Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language
• Aligning Visual Prototypes with BERT Embeddings for Few-Shot Learning
• Scalable variational Monte Carlo with graph neural ansatz
• A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges
• On the Stability of Graph Convolutional Neural Networks under Edge Rewiring
• Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching
• Deoscillated Graph Collaborative Filtering
• Improving Graph Neural Network Representations of Logical Formulae with Subgraph Pooling
• Isometric Transformation Invariant and Equivariant Graph Convolutional Networks
• Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks
• Some New Layer Architectures for Graph CNN
• GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination
• Design Space for Graph Neural Networks
• Instance-wise Graph-based Framework for Multivariate Time Series Forecasting
• Node2Seq: Towards Trainable Convolutions in Graph Neural Networks
• Node Attribute Generation on Graphs
• Graph Convolutional Networks for traffic anomaly
• Supervised Learning on Relational Databases with Graph Neural Networks
• Expanding Semantic Knowledge for Zero-shot Graph Embedding
• DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters
• Global Attention Improves Graph Networks Generalization
• Weakly-Supervised Image Semantic Segmentation Using Graph Convolutional Networks
• Learning to Cluster Faces via Confidence and Connectivity Estimation
• Lifelong Property Price Prediction: A Case Study for the Toronto Real Estate Market
• Anomaly-resistant Graph Neural Networks via Neural Architecture Search
• A Universal Model for Cross Modality Mapping by Relational Reasoning
• A deep graph convolutional network model of NOx emission prediction for coal‐fired boiler
• Uncertainty Aware Semi-Supervised Learning on Graph Data
• Signed Graph Diffusion Network
• Interstellar: Searching Recurrent Architecture for Knowledge Graph Embedding
• Multi-aspect Graph Contrastive Learning for Review-enhanced Recommendation
• Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter
• Item Tagging for Information Retrieval: A Tripartite Graph Neural Network based Approach
• Learning Elastic Embeddings for Customizing On-Device Recommenders
• Graph Inference Representation: Learning Graph Positional Embeddings with Anchor Path Encoding
• A Hyperbolic-to-Hyperbolic Graph Convolutional Network
• SwiftNet: Using Graph Propagation as Meta-knowledge to Search Highly Representative Neural Architectures
• Neuron with Steady Response Leads to Better Generalization
• Hop-Hop Relation-aware Graph Neural Networks
• Shoestring: Graph-Based Semi-Supervised Learning with Severely Limited Labeled Data
• Cyclic Label Propagation for Graph Semi-supervised Learning
• Accelerating SpMM Kernel with Cache-First Edge Sampling for Graph Neural Networks
• Utilizing virtual power plants to support main grid for frequency regulation
• Joint Use of Node Attributes and Proximity for Semi-Supervised Classification on Graphs
• Feature Correlation Aggregation: on the Path to Better Graph Neural Networks
• Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning
• Graph Attention Collaborative Similarity Embedding for Recommender System
• A Context Integrated Relational Spatio-Temporal Model for Demand and Supply Forecasting
• Graph Convolutional Neural Networks via Motif-based Attention
• Distributionally Robust Semi-Supervised Learning Over Graphs
• EchoEA: Echo Information between Entities and Relations for Entity Alignment
• Can Graph Neural Networks Help Logic Reasoning?
• A 3D Mesh-based Lifting-and-Projection Network for Human Pose Transfer
• Graph Kernels: State-of-the-Art and Future Challenges
• Learning on Random Balls is Sufficient for Estimating (Some) Graph Parameters
• Reinforcement Learning for Autonomous Driving with Latent State Inference and Spatial-Temporal Relationships
• Self-Supervised Deep Graph Embedding with High-Order Information Fusion for Community Discovery
• Demand Forecasting from Spatiotemporal Data with Graph Networks and Temporal-Guided Embedding
• Learning the Multiple Traveling Salesmen Problem with Permutation Invariant Pooling Networks
• Automated Graph Learning via Population Based Self-Tuning GCN
• Neural Bandit with Arm Group Graph
• Mask-GVAE: Blind Denoising Graphs via Partition
• A benchmarking study of embedding-based entity alignment for knowledge graphs
• Time-aware Graph Neural Networks for Entity Alignment between Temporal Knowledge Graphs
• Deep 3D Mesh Watermarking with Self-Adaptive Robustness
• SemCo: Toward Semantic Coherent Visual Relationship Forecasting
• Representation Learning for Natural Language Processing
• Inf-VAE
• A Top-down Supervised Learning Approach to Hierarchical Multi-label Classification in Networks
• Attention-based Clinical Note Summarization
• Text Recognition in the Wild: A Survey
• mSHINE: A Multiple-meta-paths Simultaneous Learning Framework for Heterogeneous Information Network Embedding
• Graph Learning Augmented Heterogeneous Graph Neural Network for Social Recommendation
• A Spatial-Temporal Graph Neural Network Framework for Automated Software Bug Triaging
• Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge
• RPT: Toward Transferable Model on Heterogeneous Researcher Data via Pre-Training
• Graph Space Embedding
• Simultaneous imputation and disease classification in incomplete medical datasets using Multigraph Geometric Matrix Completion (MGMC)
• RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Network
• RA-GCN: Graph convolutional network for disease prediction problems with imbalanced data
• Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs
• Is Graph Structure Necessary for Multi-hop Question Answering?
• Ripple Walk Training: A Subgraph-based training framework for Large and Deep Graph Neural Network
• PushNet: Efficient and Adaptive Neural Message Passing
• Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning
• Representation, learning, and planning algorithms for geometric task and motion planning
• Latent Network Summarization: Bridging Network Embedding and Summarization
• Scalable Graph Neural Networks for Heterogeneous Graphs
• Rethinking Neural Operations for Diverse Tasks
• Multi-Level Attention Pooling for Graph Neural Networks: Unifying Graph Representations with Multiple Localities
• Dual Space Graph Contrastive Learning
• Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition
• Deep Learning-Based Vehicle Behavior Prediction for Autonomous Driving Applications: A Review
• Temporal graph-based approach for behavioural entity classification
• Fairness-Aware Node Representation Learning
• Consensus Clustering: An Embedding Perspective, Extension and Beyond
• A Comparison of Neural Network Architectures for Data-Driven Reduced-Order Modeling
• Identification of patterns in cosmic-ray arrival directions using dynamic graph convolutional neural networks
• Solving NP-Hard Problems on Graphs with Extended AlphaGo Zero
• GraphAIR: Graph representation learning with neighborhood aggregation and interaction
• Predicting Information Pathways Across Online Communities
• When Contrastive Learning Meets Active Learning: A Novel Graph Active Learning Paradigm with Self-Supervision
• Data-driven thresholding in denoising with Spectral Graph Wavelet Transform
• Rational Neural Networks for Approximating Jump Discontinuities of Graph Convolution Operator
• HP-GNN: Generating High Throughput GNN Training Implementation on CPU-FPGA Heterogeneous Platform
• Structural Optimization Makes Graph Classification Simpler and Better
• Memory-Based Graph Networks
• Multi-scale Dynamic Graph Convolutional Network for Hyperspectral Image Classification
• NENET: An Edge Learnable Network for Link Prediction in Scene Text
• Defect Prediction With Semantics and Context Features of Codes Based on Graph Representation Learning
• Deep Learning for Scene Classification: A Survey
• Fish feeding intensity quantification using machine vision and a lightweight 3D ResNet-GloRe network
• Adversarial Attack on Network Embeddings via Supervised Network Poisoning
• Ego-based Entropy Measures for Structural Representations
• Geometric deep learning for computational mechanics Part I: anisotropic hyperelasticity
• Weak Supervision helps Emergence of Word-Object Alignment and improves Vision-Language Tasks
• Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage
• Effective vaccination strategy using graph neural network ansatz
• Predicting Temporal Sets with Deep Neural Networks
• DOM-Q-NET: Grounded RL on Structured Language
• tax2vec: Constructing Interpretable Features from Taxonomies for Short Text Classification
• Neural reverse engineering of stripped binaries using augmented control flow graphs
• The Universal Approximation Property
• Multi-modal Graph Learning for Disease Prediction

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