Graph Representation Learning
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Benchmarks
COMA
Most implemented
How Powerful are Graph Neural Networks?
Hierarchical Graph Representation Learning with Differentiable Pooling
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
GraphSAINT: Graph Sampling Based Inductive Learning Method
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
Fast Graph Representation Learning with PyTorch Geometric
Papers
Repurposing Unified Topological Signatures for Graph Representation Learning
Message-passing Graph Neural Networks (GNNs) iteratively propagate and aggregate local neighborhood information followed by global readout to learn graph representations. However, their discriminative power is upper-boun…
Graph Representation LearningGraph ClassificationDynamic Heterogeneous Graph Representation Learning: A Survey
Graph representation learning (GRL) serves as a canonical paradigm for modeling complex networks. However, real-world AI systems inherently manifest as evolving heterogeneous entities with complex interactions, posing si…
Graph Representation LearningGraph Neural NetworkBeyond Flat Netlist: Hierarchical Graph Representation Learning for Scalable Analysis of Sequential Circuits
Circuit Representation Learning (CRL) offers a powerful paradigm to guide and optimize core Electronic Design Automation (EDA) tasks, but its practical adoption is hindered by the immense scale of industrial netlists and…
Graph Representation LearningGraph Neural NetworkRAD: Rule-Augmented Relational Anomaly Detection
Anomaly detection is often applied to data stored in relational databases, yet most existing methods require flattening multiple tables into a single feature matrix. This flattening can obscure entity identity, schema st…
Graph Representation LearningAnomaly DetectionGraph Representation Learning of Lightweight IoT Ciphers
SIMON and SIMECK belong to a family of Lightweight Cryptographic Algorithms (LCAs) based on the Feistel block cipher, designed for Internet of Things (IoT) devices. As with all Feistel ciphers, they are susceptible to di…
Graph Representation LearningFeature EngineeringHP-JEPA: Hierarchical Partitioning for Multi-Resolution Graph Joint-Embedding Predictive Learning
Graph self-supervised learning aims to learn transferable representations from large-scale unlabeled graph data. Joint-embedding predictive architectures (JEPAs) avoid explicit negative-pair construction and raw-input re…
Graph Representation LearningSelf-Supervised LearningGraph ClassificationGraph Regression