Papers Graph Embedding
“Graph Embedding” 태그가 달린 논문 1,236편 · 필터 해제
Enhancing Distance-Based Graph Autoencoders with Structural Penalties for Dynamic Graph Embedding
Graph autoencoders (GAEs) are widely used for learning representations of dynamic graphs. However, their optimisation objectives typically do not take structural heterogeneity across nodes into account. We propose three …
Graph EmbeddingXGRVFL-MV: Residual-Coupled Graph-Embedded Multi-View Random Vector Functional Link Network with FleXi Guardian Loss
Random Vector Functional Link (RVFL) networks provide an efficient randomized learning framework for classification. Existing multi-view RVFL methods utilize complementary information from multiple views. However, preser…
Graph EmbeddingIntuitionistic Fuzzy Graph Embedded Random Vector Functional Link with Multiview Learning
Random Vector Functional Link (RVFL) networks are popular due to their fast training and universal approximation capabilities. However, RVFL models face challenges in preserving geometric relationships and utilizing mult…
Graph EmbeddingAGE: Adaptive-masking for Graph Embedding in Graph Retrieval-Augmented Generation
GraphRAG is an extension of retrieval-augmented generation (RAG) that supports large language models (LLMs) by referring to graph-structured data as external knowledge. While this technique ideally captures intricate rel…
Self-Supervised LearningGraph EmbeddingFeLoG: Scalable and Efficient Distributed Graph Embedding with Feedback Loop Mechanism
Graph embedding maps graph nodes into low-dimensional vectors to support applications such as recommendation, fraud detection, and graph-based retrieval-augmented generation (GraphRAG). As graphs scale to billions of edg…
Fraud DetectionGraph EmbeddingImplicit Semantic-Aware Communication Based on Hypergraph Reasoning
Semantic-aware communication has emerged as a transformative paradigm for next-generation communication systems, shifting the fundamental goal from transmitting bit-level symbols to reliably recovering and understanding …
Graph EmbeddingSGFormer++: Semantic Graph Transformer for Incremental 3D Scene Graph Generation
In this paper, we propose SGFormer++, a novel Semantic Graph Transformer for 3D scene graph generation (SGG), which aims to parse point cloud scenes into semantic structural graphs, where nodes denote detected object ins…
Scene Graph GenerationGraph EmbeddingAitchison Embeddings for Learning Compositional Graph Representations
Representation learning is central to graph machine learning, powering tasks such as link prediction and node classification. However, most graph embeddings are hard to interpret, offering limited insight into how learne…
Representation LearningNode ClassificationLink PredictionGraph EmbeddingNOMAD: Generating Embeddings for Massive Distributed Graphs
Successful machine learning on graphs or networks requires embeddings that not only represent nodes and edges as low-dimensional vectors but also preserve the graph structure. Established methods for generating embedding…
Graph EmbeddingFrom Load Tests to Live Streams: Graph Embedding-Based Anomaly Detection in Microservice Architectures
Prime Video regularly conducts load tests to simulate the viewer traffic spikes seen during live events such as Thursday Night Football as well as video-on-demand (VOD) events such as Rings of Power. While these stress t…
Anomaly DetectionGraph EmbeddingTIEG-Youpu Solution for NeurIPS 2022 WikiKG90Mv2-LSC
WikiKG90Mv2 in NeurIPS 2022 is a large encyclopedic knowledge graph. Embedding knowledge graphs into continuous vector spaces is important for many practical applications, such as knowledge acquisition, question answerin…
Recommendation SystemsQuestion AnsweringKnowledge GraphsGraph EmbeddingiSatCR: Graph-Empowered Joint Onboard Computing and Routing for LEO Data Delivery
Sending massive Earth observation data produced by low Earth orbit (LEO) satellites back to the ground for processing consumes a large amount of on-orbit bandwidth and exacerbates the space-to-ground link bottleneck. Mos…
Reinforcement LearningGraph EmbeddingOntology-Guided Diffusion for Zero-Shot Visual Sim2Real Transfer
Bridging the simulation-to-reality (sim2real) gap remains challenging as labelled real-world data is scarce. Existing diffusion-based approaches rely on unstructured prompts or statistical alignment, which do not capture…
Graph Neural NetworkGraph EmbeddingThe Value of Graph-based Encoding in NBA Salary Prediction
Market valuations for professional athletes is a difficult problem, given the amount of variability in performance and location from year to year. In the National Basketball Association (NBA), a straightforward way to ad…
Graph EmbeddingOptimization-Free Graph Embedding via Distributional Kernel for Community Detection
Neighborhood Aggregation Strategy (NAS) is a widely used approach in graph embedding, underpinning both Graph Neural Networks (GNNs) and Weisfeiler-Lehman (WL) methods. However, NAS-based methods are identified to be pro…
Community DetectionGraph EmbeddingLIT-GRAPH: Evaluating Deep vs. Shallow Graph Embeddings for High-Quality Text Recommendation in Domain-Specific Knowledge Graphs
This study presents LIT-GRAPH (Literature Graph for Recommendation and Pedagogical Heuristics), a novel knowledge graph-based recommendation system designed to scaffold high school English teachers in selecting diverse, …
Knowledge GraphsLink PredictionGraph EmbeddingDeep Reinforcement Learning for Solving the Fleet Size and Mix Vehicle Routing Problem
The Fleet Size and Mix Vehicle Routing Problem (FSMVRP) is a prominent variant of the Vehicle Routing Problem (VRP), extensively studied in operations research and computational science. FSMVRP requires simultaneous deci…
Computational EfficiencyReinforcement LearningGraph EmbeddingSpectral and Spatial Graph Learning for Multispectral Solar Image Compression
High-fidelity compression of multispectral solar imagery remains challenging for space missions, where limited bandwidth must be balanced against preserving fine spectral and spatial details. We present a learned image c…
Image CompressionGraph EmbeddingGraph LearningHyperbolic Graph Embeddings: a Survey and an Evaluation on Anomaly Detection
This survey reviews hyperbolic graph embedding models, and evaluate them on anomaly detection, highlighting their advantages over Euclidean methods in capturing complex structures. Evaluating models like \textit{HGCAE}, …
Anomaly DetectionGraph EmbeddingTowards Practical Large-scale Dynamical Heterogeneous Graph Embedding: Cold-start Resilient Recommendation
Deploying dynamic heterogeneous graph embeddings in production faces key challenges of scalability, data freshness, and cold-start. This paper introduces a practical, two-stage solution that balances deep graph represent…
Graph EmbeddingGraph Learning