Papers Graph Embedding
“Graph Embedding” 태그가 달린 논문 1,236편 · 필터 해제
Graph Embedding with Mel-spectrograms for Underwater Acoustic Target Recognition
Underwater acoustic target recognition (UATR) is extremely challenging due to the complexity of ship-radiated noise and the variability of ocean environments. Although deep learning (DL) approaches have achieved promisin…
Graph EmbeddingGraph Neural Network Based Adaptive Threat Detection for Cloud Identity and Access Management Logs
The rapid expansion of cloud infrastructures and distributed identity systems has significantly increased the complexity and attack surface of modern enterprises. Traditional rule based or signature driven detection syst…
Graph Neural NetworkGraph EmbeddingHow Do Graph Signals Affect Recommendation: Unveiling the Mystery of Low and High-Frequency Graph Signals
Spectral graph neural networks (GNNs) are highly effective in modeling graph signals, with their success in recommendation often attributed to low-pass filtering. However, recent studies highlight the importance of high-…
Graph EmbeddingTowards Robust DeepFake Detection under Unstable Face Sequences: Adaptive Sparse Graph Embedding with Order-Free Representation and Explicit Laplacian Spectral Prior
Ensuring the authenticity of video content remains challenging as DeepFake generation becomes increasingly realistic and robust against detection. Most existing detectors implicitly assume temporally consistent and clean…
DeepFake DetectionGraph EmbeddingFace DetectionPERM EQ x GRAPH EQ: Equivariant Neural Networks for Quantum Molecular Learning
In hierarchal order of molecular geometry, we compare the performances of Geometric Quantum Machine Learning models. Two molecular datasets are considered: the simplistic linear shaped LiH-molecule and the trigonal pyram…
Quantum Machine LearningGraph EmbeddingDS-Span: Single-Phase Discriminative Subgraph Mining for Efficient Graph Embeddings
Graph representation learning seeks to transform complex, high-dimensional graph structures into compact vector spaces that preserve both topology and semantics. Among the various strategies, subgraph-based methods provi…
Graph Representation LearningGraph EmbeddingLearning Time-Varying Graph Signals via Koopman
A wide variety of real-world data, such as sea measurements, e.g., temperatures collected by distributed sensors and multiple unmanned aerial vehicles (UAV) trajectories, can be naturally represented as graphs, often exh…
Graph EmbeddingImportance Ranking in Complex Networks via Influence-aware Causal Node Embedding
Understanding and quantifying node importance is a fundamental problem in network science and engineering, underpinning a wide range of applications such as influence maximization, social recommendation, and network dism…
Representation LearningGraph EmbeddingResource Allocation in Hybrid Radio-Optical IoT Networks using GNN with Multi-task Learning
This paper addresses the problem of dual-technology scheduling in hybrid Internet-of-Things (IoT) networks that integrate Optical Wireless Communication (OWC) with Radio Frequency (RF). We first present an optimization f…
Graph Neural NetworkMulti-Task LearningGraph EmbeddingNeighborhood-Adaptive Generalized Linear Graph Embedding with Latent Pattern Mining
Graph embedding has been widely applied in areas such as network analysis, social network mining, recommendation systems, and bioinformatics. However, current graph construction methods often require the prior definition…
Recommendation SystemsGraph EmbeddingGraph LearningGraph2Region: Efficient Graph Similarity Learning with Structure and Scale Restoration
Graph similarity is critical in graph-related tasks such as graph retrieval, where metrics like maximum common subgraph (MCS) and graph edit distance (GED) are commonly used. However, exact computations of these metrics …
Graph SimilarityGraph EmbeddingNear-Real-Time Resource Slicing for QoS Optimization in 5G O-RAN using Deep Reinforcement Learning
Open-Radio Access Network (O-RAN) has become an important paradigm for 5G and beyond radio access networks. This paper presents an xApp called xSlice for the Near-Real-Time (Near-RT) RAN Intelligent Controller (RIC) of 5…
Reinforcement LearningGraph EmbeddingRepresentation Learning on Large Non-Bipartite Transaction Networks using GraphSAGE
Financial institutions increasingly require scalable tools to analyse complex transactional networks, yet traditional graph embedding methods struggle with dynamic, real-world banking data. This paper demonstrates the pr…
Representation LearningGraph Neural NetworkFraud DetectionGraph EmbeddingiMatcher: Improve matching in point cloud registration via local-to-global geometric consistency learning
This paper presents iMatcher, a fully differentiable framework for feature matching in point cloud registration. The proposed method leverages learned features to predict a geometrically consistent confidence matrix, inc…
Point Cloud RegistrationGraph EmbeddingPose EstimationLMAE4Eth: Generalizable and Robust Ethereum Fraud Detection by Exploring Transaction Semantics and Masked Graph Embedding
Current Ethereum fraud detection methods rely on context-independent, numerical transaction sequences, failing to capture semantic of account transactions. Furthermore, the pervasive homogeneity in Ethereum transaction r…
Self-Supervised LearningContrastive LearningGraph EmbeddingFraud DetectionHybrid Topic-Semantic Labeling and Graph Embeddings for Unsupervised Legal Document Clustering
Legal documents pose unique challenges for text classification due to their domain-specific language and often limited labeled data. This paper proposes a hybrid approach for classifying legal texts by combining unsuperv…
Text ClassificationGraph EmbeddingOpenM3D: Open Vocabulary Multi-view Indoor 3D Object Detection without Human Annotations
Open-vocabulary (OV) 3D object detection is an emerging field, yet its exploration through image-based methods remains limited compared to 3D point cloud-based methods. We introduce OpenM3D, a novel open-vocabulary multi…
3D Object DetectionGraph EmbeddingNatural Image Classification via Quasi-Cyclic Graph Ensembles and Random-Bond Ising Models at the Nishimori Temperature
Modern multi-class image classification uses high-dimensional CNN features that incur large memory and computational costs and obscure the data manifold's geometry. Existing graph-based spectral classifiers work on synth…
Image ClassificationGraph EmbeddingSVDformer: Direction-Aware Spectral Graph Embedding Learning via SVD and Transformer
Directed graphs are widely used to model asymmetric relationships in real-world systems. However, existing directed graph neural networks often struggle to jointly capture directional semantics and global structural patt…
Graph Representation LearningNode ClassificationGraph EmbeddingMatch & Choose: Model Selection Framework for Fine-tuning Text-to-Image Diffusion Models
Text-to-image (T2I) models based on diffusion and transformer architectures advance rapidly. They are often pretrained on large corpora, and openly shared on a model platform, such as HuggingFace. Users can then build up…
Graph Embedding