Papers Network Embedding
“Network Embedding” 태그가 달린 논문 403편 · 필터 해제
GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money Laundering
Money laundering poses a significant challenge as it is estimated to account for 2%-5% of the global GDP. This has compelled regulators to impose stringent controls on financial institutions. One prominent laundering met…
Computational EfficiencyFraud DetectionNetwork EmbeddingNetwork Alignment
Complex networks are frequently employed to model physical or virtual complex systems. When certain entities exist across multiple systems simultaneously, unveiling their corresponding relationships across the networks b…
Graph Neural NetworkNetwork EmbeddingSubset-Contrastive Multi-Omics Network Embedding
Motivation: Network-based analyses of omics data are widely used, and while many of these methods have been adapted to single-cell scenarios, they often remain memory- and space-intensive. As a result, they are better su…
Contrastive LearningNetwork EmbeddingNetwork Embedding Exploration Tool (NEExT)
Many real-world and artificial systems and processes can be represented as graphs. Some examples of such systems include social networks, financial transactions, supply chains, and molecular structures. In many of these …
Graph ClassificationNetwork EmbeddingASD Classification on Dynamic Brain Connectome using Temporal Random Walk with Transformer-based Dynamic Network Embedding
Autism Spectrum Disorder (ASD) is a complex neurological condition characterized by varied developmental impairments, especially in communication and social interaction. Accurate and early diagnosis of ASD is crucial for…
Network EmbeddingTemporal SequencesUnifying Structural Proximity and Equivalence for Enhanced Dynamic Network Embedding
Dynamic network embedding methods transform nodes in a dynamic network into low-dimensional vectors while preserving network characteristics, facilitating tasks such as node classification and community detection. Severa…
Community DetectionNetwork EmbeddingNode ClassificationUnsupervised Attributed Dynamic Network Embedding with Stability Guarantees
Stability for dynamic network embeddings ensures that nodes behaving the same at different times receive the same embedding, allowing comparison of nodes in the network across time. We present attributed unfolded adjacen…
Link PredictionNetwork EmbeddingNode ClassificationPosition+1Adversarial network embedding with bootstrapped representations for sparse networks
The inherent sparsity of real-world networks presents challenges in learning-rich embeddings and accurately reconstructing networks. To address these challenges, a novel method termed Adversarial Network Embedding with B…
Data VisualizationLink PredictionNetwork EmbeddingNode ClassificationStationary distribution of node2vec random walks on household models
The node2vec random walk has proven to be a key tool in network embedding algorithms. These random walks are tuneable, and their transition probabilities depend on the previous visited node and on the triangles containin…
Network EmbeddingEnd-to-End triplet loss based fine-tuning for network embedding in effective PII detection
There are many approaches in mobile data ecosystem that inspect network traffic generated by applications running on user's device to detect personal data exfiltration from the user's device. State-of-the-art methods rel…
feature selectionLarge Language ModelNetwork EmbeddingTripletHeterogeneous Federated Learning Systems for Time-Series Power Consumption Prediction with Multi-Head Embedding Mechanism
Time-series prediction is increasingly popular in a variety of applications, such as smart factories and smart transportation. Researchers have used various techniques to predict power consumption, but existing models la…
Federated LearningNetwork EmbeddingTime SeriesTime Series Prediction+1A novel robust integrating method by high-order proximity for self-supervised attribute network embedding
Attribute network embedding faces significant challenges, primarily integrating heterogeneous information and managing outliers. In this paper, we introduce a novel Robust Integrating Method by High-order Proximity for S…
AttributeData VisualizationGraph Outlier DetectionLink Prediction+2Non-Euclidean Mixture Model for Social Network Embedding
It is largely agreed that social network links are formed due to either homophily or social influence. Inspired by this, we aim at understanding the generation of links via providing a novel embedding-based graph formati…
DecoderGraph Representation LearningmodelNetwork Embedding+1Just Propagate: Unifying Matrix Factorization, Network Embedding, and LightGCN for Link Prediction
Link prediction is a fundamental task in graph analysis. Despite the success of various graph-based machine learning models for link prediction, there lacks a general understanding of different models. In this paper, we …
Graph Neural NetworkLink PredictionNetwork EmbeddingPredictionGeometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds
Rapid growth of high-dimensional datasets in fields such as single-cell RNA sequencing and spatial genomics has led to unprecedented opportunities for scientific discovery, but it also presents unique computational and s…
Metric LearningNetwork Embeddingscientific discoverySimultaneous Weight and Architecture Optimization for Neural Networks
Neural networks are trained by choosing an architecture and training the parameters. The choice of architecture is often by trial and error or with Neural Architecture Search (NAS) methods. While NAS provides some automa…
DecoderNetwork EmbeddingNeural Architecture SearchSimplifying complex machine learning by linearly separable network embedding spaces
Low-dimensional embeddings are a cornerstone in the modelling and analysis of complex networks. However, most existing approaches for mining network embedding spaces rely on computationally intensive machine learning sys…
Information RetrievalNetwork EmbeddingEfficient Network Embedding by Approximate Equitable Partitions
Structural network embedding is a crucial step in enabling effective downstream tasks for complex systems that aims to project a network into a lower-dimensional space while preserving similarities among nodes. We introd…
Network EmbeddingA Survey on Signed Graph Embedding: Methods and Applications
A signed graph (SG) is a graph where edges carry sign information attached to it. The sign of a network can be positive, negative, or neutral. A signed network is ubiquitous in a real-world network like social networks, …
Community DetectionGraph EmbeddingLink PredictionNetwork Embedding+2PSNE: Efficient Spectral Sparsification Algorithms for Scaling Network Embedding
Network embedding has numerous practical applications and has received extensive attention in graph learning, which aims at mapping vertices into a low-dimensional and continuous dense vector space by preserving the unde…
Graph LearningNetwork Embedding