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Network Embedding

Network Embedding, also known as "Network Representation Learning", is a collective term for techniques for mapping graph nodes to vectors of real numbers in a multidimensional space. To be useful, a good embedding should preserve the structure of the graph. The vectors can then be used as input to various network and graph analysis tasks, such as link prediction

Source: Tutorial on NLP-Inspired Network Embedding

Papers

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Showing 101–125 of 403 papers

TitleStatusHype
Gradient-Based Spectral Embeddings of Random Dot Product GraphsCode0
Cross-Network Social User Embedding with Hybrid Differential Privacy GuaranteesCode0
GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network EmbeddingCode0
Graph Representation Learning via Hard and Channel-Wise Attention NetworksCode0
HAHE: Hierarchical Attentive Heterogeneous Information Network EmbeddingCode0
ASD Classification on Dynamic Brain Connectome using Temporal Random Walk with Transformer-based Dynamic Network EmbeddingCode0
Contextual Regression: An Accurate and Conveniently Interpretable Nonlinear Model for Mining Discovery from Scientific DataCode0
GENE: Global Event Network EmbeddingCode0
GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money LaunderingCode0
Accelerating Dynamic Network Embedding with Billions of Parameter Updates to MillisecondsCode0
Name Disambiguation in Anonymized Graphs using Network EmbeddingCode0
Domain-adversarial Network AlignmentCode0
DyCSC: Modeling the Evolutionary Process of Dynamic Networks Based on Cluster StructureCode0
BiasedWalk: Biased Sampling for Representation Learning on GraphsCode0
Dynamic Embedding on Textual Networks via a Gaussian ProcessCode0
Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data ManifoldsCode0
Dynamic Network Embedding via Incremental Skip-gram with Negative SamplingCode0
Benchmarking Network Embedding Models for Link Prediction: Are We Making Progress?Code0
Billion-scale Network Embedding with Iterative Random ProjectionCode0
Flexible Attributed Network EmbeddingCode0
Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information NetworksCode0
Edgeless-GNN: Unsupervised Representation Learning for Edgeless NodesCode0
Are Graph Embeddings the Panacea? An Empirical Survey from the Data Fitness PerspectiveCode0
Font Size: Community Preserving Network EmbeddingCode0
Collaborative Graph Neural Networks for Attributed Network EmbeddingCode0
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