Graph Embedding
474 papers with code • 1 benchmarks • 11 datasets
Graph embeddings learn a mapping from a network to a vector space, while preserving relevant network properties.
( Image credit: GAT )
Benchmarks
These leaderboards are used to track progress in Graph Embedding
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Libraries
Use these libraries to find Graph Embedding models and implementationsDatasets
BioGRID
LastFM Asia
OLPBENCH
HeriGraph
Synthetic Dynamic Networks
Subtasks
Latest papers
Block-Diagonal Orthogonal Relation and Matrix Entity for Knowledge Graph Embedding
The primary aim of Knowledge Graph embeddings (KGE) is to learn low-dimensional representations of entities and relations for predicting missing facts.
Hierarchical Aggregations for High-Dimensional Multiplex Graph Embedding
abdouskamel/hmge
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To address these issues, we propose HMGE, a novel embedding method based on hierarchical aggregation for high-dimensional multiplex graphs.
LGMRec: Local and Global Graph Learning for Multimodal Recommendation
The multimodal recommendation has gradually become the infrastructure of online media platforms, enabling them to provide personalized service to users through a joint modeling of user historical behaviors (e. g., purchases, clicks) and item various modalities (e. g., visual and textual).
RDF-star2Vec: RDF-star Graph Embeddings for Data Mining
Knowledge Graphs (KGs) such as Resource Description Framework (RDF) data represent relationships between various entities through the structure of triples (<subject, predicate, object>).
Do Similar Entities have Similar Embeddings?
nicolas-hbt/similar-embeddings
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A common tacit assumption is the KGE entity similarity assumption, which states that these KGEMs retain the graph's structure within their embedding space, \textit{i. e.}, position similar entities within the graph close to one another.
OCGEC: One-class Graph Embedding Classification for DNN Backdoor Detection
jhy549/ocgec
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We then pre-train a generative self-supervised graph autoencoder (GAE) to better learn the features of benign models in order to detect backdoor models without knowing the attack strategy.
Normed Spaces for Graph Embedding
andyweizhao/graphs-normed-spaces
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Theoretical results from discrete geometry suggest that normed spaces can abstractly embed finite metric spaces with surprisingly low theoretical bounds on distortion in low dimensions.
Graph Coordinates and Conventional Neural Networks -- An Alternative for Graph Neural Networks
We propose Topology Coordinate Neural Network (TCNN) and Directional Virtual Coordinate Neural Network (DVCNN) as novel and efficient alternatives to message passing GNNs, that directly leverage the graph's topology, sidestepping the computational challenges presented by competing algorithms.
Visualizing DNA reaction trajectories with deep graph embedding approaches
Synthetic biologists and molecular programmers design novel nucleic acid reactions, with many potential applications.
Contrastive Deep Nonnegative Matrix Factorization for Community Detection
6lyc/cdnmf
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Recently, nonnegative matrix factorization (NMF) has been widely adopted for community detection, because of its better interpretability.
