Graph Embedding
476 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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
Libraries
Use these libraries to find Graph Embedding models and implementationsDatasets
BioGRID
LastFM Asia
OLPBENCH
HeriGraph
Synthetic Dynamic Networks
Subtasks
Most implemented papers
Multi-Relational Embedding for Knowledge Graph Representation and Analysis
tranhungnghiep/AnalyzeKGE
•
•
PhD Dissertation, The Graduate University for Advanced Studies, SOKENDAI, Japan 2020
The goal of this thesis is first to study multi-relational embedding on knowledge graphs to propose a new embedding model that explains and improves previous methods, then to study the applications of multi-relational embedding in representation and analysis of knowledge graphs.
MEIM: Multi-partition Embedding Interaction Beyond Block Term Format for Efficient and Expressive Link Prediction
Knowledge graph embedding aims to predict the missing relations between entities in knowledge graphs.
Multi-Paradigm Analysis of Thai Capital Market Linkages: Bivariate/Vine Copulas, Granger Causality, Network Centrality, and Graph Neural Network/Graph Embedding Approaches
Analytically thorough understanding of causal, probabilistic, and informational linkages amongst modern, highly-interconnected capital markets is fundamental to the promotion of capital-market innovation, efficiency, and resilience; whereupon innovative, efficient, and resilient capital markets are fundamental to the sustainable economic development of any nation and the robust financial stability of her economy.
Graph Embedding Techniques, Applications, and Performance: A Survey
palash1992/GEM
•
•
Graphs, such as social networks, word co-occurrence networks, and communication networks, occur naturally in various real-world applications.
KBGAN: Adversarial Learning for Knowledge Graph Embeddings
This framework is independent of the concrete form of generator and discriminator, and therefore can utilize a wide variety of knowledge graph embedding models as its building blocks.
Hyperbolic Entailment Cones for Learning Hierarchical Embeddings
Learning graph representations via low-dimensional embeddings that preserve relevant network properties is an important class of problems in machine learning.
A Simple Baseline Algorithm for Graph Classification
Graph classification has recently received a lot of attention from various fields of machine learning e. g. kernel methods, sequential modeling or graph embedding.
Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation
hwwang55/MKR
•
•
Collaborative filtering often suffers from sparsity and cold start problems in real recommendation scenarios, therefore, researchers and engineers usually use side information to address the issues and improve the performance of recommender systems.
AutoSF: Searching Scoring Functions for Knowledge Graph Embedding
The algorithm is further sped up by a filter and a predictor, which can avoid repeatedly training SFs with same expressive ability and help removing bad candidates during the search before model training.
Graph U-Nets
We further propose the gUnpool layer as the inverse operation of the gPool layer.
