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
| Trend | Dataset | Best Model | Paper | Code | Compare |
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Libraries
Use these libraries to find Graph Embedding models and implementationsDatasets
BioGRID
LastFM Asia
OLPBENCH
HeriGraph
Synthetic Dynamic Networks
Subtasks
Most implemented papers
struc2vec: Learning Node Representations from Structural Identity
Implementation and experiments of graph embedding algorithms. deep walk, LINE(Large-scale Information Network Embedding), node2vec, SDNE(Structural Deep Network Embedding), struc2vec
Spherical and Hyperbolic Toric Topology-Based Codes On Graph Embedding for Ising MRF Models: Classical and Quantum Topology Machine Learning
Additionally, the layer depth in QAOA correlates to the number of decoding belief propagation iterations in the Wiberg decoding tree.
Adversarially Regularized Graph Autoencoder for Graph Embedding
Ruiqi-Hu/ARGA
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Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics.
Representation Learning for Attributed Multiplex Heterogeneous Network
cenyk1230/GATNE
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Network embedding (or graph embedding) has been widely used in many real-world applications.
Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations
Our experimental results demonstrate that the recent graph embedding methods achieve promising results and deserve more attention in the future biomedical graph analysis.
Knowledge Graph Embedding for Ecotoxicological Effect Prediction
Erik-BM/NIVAUC
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A knowledge graph has been constructed from publicly available data sets, including a species taxonomy and chemical classification and similarity.
Composition-based Multi-Relational Graph Convolutional Networks
malllabiisc/CompGCN
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ICLR 2020
Multi-relational graphs are a more general and prevalent form of graphs where each edge has a label and direction associated with it.
Interstellar: Searching Recurrent Architecture for Knowledge Graph Embedding
AutoML-4Paradigm/Interstellar
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NeurIPS 2020
In this work, based on the relational paths, which are composed of a sequence of triplets, we define the Interstellar as a recurrent neural architecture search problem for the short-term and long-term information along the paths.
Fast Sequence-Based Embedding with Diffusion Graphs
A graph embedding is a representation of graph vertices in a low-dimensional space, which approximately preserves properties such as distances between nodes.
Inductive Representation Learning on Temporal Graphs
StatsDLMathsRecomSys/Inductive-representation-learning-on-temporal-graphs
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ICLR 2020
Moreover, node and topological features can be temporal as well, whose patterns the node embeddings should also capture.
