Dynamic graph embedding
7 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Dynamic graph embedding
Most implemented papers
DynamicGEM: A Library for Dynamic Graph Embedding Methods
DynamicGEM is an open-source Python library for learning node representations of dynamic graphs.
FILDNE: A Framework for Incremental Learning of Dynamic Networks Embeddings
Experimental results on several downstream tasks, over seven real-world data sets, show that FILDNE is able to reduce memory and computational time costs while providing competitive quality measure gains with respect to the contemporary methods for representation learning on dynamic graphs.
K-Core based Temporal Graph Convolutional Network for Dynamic Graphs
jhljx/CTGCN
•
•
Graph representation learning is a fundamental task in various applications that strives to learn low-dimensional embeddings for nodes that can preserve graph topology information.
FeatureNorm: L2 Feature Normalization for Dynamic Graph Embedding
As a matter of fact, this smoothing technique can not only encourage must-link node pairs to get closer but also push cannot-link pairs to shrink together, which potentially cause serious feature shrink or oversmoothing problem, especially when stacking graph convolution in multiple layers or steps.
DynG2G: An Efficient Stochastic Graph Embedding Method for Temporal Graphs
However, recent advances mostly focus on learning node embeddings as deterministic "vectors" for static graphs yet disregarding the key graph temporal dynamics and the evolving uncertainties associated with node embedding in the latent space.
Parameter-free Dynamic Graph Embedding for Link Prediction
Dynamic interaction graphs have been widely adopted to model the evolution of user-item interactions over time.
TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformers
alanjohnvarghese/TransformerG2G
•
•
Hence, incorporating long-range dependencies from the historical graph context plays a crucial role in accurately learning their temporal dynamics.
