Dynamic graph embedding

10 papers with code • 0 benchmarks • 0 datasets

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Most implemented papers

DynamicGEM: A Library for Dynamic Graph Embedding Methods

palash1992/DynamicGEM 26 Nov 2018

DynamicGEM is an open-source Python library for learning node representations of dynamic graphs.

FILDNE: A Framework for Incremental Learning of Dynamic Networks Embeddings

fildne/fildne 6 Apr 2019

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 22 Mar 2020

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

marlin-codes/FeatureNorm 27 Feb 2021

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

gracexu182/dyng2g 28 Sep 2021

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

fudancisl/freegem 15 Oct 2022

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 5 Jul 2023

Hence, incorporating long-range dependencies from the historical graph context plays a crucial role in accurately learning their temporal dynamics.

Valid Conformal Prediction for Dynamic GNNs

edwarddavis1/valid_conformal_for_dynamic_gnn 29 May 2024

Using this, we extend the validity of conformal prediction to dynamic GNNs in both transductive and semi-inductive regimes.

DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphs

zjs123/DTGB 17 Jun 2024

Moreover, we conduct extensive benchmark experiments on DTGB, evaluating 7 popular dynamic graph learning algorithms and their variants of adapting to text attributes with LLM embeddings, along with 6 powerful large language models (LLMs).

Empowering Interdisciplinary Insights with Dynamic Graph Embedding Trajectories

Ahren09/dygetviz 25 Jun 2024

Our work not only addresses the ongoing challenges in visualizing and analyzing DTDG models but also establishes a foundational framework for future investigations into dynamic graph representation and analysis across various disciplines.