Graph Reconstruction

21 papers with code • 0 benchmarks • 1 datasets

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Libraries

Use these libraries to find Graph Reconstruction models and implementations

Most implemented papers

GLEE: Geometric Laplacian Eigenmap Embedding

leotrs/glee 23 May 2019

Graph embedding seeks to build a low-dimensional representation of a graph G. This low-dimensional representation is then used for various downstream tasks.

Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer

google-health/records-research 11 Jun 2019

A recent study showed that using the graphical structure underlying EHR data (e. g. relationship between diagnoses and treatments) improves the performance of prediction tasks such as heart failure prediction.

DynWalks: Global Topology and Recent Changes Awareness Dynamic Network Embedding

houchengbin/DynWalks arXiv 2019

Dynamic network embedding aims to learn low dimensional embeddings for unseen and seen nodes by using any currently available snapshots of a dynamic network.

Vectorizing World Buildings: Planar Graph Reconstruction by Primitive Detection and Relationship Inference

zhangfuyang/Conv-MPN ECCV 2020

This paper tackles a 2D architecture vectorization problem, whose task is to infer an outdoor building architecture as a 2D planar graph from a single RGB image.

GloDyNE: Global Topology Preserving Dynamic Network Embedding

houchengbin/GloDyNE 5 Aug 2020

The main and common objective of Dynamic Network Embedding (DNE) is to efficiently update node embeddings while preserving network topology at each time step.

Symmetric Spaces for Graph Embeddings: A Finsler-Riemannian Approach

fedelopez77/sympa 9 Jun 2021

We propose the systematic use of symmetric spaces in representation learning, a class encompassing many of the previously used embedding targets.

Local2Global: Scaling global representation learning on graphs via local training

LJeub/Local2Global_embedding 26 Jul 2021

Our local2global approach proceeds by first dividing the input graph into overlapping subgraphs (or "patches") and training local representations for each patch independently.

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.

Self-Constructing Graph Convolutional Networks for Semantic Labeling

samleoqh/MSCG-Net 15 Mar 2020

Here, we propose a novel architecture called the Self-Constructing Graph (SCG), which makes use of learnable latent variables to generate embeddings and to self-construct the underlying graphs directly from the input features without relying on manually built prior knowledge graphs.

Permutation-equivariant and Proximity-aware Graph Neural Networks with Stochastic Message Passing

niuchh/smp 5 Sep 2020

Graph neural networks (GNNs) are emerging machine learning models on graphs.