Graph Reconstruction
34 papers with code • 0 benchmarks • 2 datasets
Benchmarks
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Libraries
Use these libraries to find Graph Reconstruction models and implementations
Netzschleuder
NBA player performance prediction dataset
Most implemented papers
GLEE: Geometric Laplacian Eigenmap Embedding
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
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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
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
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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
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
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
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Our local2global approach proceeds by first dividing the input graph into overlapping subgraphs (or "patches") and training local representations for each patch independently.
TranSG: Transformer-Based Skeleton Graph Prototype Contrastive Learning with Structure-Trajectory Prompted Reconstruction for Person Re-Identification
Then, we propose the Graph Prototype Contrastive learning (GPC) to mine the most typical graph features (graph prototypes) of each identity, and contrast the inherent similarity between graph representations and different prototypes from both skeleton and sequence levels to learn discriminative graph representations.
Information Recovery-Driven Deep Incomplete Multiview Clustering Network
Concretely, a two-stage autoencoder network with the self-attention structure is built to synchronously extract high-level semantic representations of multiple views and recover the missing data.
DynamicGEM: A Library for Dynamic Graph Embedding Methods
DynamicGEM is an open-source Python library for learning node representations of dynamic graphs.
