26 papers with code • 0 benchmarks • 2 datasets
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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
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
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
Our local2global approach proceeds by first dividing the input graph into overlapping subgraphs (or "patches") and training local representations for each patch independently.
Information Recovery-Driven Deep Incomplete Multi-view 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.
Self-Constructing Graph Convolutional Networks for Semantic Labeling
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.