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
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.
Permutation-equivariant and Proximity-aware Graph Neural Networks with Stochastic Message Passing
niuchh/smp
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Graph neural networks (GNNs) are emerging machine learning models on graphs.
Disentangling homophily, community structure and triadic closure in networks
Network homophily, the tendency of similar nodes to be connected, and transitivity, the tendency of two nodes being connected if they share a common neighbor, are conflated properties in network analysis, since one mechanism can drive the other.
Accurate Learning of Graph Representations with Graph Multiset Pooling
Graph neural networks have been widely used on modeling graph data, achieving impressive results on node classification and link prediction tasks.
Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning
jrwnter/pigvae
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NeurIPS 2021
In this work we address this issue by proposing a permutation-invariant variational autoencoder for graph structured data.
A Robust and Generalized Framework for Adversarial Graph Embedding
RingBDStack/AGE
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With the prevalence of graph data in real-world applications, many methods have been proposed in recent years to learn high-quality graph embedding vectors various types of graphs.
Pseudo-Riemannian Graph Convolutional Networks
xiongbo010/qgcn
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Empirical results demonstrate that our method outperforms Riemannian counterparts when embedding graphs of complex topologies.
JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs
thu-coai/JointGT
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Findings (ACL) 2021
Existing pre-trained models for knowledge-graph-to-text (KG-to-text) generation simply fine-tune text-to-text pre-trained models such as BART or T5 on KG-to-text datasets, which largely ignore the graph structure during encoding and lack elaborate pre-training tasks to explicitly model graph-text alignments.
TGRNet: A Table Graph Reconstruction Network for Table Structure Recognition
xuewenyuan/TGRNet
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ICCV 2021
A table arranging data in rows and columns is a very effective data structure, which has been widely used in business and scientific research.
Edge Representation Learning with Hypergraphs
harryjo97/EHGNN
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NeurIPS 2021
This dual hypergraph construction allows us to apply message-passing techniques for node representations to edges.
