Link Property Prediction
12 papers with code • 6 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Link Property Prediction
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OGB
Most implemented papers
Inductive Representation Learning on Large Graphs
williamleif/GraphSAGE
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NeurIPS 2017
Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions.
AutoSF: Searching Scoring Functions for Knowledge Graph Embedding
The algorithm is further sped up by a filter and a predictor, which can avoid repeatedly training SFs with same expressive ability and help removing bad candidates during the search before model training.
Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction
DeepGraphLearning/NBFNet
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NeurIPS 2021
To further improve the capacity of the path formulation, we propose the Neural Bellman-Ford Network (NBFNet), a general graph neural network framework that solves the path formulation with learned operators in the generalized Bellman-Ford algorithm.
Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning
facebookresearch/SEAL_OGB
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NeurIPS 2021
In this paper, we provide a theory of using graph neural networks (GNNs) for multi-node representation learning (where we are interested in learning a representation for a set of more than one node, such as link).
Pairwise Learning for Neural Link Prediction
The framework treats link prediction as a pairwise learning to rank problem and consists of four main components, i. e., neighborhood encoder, link predictor, negative sampler and objective function.
Distance-Enhanced Graph Neural Network for Link Prediction
lbn187/DLGNN
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NA 2021
To overcome this difficulty, we propose an anchorbased distance: First, we randomly select K anchor vertices from the graph and then calculate the shortest distances of all vertices in the graph to them.
Embedding Knowledge Graphs Attentive to Positional and Centrality Qualities
afshinsadeghi/GFA-NN
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ECML PKDD 2021
Capturing not only local graph structure but global features of entities are crucial for prediction tasks on Knowledge Graphs.
Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations
facebookresearch/ssl-relation-prediction
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AKBC 2021
Learning good representations on multi-relational graphs is essential to knowledge base completion (KBC).
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization
devnkong/VQ-GNN
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NeurIPS 2021
Our framework avoids the "neighbor explosion" problem of GNNs using quantized representations combined with a low-rank version of the graph convolution matrix.
D-HYPR: Harnessing Neighborhood Modeling and Asymmetry Preservation for Digraph Representation Learning
hongluzhou/dhypr
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Digraph Representation Learning (DRL) aims to learn representations for directed homogeneous graphs (digraphs).
