# Link Property Prediction

12 papers with code • 6 benchmarks • 1 datasets

## Most implemented papers

# Inductive Representation Learning on Large Graphs

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.

# Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning

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

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

Capturing not only local graph structure but global features of entities are crucial for prediction tasks on Knowledge Graphs.

# Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction

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.

# Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations

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

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

Digraph Representation Learning (DRL) aims to learn representations for directed homogeneous graphs (digraphs).