Link Property Prediction

12 papers with code • 6 benchmarks • 1 datasets

This task has no description! Would you like to contribute one?


Most implemented papers

Inductive Representation Learning on Large Graphs

williamleif/GraphSAGE 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

AutoML-4Paradigm/ERAS 26 Apr 2019

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

facebookresearch/SEAL_OGB 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

zhitao-wang/plnlp 6 Dec 2021

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 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 ECML PKDD 2021

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

DeepGraphLearning/NBFNet 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.

VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization

devnkong/VQ-GNN 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 22 Dec 2021

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