Link Property Prediction
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Benchmarks
ogbl-collab
ogbl-ddi
ogbl-wikikg2
ogbl-ppa
ogbl-citation2
ogbl-biokg
Most implemented
Inductive Representation Learning on Large Graphs
Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction
AutoSF: Searching Scoring Functions for Knowledge Graph Embedding
Pairwise Learning for Neural Link Prediction
Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning
Temporal graph models fail to capture global temporal dynamics
Papers
Revisiting Node Affinity Prediction in Temporal Graphs
Node affinity prediction is a common task that is widely used in temporal graph learning with applications in social and financial networks, recommender systems, and more. Recent works have addressed this task by adaptin…
Link Property PredictionGraph LearningEdge2Node: Reducing Edge Prediction to Node Classification
Despite the success of graph neural network models in node classification, edge prediction (the task of predicting missing or potential links between nodes in a graph) remains a challenging problem for these models. A co…
Graph Neural NetworkLink PredictionLink Property PredictionNode Classification+1Temporal graph models fail to capture global temporal dynamics
A recently released Temporal Graph Benchmark is analyzed in the context of Dynamic Link Property Prediction. We outline our observations and propose a trivial optimization-free baseline of "recently popular nodes" outper…
Link Property PredictionProperty PredictionPath-aware Siamese Graph Neural Network for Link Prediction
In this paper, we propose a Path-aware Siamese Graph neural network(PSG) for link prediction tasks. First, PSG captures both nodes and edge features for given two nodes, namely the structure information of k-neighborhood…
Contrastive LearningGraph Neural NetworkLink PredictionLink Property Prediction+2D-HYPR: Harnessing Neighborhood Modeling and Asymmetry Preservation for Digraph Representation Learning
Digraph Representation Learning (DRL) aims to learn representations for directed homogeneous graphs (digraphs). Prior work in DRL is largely constrained (e.g., limited to directed acyclic graphs), or has poor generalizab…
Link PredictionLink Property PredictionNode ClassificationProperty Prediction+1Pairwise Learning for Neural Link Prediction
In this paper, we aim at providing an effective Pairwise Learning Neural Link Prediction (PLNLP) framework. The framework treats link prediction as a pairwise learning to rank problem and consists of four main components…
Learning-To-RankLink PredictionLink Property PredictionPrediction+1