Papers Link Property Prediction
“Link Property Prediction” 태그가 달린 논문 14편 · 필터 해제
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+1VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization
Most state-of-the-art Graph Neural Networks (GNNs) can be defined as a form of graph convolution which can be realized by message passing between direct neighbors or beyond. To scale such GNNs to large graphs, various ne…
Link PredictionLink Property PredictionNode ClassificationQuantizationRelation 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). In this paper, we propose a new self-supervised training objective for multi-relational graph representation learn…
Graph Representation LearningKnowledge Base CompletionKnowledge Graph CompletionKnowledge Graph Embedding+6Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction
Link prediction is a very fundamental task on graphs. Inspired by traditional path-based methods, in this paper we propose a general and flexible representation learning framework based on paths for link prediction. Spec…
Graph Neural NetworkInductive Relation PredictionKnowledge Graph CompletionLink Prediction+1Embedding Knowledge Graphs Attentive to Positional and Centrality Qualities
Knowledge graphs embeddings (KGE) are lately at the center of many artificial intelligence studies due to their applicability for solving downstream tasks, including link prediction and node classification. However, most…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsLink Prediction+2Distance-Enhanced Graph Neural Network for Link Prediction
Link prediction, which is to predict the existence of a link/edge between two vertices in a graph, is a classical problem in machine learning. Intuitively, if it takes a long distance to walk from u to v along the existi…
Graph Neural NetworkLink PredictionLink Property PredictionPredictionLabeling 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). We kno…
General ClassificationGraph ClassificationLink PredictionLink Property Prediction+2AutoSF: Searching Scoring Functions for Knowledge Graph Embedding
Scoring functions (SFs), which measure the plausibility of triplets in knowledge graph (KG), have become the crux of KG embedding. Lots of SFs, which target at capturing different kinds of relations in KGs, have been des…
AutoMLGraph EmbeddingKnowledge Graph EmbeddingLink Prediction+1Inductive 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. However, most existing approaches require t…
Graph ClassificationGraph RegressionLink PredictionLink Property Prediction+4