Papers Link Prediction
“Link Prediction” 태그가 달린 논문 2,136편 · 필터 해제
Code evolution for link prediction in complex networks
The problem of predicting links in complex networks appears in different disciplines and has led to a variety of ingenious human-designed methods. We use this rich program space to explore the performance and behavior of…
Computational EfficiencyLink PredictionTowards Graph-Based Deep Learning for Map Generalization: Insights from Building Footprints Simplification and Aggregation
Map generalization remains one of the fundamental tasks in cartography, especially for the simplification and aggregation of complex building footprints. This study presents the first exploratory application of graph-bas…
Graph Neural NetworkLink PredictionGraph LearningBeyond Accuracy: Measuring Logical Compliance of Predictive Models
Machine learning models are predominantly evaluated through predictive performance metrics such as ranking quality, prediction error, or classification accuracy. While these metrics effectively quantify how closely predi…
Link PredictionBoundary Embedding Shaping with Adaptive Contrastive Learning for Graph Structural Disentanglement
Graph neural networks (GNNs) excel at aggregating neighbor information for classification, yet their performance is hindered by graph structural entanglement, where spurious correlations from semantically irrelevant neig…
Contrastive LearningNode ClassificationLink PredictionTrust-Aware Multi-Agent Traceability: Confidence-Calibrated Knowledge Graphs for Consistent Software Artifact Management
Multi-agent AI systems are increasingly used to automate software engineering tasks including requirements analysis, architecture design, test generation, and traceability linking. When these agents operate as a sequenti…
Knowledge GraphsLink PredictionTemporal Sheaf Neural Networks with Dynamic Orthogonal Transport
We introduce Temporal Sheaf Neural Networks (TSNN), a temporal link prediction framework that equips each node with a time-varying orthogonal frame and compares node states only after explicit transport between local coo…
Link PredictionOn solving symmetric multi-type orthogonal non-negative matrix tri-factorization problem
We study the symmetric multi-type orthogonal non-negative matrix tri-factorization problem, where several symmetric non-negative matrices are simultaneously approximated by factors of the form $GS_{i}G^{\top}$, with a sh…
Node ClassificationLink PredictionAre Common Substructures Transferable? Riemannian Graph Foundation Model with Neural Vector Bundles
Foundation models have sparked a revolution via a pretraining-adaptation paradigm, with recent efforts extending this success to graphs. Unlike other modalities, graphs contain rich structural patterns, yet their structu…
Link PredictionLink Prediction or Perdition: the Seeds of Instability in Knowledge Graph Embeddings
Embedding models (KGEMs) constitute the main link prediction approach to complete knowledge graphs. Standard evaluation protocols emphasize rank-based metrics such as MRR or Hits@$K$, but usually overlook the influence o…
Knowledge Graph CompletionKnowledge GraphsLink PredictionGraphARC: A Comprehensive Benchmark for Graph-Based Abstract Reasoning
Relational reasoning lies at the heart of intelligence, but existing benchmarks are typically confined to formats such as grids or text. We introduce GraphARC, a benchmark for abstract reasoning on graph-structured data.…
Relational ReasoningNode ClassificationGraph GenerationLink PredictionDG-CoLearn: An Efficient Collaborative Learning Framework for Dynamic Graphs
Dynamic graph learning (DGL) is essential for modelling evolving graph data, but existing methods suffer from significant computational overhead due to repeated full-snapshot retraining and are not well-suited for collab…
Node ClassificationLink PredictionGraph LearningForget Less, Generalize More: Unifying Temporal and Structural Adaptation for Dynamic Graphs
Representation learning on dynamic graphs requires capturing complex dependencies that evolve across both time and structure. Existing approaches typically adopt fixed temporal decay schemes or predetermined structural p…
Representation LearningNode ClassificationLink PredictionTemporal Hyperbolic Graph Representation Learning for Scale-Free Internet Routing and Delay Prediction
Predicting Internet round-trip time (RTT) is critical for routing optimization, quality-of-service (QoS) provisioning, and traffic engineering, yet remains challenging due to long-term temporal dependencies, evolving rou…
Graph Representation LearningLink PredictionGraph LearningApplications of temporal graph learning for predicting the dynamics of biological systems
Biological foundation models have shown strong performance in single-cell representation learning by applying transformer architectures directly to gene-expression matrices. However, these approaches predominantly operat…
Representation LearningLink PredictionGraph Learning'Si'multaneous 'S'patial-'T'emporal Message Passing for Dynamic Graph Representation Learning
Dynamic graph neural networks (DGNNs) that operate on snapshot sequences typically fall into one of two categories. \emph{Temporal-first} approaches build per-node temporal embeddings and only afterwards perform spatial …
Graph Representation LearningLink PredictionGenerative Representation Learning on Hyper-relational Knowledge Graphs via Masked Discrete Diffusion
Hyper-relational knowledge graphs (HKGs) effectively represent complex facts. While inferring new knowledge in HKGs is a critical problem, current methods cast it as a simple link prediction, assuming that nearly all ent…
Representation LearningKnowledge GraphsLink PredictionLeveraging Graph Structure in Seq2Seq Models for Knowledge Graph Link Prediction
We introduce Graph-Augmented Sequence-to-Sequence (GA-S2S), a novel framework that integrates a T5-small encoder-decoder with a Relational Graph Attention Network (RGAT) to improve link prediction in knowledge graphs. Wh…
Knowledge GraphsLink PredictionBayesian Latent Space Models for Graphs Are Misspecified: Toward Robust Inference via Generalized Posteriors
Bayesian latent space models offer a principled approach to network representation, but rely on correct specification of both geometry and link function. Real-world networks often violate these assumptions, exhibiting ge…
Bayesian InferenceLink PredictionInstance Discrimination for Link Prediction
Recently, instance discrimination models have emerged as a major solution for self-supervised learning. Having already demonstrated its effectiveness in the image domain, instance discrimination learning is now proving e…
Self-Supervised LearningNode ClassificationLink PredictionUNR-Explainer: Counterfactual Explanations for Unsupervised Node Representation Learning Models
Node representation learning, such as Graph Neural Networks (GNNs), has emerged as a pivotal method in machine learning. The demand for reliable explanation generation surges, yet unsupervised models remain underexplored…
Representation LearningExplanation GenerationLink Prediction