Papers Link Prediction
“Link Prediction” 태그가 달린 논문 2,136편 · 필터 해제
ArtifactLinker: Linking Scientific Artifacts for Automatic State-of-the-Art Discovery
Scientific artifacts such as models and datasets are foundations for research. With the rapid growth of platforms like HuggingFace, researchers now have access to a large number of artifacts. Yet, a key challenge remains…
Link PredictionDecoupled and Divergence-Conditioned Prompt for Multi-domain Dynamic Graph Foundation Models
Dynamic graphs are ubiquitous in real-world systems, and building generalizable dynamic Graph Foundation Models has become a frontier in graph learning. However, dynamic graphs from different domains pose fundamental cha…
Node ClassificationDomain AdaptationLink PredictionGraph LearningA Unified Perspective for Learning Graph Representations Across Multi-Level Abstractions
Graph Self-Supervised Learning (GSSL) has emerged as a powerful paradigm for generating high-quality representations for graph-structured data. While multi-scale graph contrastive learning has received increasing attenti…
Self-Supervised LearningContrastive LearningLink PredictionRank Is Not Capacity: Spectral Occupancy for Latent Graph Models
Graph representation learning has become a standard approach for analyzing networked data, with latent embeddings widely used for link prediction, community detection, and related tasks. Yet a basic design choice, the la…
Graph Representation LearningCommunity DetectionLink PredictionCORE: Cyclic Orthotope Relation Embedding for Knowledge Graph Completion
Knowledge graph completion (KGC) aims to automatically infer missing facts in multi-relational data by mapping entities and relations into continuous representation spaces. Recent region-based embedding models have shown…
Knowledge Graph CompletionLink PredictionGravityGraphSAGE: Link Prediction in Directed Attributed Graphs
Link prediction (inferring missing or future connections between nodes in a graph) is a fundamental problem in network science with widespread applications in, e.g., biological systems, recommender systems, finance and c…
Link PredictionPRISM: Iterative Cross-Modal Posterior Refinement for Dynamic Text-Attributed Graphs
Dynamic text-attributed graphs (DyTAGs) provide a powerful framework for modeling evolving systems in which node semantics and time-dependent interactions are tightly coupled. Recently, multimodal learning has emerged as…
Representation LearningLink PredictionGraphlets as Building Blocks for Structural Vocabulary in Knowledge Graph Foundation Models
Foundation models excel at language, where sentences become tokens, and vision, where images become pixels, because both reduce to discrete symbols on a shared, fixed grid. Knowledge Graphs share the discreteness, but no…
Knowledge GraphsLink PredictionTraXion: Rethinking Pre-training Frameworks for Mobility and Beyond
Human mobility differs from text and from generic time series in three structural ways: visits are tuple-valued events whose meaning depends on the joint distribution over location, time, and activity; users carry persis…
Anomaly DetectionLink PredictionICU MortalityAdvancing Edge Classification through High-Dimensional Causal Modeling of Node-Edge Interplay
Edge classification, a crucial task for graph applications, remains relatively under-explored compared to link prediction. Current methods often overlook the potential causal influences of node features on edge features,…
Graph Neural NetworkCausal InferenceLink PredictionAitchison Embeddings for Learning Compositional Graph Representations
Representation learning is central to graph machine learning, powering tasks such as link prediction and node classification. However, most graph embeddings are hard to interpret, offering limited insight into how learne…
Representation LearningNode ClassificationLink PredictionGraph EmbeddingTopological Neural Tangent Kernel
Graph neural tangent kernels give a principled infinite-width theory for graph neural networks, but inherit a basic limitation of graph models: they see only pairwise structure. Many relational systems contain higher-ord…
Link PredictionA Unified Framework of Hyperbolic Graph Representation Learning Methods
Hyperbolic geometry has emerged as an effective latent space for representing complex networks, owing to its ability to capture hierarchical organization and heterogeneous connectivity patterns using low-dimensional embe…
Graph Representation LearningNode ClassificationLink PredictionMini-Batch Class Composition Bias in Link Prediction
Prior work on node classification has shown that Graph Neural Networks (GNNs) can learn representations that transfer across graphs, when underlying graph properties are shared. For a fixed graph, one would then expect G…
Node ClassificationLink PredictionMochi: Aligning Pre-training and Inference for Efficient Graph Foundation Models via Meta-Learning
We propose Mochi, a Graph Foundation Model that addresses task unification and training efficiency by adopting a meta-learning based training framework. Prior models pre-train with reconstruction-based objectives such as…
Graph ClassificationNode ClassificationLink PredictionEvaluating Assurance Cases as Text-Attributed Graphs for Structure and Provenance Analysis
An assurance case is a structured argument document that justifies claims about a system's requirements or properties, which are supported by evidence. In regulated domains, these are crucial for meeting compliance and s…
Graph ClassificationLink PredictionSpectral Embeddings Leak Graph Topology: Theory, Benchmark, and Adaptive Reconstruction
Graph Neural Networks (GNNs) excel on relational data, but standard benchmarks unrealistically assume the graph is centrally available. In practice, settings such as Federated Graph Learning, distributed systems, and pri…
Node ClassificationLink PredictionGraph LearningHopRank: Self-Supervised LLM Preference-Tuning on Graphs for Few-Shot Node Classification
Node classification on text-attributed graphs (TAGs) is a fundamental task with broad applications in citation analysis, social networks, and recommendation systems. Current GNN-based approaches suffer from shallow text …
Recommendation SystemsNode ClassificationLink PredictionLearning Chain Of Thoughts Prompts for Predicting Entities, Relations, and even Literals on Knowledge Graphs
Knowledge graph embedding (KGE) models perform well on link prediction but struggle with unseen entities, relations, and especially literals, limiting their use in dynamic, heterogeneous graphs. In contrast, pretrained l…
Knowledge Graph EmbeddingKnowledge GraphsLink PredictionAgentGL: Towards Agentic Graph Learning with LLMs via Reinforcement Learning
Large Language Models (LLMs) increasingly rely on agentic capabilities-iterative retrieval, tool use, and decision-making-to overcome the limits of static, parametric knowledge. Yet existing agentic frameworks treat exte…
Reinforcement LearningNode ClassificationLink PredictionGraph Learning