Papers Dynamic Link Prediction
“Dynamic Link Prediction” 태그가 달린 논문 50편 · 필터 해제
Learning Long Range Spatio-Temporal Representations over Continuous Time Dynamic Graphs with State Space Models
Continuous-time dynamic graphs (CTDGs) provide a richer framework to capture fine-grained temporal patterns in evolving relational data. Long-range information propagation is a key challenge while learning representation…
Dynamic Link PredictionNode ClassificationSpatial ReasoningDynamic Link Prediction with Temporally Enhanced Signed Graph Neural Networks
Temporal signed networks (TSNs) model the time evolution of cooperative and adversarial relationships that arise in applications such as social media analysis, trust and reputation systems, and financial transaction netw…
Dynamic Link PredictionTrajectory ModelingA2QTGN: Adaptive Amplitude Quantum-Integrated Temporal Graph Network for Dynamic Link Prediction
Dynamic link prediction is important for modeling evolving interactions in complex systems, including social, communication, financial, and transportation networks. Classical temporal graph models capture sequential depe…
Dynamic Link PredictionGraph LearningTFWaveFormer: Temporal-Frequency Collaborative Multi-level Wavelet Transformer for Dynamic Link Prediction
Dynamic link prediction plays a crucial role in diverse applications including social network analysis, communication forecasting, and financial modeling. While recent Transformer-based approaches have demonstrated promi…
Dynamic Link PredictionGraph LearningData-centric Prompt Tuning for Dynamic Graphs
Dynamic graphs have attracted increasing attention due to their ability to model complex and evolving relationships in real-world scenarios. Traditional approaches typically pre-train models using dynamic link prediction…
Dynamic Link PredictionDeXposure: A Dataset and Benchmarks for Inter-protocol Credit Exposure in Decentralized Financial Networks
We curate the DeXposure dataset, the first large-scale dataset for inter-protocol credit exposure in decentralized financial networks, covering global markets of 43.7 million entries across 4.3 thousand protocols, 602 bl…
Dynamic Link PredictionGraph ClusteringGlobal-Lens Transformers: Adaptive Token Mixing for Dynamic Link Prediction
Dynamic graph learning plays a pivotal role in modeling evolving relationships over time, especially for temporal link prediction tasks in domains such as traffic systems, social networks, and recommendation platforms. W…
Dynamic Link PredictionGraph LearningTAWRMAC: A Novel Dynamic Graph Representation Learning Method
Dynamic graph representation learning has become essential for analyzing evolving networks in domains such as social network analysis, recommendation systems, and traffic analysis. However, existing continuous-time metho…
Graph Representation LearningDynamic Link PredictionRecommendation SystemsNode ClassificationHyperEvent: A Strong Baseline for Dynamic Link Prediction via Relative Structural Encoding
Learning representations for continuous-time dynamic graphs is critical for dynamic link prediction. While recent methods have become increasingly complex, the field lacks a strong and informative baseline to reliably ga…
Dynamic Link PredictionHigher-order Structure Boosts Link Prediction on Temporal Graphs
Temporal Graph Neural Networks (TGNNs) have gained growing attention for modeling and predicting structures in temporal graphs. However, existing TGNNs primarily focus on pairwise interactions while overlooking higher-or…
Dynamic Link PredictionGraph LearningGraph Neural NetworkHypergraph representations+1A PID-Controlled Tensor Wheel Decomposition Model for Dynamic Link Prediction
Link prediction in dynamic networks remains a fundamental challenge in network science, requiring the inference of potential interactions and their evolving strengths through spatiotemporal pattern analysis. Traditional …
Dynamic Link PredictionLink PredictionPredictionPredict Confidently, Predict Right: Abstention in Dynamic Graph Learning
Many real-world systems can be modeled as dynamic graphs, where nodes and edges evolve over time, requiring specialized models to capture their evolving dynamics in risk-sensitive applications effectively. Temporal graph…
Abstention PredictionDynamic Link PredictionDynamic Node ClassificationGraph Learning+3Supra-Laplacian Encoding for Transformer on Dynamic Graphs
Fully connected Graph Transformers (GT) have rapidly become prominent in the static graph community as an alternative to Message-Passing models, which suffer from a lack of expressivity, oversquashing, and under-reaching…
Dynamic Link PredictionLink PredictionDynamic Link and Flow Prediction in Bank Transfer Networks
The prediction of both the existence and weight of network links at future time points is essential as complex networks evolve over time. Traditional methods, such as vector autoregression and factor models, have been ap…
Dynamic Link PredictionLink PredictionPredictionContrastive Representation Learning for Dynamic Link Prediction in Temporal Networks
Evolving networks are complex data structures that emerge in a wide range of systems in science and engineering. Learning expressive representations for such networks that encode their structural connectivity and tempora…
Contrastive LearningDynamic Link PredictionGraph Neural NetworkGraph Reconstruction+2DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs
Dynamic graph learning aims to uncover evolutionary laws in real-world systems, enabling accurate social recommendation (link prediction) or early detection of cancer cells (classification). Inspired by the success of st…
Dynamic Link PredictionDynamic Node ClassificationGraph LearningLanguage Modeling+5DyGMamba: Efficiently Modeling Long-Term Temporal Dependency on Continuous-Time Dynamic Graphs with State Space Models
Learning useful representations for continuous-time dynamic graphs (CTDGs) is challenging, due to the concurrent need to span long node interaction histories and grasp nuanced temporal details. In particular, two problem…
Dynamic Link PredictionLink PredictionMambaRepresentation Learning+1Co-Neighbor Encoding Schema: A Light-cost Structure Encoding Method for Dynamic Link Prediction
Structure encoding has proven to be the key feature to distinguishing links in a graph. However, Structure encoding in the temporal graph keeps changing as the graph evolves, repeatedly computing such features can be tim…
Dynamic Link PredictionGraph LearningLink PredictionTemporal receptive field in dynamic graph learning: A comprehensive analysis
Dynamic link prediction is a critical task in the analysis of evolving networks, with applications ranging from recommender systems to economic exchanges. However, the concept of the temporal receptive field, which refer…
BenchmarkingDynamic Link PredictionGraph LearningLink Prediction+1From Link Prediction to Forecasting: Information Loss in Batch-based Temporal Graph Learning
Dynamic link prediction is an important problem considered by many recent works proposing various approaches for learning temporal edge patterns. To assess their efficacy, models are evaluated on publicly available bench…
Dynamic Link PredictionGraph LearningLink PredictionPrediction