Dynamic Link Prediction
15 papers with code • 9 benchmarks • 7 datasets
Benchmarks
These leaderboards are used to track progress in Dynamic Link Prediction
Libraries
Use these libraries to find Dynamic Link Prediction models and implementationsDatasets
Reddit
Enron Emails
HeriGraph
Latest papers
New Perspectives on the Evaluation of Link Prediction Algorithms for Dynamic Graphs
We leverage these visualization tools to investigate the effect of negative sampling on the predictive performance, at the node and edge level.
Exploring Time Granularity on Temporal Graphs for Dynamic Link Prediction in Real-world Networks
silencex12138/time-granularity-on-temporal-graphs
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Dynamic Graph Neural Networks (DGNNs) have emerged as the predominant approach for processing dynamic graph-structured data.
Towards Better Dynamic Graph Learning: New Architecture and Unified Library
yule-buaa/dyglib
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NeurIPS 2023
We propose DyGFormer, a new Transformer-based architecture for dynamic graph learning.
EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph Learning
Dynamic graphs arise in various real-world applications, and it is often welcomed to model the dynamics directly in continuous time domain for its flexibility.
DyG2Vec: Efficient Representation Learning for Dynamic Graphs
Temporal graph neural networks have shown promising results in learning inductive representations by automatically extracting temporal patterns.
DyCSC: Modeling the Evolutionary Process of Dynamic Networks Based on Cluster Structure
Temporal networks are an important type of network whose topological structure changes over time.
Towards Better Evaluation for Dynamic Link Prediction
fpour/dgb
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To evaluate against more difficult negative edges, we introduce two more challenging negative sampling strategies that improve robustness and better match real-world applications.
Euler: Detecting Network Lateral Movement via Scalable Temporal Link Prediction
iHeartGraph/Euler
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NDSS 2022
In this paper, we propose a formalized approach to this problem with a framework we call EULER.
Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential Recommendation
User interests are usually dynamic in the real world, which poses both theoretical and practical challenges for learning accurate preferences from rich behavior data.
Benchmarking Graph Neural Networks on Dynamic Link Prediction
We compare link prediction heuristics, GNNs, discrete DGNNs, and continuous DGNNs on dynamic link prediction.
