Dynamic Link Prediction
12 papers with code • 2 benchmarks • 6 datasets
Benchmarks
These leaderboards are used to track progress in Dynamic Link Prediction
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Libraries
Use these libraries to find Dynamic Link Prediction models and implementationsDatasets
Enron Email Dataset
HeriGraph
Most implemented papers
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
IBM/EvolveGCN
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Existing approaches typically resort to node embeddings and use a recurrent neural network (RNN, broadly speaking) to regulate the embeddings and learn the temporal dynamics.
Variational Graph Recurrent Neural Networks
VGraphRNN/VGRNN
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NeurIPS 2019
Representation learning over graph structured data has been mostly studied in static graph settings while efforts for modeling dynamic graphs are still scant.
Towards Better Dynamic Graph Learning: New Architecture and Unified Library
yule-buaa/dyglib
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We propose DyGFormer, a new Transformer-based architecture for dynamic graph learning that solely learns from the sequences of nodes' historical first-hop interactions.
dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation Learning
Capturing such evolution is key to predicting the properties of unseen networks.
DyRep: Learning Representations over Dynamic Graphs
Harryi0/dyrep_torch
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ICLR 2019
We present DyRep - a novel modeling framework for dynamic graphs that posits representation learning as a latent mediation process bridging two observed processes namely -- dynamics of the network (realized as topological evolution) and dynamics on the network (realized as activities between nodes).
Learning Temporal Attention in Dynamic Graphs with Bilinear Interactions
We consider a common case in which edges can be short term interactions (e. g., messaging) or long term structural connections (e. g., friendship).
Benchmarking Graph Neural Networks on Dynamic Link Prediction
We compare link prediction heuristics, GNNs, discrete DGNNs, and continuous DGNNs on dynamic link prediction.
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.
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.
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.
