Dynamic Link Prediction

12 papers with code • 2 benchmarks • 6 datasets

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Use these libraries to find Dynamic Link Prediction models and implementations

Most implemented papers

EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs

IBM/EvolveGCN 26 Feb 2019

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 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 23 Mar 2023

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

palash1992/DynamicGEM 7 Sep 2018

Capturing such evolution is key to predicting the properties of unseen networks.

DyRep: Learning Representations over Dynamic Graphs

Harryi0/dyrep_torch 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

uoguelph-mlrg/LDG 23 Sep 2019

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

xkcd1838/bench-dgnn 29 Sep 2021

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

cchao0116/CTSMA-ICML21 30 Mar 2022

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 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 20 Jul 2022

To evaluate against more difficult negative edges, we introduce two more challenging negative sampling strategies that improve robustness and better match real-world applications.