Dynamic Link Prediction
15 papers with code • 9 benchmarks • 7 datasets
Benchmarks
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Libraries
Use these libraries to find Dynamic Link Prediction models and implementationsDatasets
Reddit
Enron Emails
HeriGraph
Latest papers with no code
One Graph Model for Cross-domain Dynamic Link Prediction
Extensive experiments on eight untrained graphs demonstrate that DyExpert achieves state-of-the-art performance in cross-domain link prediction.
HOT: Higher-Order Dynamic Graph Representation Learning with Efficient Transformers
A fundamental workload in this setting is dynamic link prediction: using a history of graph updates to predict whether a given pair of vertices will become connected.
Dynamic Link Prediction for New Nodes in Temporal Graph Networks
To overcome the few-shot challenge, we incorporate the encoder-predictor into the meta-learning paradigm, which can learn two types of implicit information during the formation of the temporal network through span adaptation and node adaptation.
Structure-reinforced Transformer for Dynamic Graph Representation Learning with Edge Temporal States
The burgeoning field of dynamic graph representation learning, fuelled by the increasing demand for graph data analysis in real-world applications, poses both enticing opportunities and formidable challenges.
DBGDGM: Dynamic Brain Graph Deep Generative Model
In this paper, we propose a dynamic brain graph deep generative model (DBGDGM) which simultaneously clusters brain regions into temporally evolving communities and learns dynamic unsupervised node embeddings.
Dyn-Backdoor: Backdoor Attack on Dynamic Link Prediction
Backdoor attacks induce the DLP methods to make wrong prediction by the malicious training data, i. e., generating a subgraph sequence as the trigger and embedding it to the training data.
Learning Representation over Dynamic Graph using Aggregation-Diffusion Mechanism
However, relying only on aggregation to propagate information in dynamic graphs can result in delays in information propagation and thus affect the performance of the method.
A Survey on Embedding Dynamic Graphs
Embedding static graphs in low-dimensional vector spaces plays a key role in network analytics and inference, supporting applications like node classification, link prediction, and graph visualization.
GRADE: Graph Dynamic Embedding
At each time step link generation is performed by first assigning node membership from a distribution over the communities, and then sampling a neighbor from a distribution over the nodes for the assigned community.
Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey
Second, we present a comprehensive survey of dynamic graph neural network models using the proposed terminology
