DeepAI AI Chat
Log In Sign Up

Efficient-Dyn: Dynamic Graph Representation Learning via Event-based Temporal Sparse Attention Network

by   Yan Pang, et al.
University of Colorado Denver

Static graph neural networks have been widely used in modeling and representation learning of graph structure data. However, many real-world problems, such as social networks, financial transactions, recommendation systems, etc., are dynamic, that is, nodes and edges are added or deleted over time. Therefore, in recent years, dynamic graph neural networks have received more and more attention from researchers. In this work, we propose a novel dynamic graph neural network, Efficient-Dyn. It adaptively encodes temporal information into a sequence of patches with an equal amount of temporal-topological structure. Therefore, while avoiding the use of snapshots to cause information loss, it also achieves a finer time granularity, which is close to what continuous networks could provide. In addition, we also designed a lightweight module, Sparse Temporal Transformer, to compute node representations through both structural neighborhoods and temporal dynamics. Since the fully-connected attention conjunction is simplified, the computation cost is far lower than the current state-of-the-arts. Link prediction experiments are conducted on both continuous and discrete graph datasets. Through comparing with several state-of-the-art graph embedding baselines, the experimental results demonstrate that Efficient-Dyn has a faster inference speed while having competitive performance.


Decoupled Graph Neural Networks for Large Dynamic Graphs

Real-world graphs, such as social networks, financial transactions, and ...

TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning

Dynamic graph modeling has recently attracted much attention due to its ...

Dynamic Network Embedding Survey

Since many real world networks are evolving over time, such as social ne...

APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embedding

Limited by the time complexity of querying k-hop neighbors in a graph da...

Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation Learning

Graphs are a commonly used construct for representing relationships betw...

Fast Temporal Wavelet Graph Neural Networks

Spatio-temporal signals forecasting plays an important role in numerous ...

Neighborhood-aware Scalable Temporal Network Representation Learning

Temporal networks have been widely used to model real-world complex syst...