Sparse-Dyn: Sparse Dynamic Graph Multi-representation Learning via Event-based Sparse Temporal Attention Network

TL;DR

Sparse-Dyn introduces a novel dynamic graph neural network that adaptively encodes temporal information into patches, avoiding information loss and enabling efficient, fine-grained temporal representation with lower computational cost.

Contribution

It proposes a new adaptive encoding method and a lightweight Sparse Temporal Transformer for efficient, fine-grained dynamic graph learning without snapshot-based information loss.

Findings

Faster inference speed compared to state-of-the-art methods.

Competitive performance on link prediction tasks.

Effective encoding of temporal information into patches.

Abstract

Dynamic graph neural networks have been widely used in modeling and representation learning of graph structure data. Current dynamic representation learning focuses on either discrete learning which results in temporal information loss or continuous learning that involves heavy computation. In this work, we proposed a novel dynamic graph neural network, Sparse-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 which causes 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 Sparse-Dyn has a faster inference speed while having competitive performance.