Sparse‐Dyn: Sparse dynamic graph multirepresentation learning via event‐based sparse temporal attention network

Abstract

Dynamic graph neural networks (DGNNs) 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 which involves heavy computation. In this study, we proposed a novel DGNN, sparse dynamic (Sparse-Dyn). It adaptively encodes temporal information into a sequence of patches with an equal amount of temporal-topological structure. Therefore, while avoiding using snapshots which 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 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-art. Link prediction experiments are conducted on both continuous and discrete graph data sets. By comparing several state-ofthe-art graph embedding baselines, the experimental

NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams

“…Each snapshot 𝐺 (𝑡 𝑖 ) is created by incorporating dynamic information Δ𝐺 (𝑡 𝑖 ) between time intervals 𝑡 (𝑖 −1) and 𝑡 (𝑖 −1) + Δ𝑡 on the previous snapshot 𝐺 (𝑡 𝑖 −1 ) . This snapshot-based method cannot represent the complete evolution process of a graph [17,37,45,79]. A snapshot only represents the structure at one particular time period, which can result in the loss of dynamic information.…”