ReInc: Scaling Training of Dynamic Graph Neural Networks

Dynamic Graph Neural Networks (DGNNs) have gained widespread attention due to their applicability in diverse domains such as traffic network prediction, epidemiological forecasting, and social network analysis. In this paper, we present ReInc, a system designed to enable efficient and scalable training of DGNNs on large-scale graphs. ReInc introduces key innovations that capitalize on the unique combination of Graph Neural Networks (GNNs) and Recurrent Neural Networks (RNNs) inherent in DGNNs. By reusing intermediate results and incrementally computing aggregations across consecutive graph snapshots, ReInc significantly enhances computational efficiency. To support these optimizations, ReInc incorporates a novel two-level caching mechanism with a specialized caching policy aligned to the DGNN execution workflow. Additionally, ReInc addresses the challenges of managing structural and temporal dependencies in dynamic graphs through a new distributed training strategy. This approach eliminates communication overheads associated with accessing remote features and redistributing intermediate results. Experimental results demonstrate that ReInc achieves up to an order of magnitude speedup compared to state-of-the-art frameworks, tested across various dynamic GNN architectures and real-world graph datasets.

HetTree: Heterogeneous Tree Graph Neural Network

The recent past has seen an increasing interest in Heterogeneous Graph Neural Networks (HGNNs) since many real-world graphs are heterogeneous in nature, from citation graphs to email graphs. However, existing methods ignore a tree hierarchy among metapaths, which is naturally constituted by different node types and relation types. In this paper, we present HetTree, a novel heterogeneous tree graph neural network that models both the graph structure and heterogeneous aspects in a scalable and effective manner. Specifically, HetTree builds a semantic tree data structure to capture the hierarchy among metapaths. Existing tree encoding techniques aggregate children nodes by weighting the contribution of children nodes based on similarity to the parent node. However, we find that this tree encoding fails to capture the entire parent-children hierarchy by only considering the parent node. Hence, HetTree uses a novel subtree attention mechanism to emphasize metapaths that are more helpful in encoding parent-children relationships. Moreover, instead of separating feature learning from label learning or treating features and labels equally by projecting them to the same latent space, HetTree proposes to match them carefully based on corresponding metapaths, which provides more accurate and richer information between node features and labels. Our evaluation of HetTree on a variety of real-world datasets demonstrates that it outperforms all existing baselines on open benchmarks and efficiently scales to large real-world graphs with millions of nodes and edges.