🚀 The feature, motivation and pitch

Different GNN applications require customized sampling to improve the accuracy and/or performance. We propose the following advanced sampling routines with detailed benchmarking reports.

• Multi-threading support: All sampling operations are single-threaded. We can expect additional speed-ups when providing multi-threading implementation:
  • Multi-threaded neighbor_sample implementation
  • Multi-threaded hetero_neighbor_sample implementation (Hetero neighbor sampler multithreading pyg-lib#215)
  • Introduce benchmarks in pyg-lib and/or PyG
• Bi-directional sampling: Allow the option to return a bidirectional graph as part of sampling (e.g., for allowing GNNs with more layers than sampled hops)
  • Add SubgraphType with bidirectional sampling support (1/2) #7199
  • Add SubgraphType with bidirectional sampling support (2/2) #7200
  • Introduce bidirectional sampling benchmarks
• Hierarchical Neighborhood sampling: Hierarchical Neighborhood Sampling is extending classical Neighborhood Sampling by collecting additional information about number of sampled nodes and edges per each hop. That information can be used in special features like Hierarchical Graph Adjacency Matrix implemented by trim_to_layer functionality which helps to boost the performance.
  • Adding option to return info about sampled graph pyg-lib#197
  • Adding trim_to_layer utility function #6661
  • SparseTensor support for trim_to_layer #7089
  • Adds an example for Hierarchical Sampling #7244
  • Add automatic trimming example via a separate HGAM layer and add an example
  • Write documentation/tutorial
• Weighted Sampling: Allow passing a weight vector to neighbor_sample to sample based on
  • Allow passing a weight vector to neighbor_sample to sample based on importance scores. Reference DGL implementation (see prob argument).
  • Integrate into PyG
• Subgraph sampling
  • Initial version in pyg-lib, see here
  • Benchmarking efforts
  • Integrate into pyg_lib.neighbor_sample in case directed=False