v1.1.0

What's new

• Sparse API improvement
• Datasets for evaluating graph transformers and graph learning under heterophily
• Modules and utilities, including Cugraph convolution modules and SubgraphX
• Graph transformer deprecation
• Performance improvement
• Extended BF16 data type to support 4th Generation Intel® Xeon® Scalable Processors (#5497)

Detailed breakdown

Sparse API improvement (@czkkkkkk )

SparseMatrix class

• Merge DiagMatrix class into SparseMatrix class, where the diagonal matrix is stored as a sparse matrix and inherits all the operators from sparse matrix. (#5367)
• Support converting DGLGraph to SparseMatrix.g.adj(self, etype=None, eweight_name=None) returns the sparse matrix representation of the DGL graph g on the edge type etype and edge weight eweight_name. (#5372)
• Enable zero-overhead conversion between Pytorch sparse tensors and SparseMatrix via dgl.sparse.to_torch_sparse_coo/csr/csc and dgl.sparse.from_torch_sparse. (#5373)

SparseMatrix operators

• Support element-wise multiplication on two sparse matrices with different sparsities, e.g., A * B. (#5368)
• Support element-wise division on two sparse matrices with the same sparsity, e.g., A / B. (#5369)
• Support broadcast operators on a sparse matrix and a 1-D tensor via dgl.sparse.broadcast_add/sub/mul/div. (#5370)
• Support column-wise softmax. (#5371)

SparseMatrix examples

• Example for Heterogeneous Graph Attention Networks (#5568, @mufeili )

Datasets

• PATTERNDataset (#5422, @gyzhou2000 )
• CLUSTERDataset (#5389, @ZHITENGLI )
• ChameleonDataset (#5477, @mufeili )
• SquirrelDataset (#5507, @mufeili )
• ActorDataset (#5511, @mufeili )
• CornellDataset (#5513, @mufeili )
• TexasDataset (#5513, @mufeili )
• WisconsinDataset (#5520, @mufeili )
• ZINCDataset (#5428, @ZhenyuLU-Heliodore )

Modules and utilities

• Dataset split for out-of-distribution (OOD) evaluation (#5418, @gvbazhenov )
• SVD-based positional encoding (#5121, @ZhenyuLU-Heliodore )
• Utils for measuring homophily of a graph (#5376, #5382, @mufeili )
• EdgeGATConv (#5282, @schmidt-ju )
• CuGraphGATConv (#5168, @tingyu66 )
• CuGraphSAGEConv (#5137, @tingyu66 )
• SubgraphX (#5315, @kunmukh )
• SubgraphX for heterogeneous graphs (#5530, @ndbaker1 , @kunmukh )

Deprecation (#5100, @rudongyu )

• laplacian_pe is deprecated and replaced by lap_pe
• LaplacianPE is deprecated and replaced by LapPE
• LaplacianPosEnc is deprecated and replaced by LapPosEncoder
• BiasedMultiheadAttention is deprecated and replaced by BiasedMHA

Performance improvement

Speedup the CPU to_block function in graph sampling. (#5305, @peizhou001 )

• Add a concurrent hash map to speed up the id mapping process by leveraging multi-thread capability (#5241, #5304).
• Accelerate the expensive to_block by using the new hash map, improve the performance by ~2.5x on average and more when the batch size is large.

Breaking changes

• Since the new .adj() function of DGLGraph produces a SparseMatrix, the original .adj(self, transpose=False, ctx=F.cpu(), scipy_fmt=None, etype=None) is renamed as .adj_external, returning the sparse format from external packages such as Scipy and Pytorch. (#5372)