Releases
1.6.2
Features
Minor improvements
The SIGN
example now operates on mini-batches of nodes
Improved data loading runtime of InMemoryDataset
s
NeighborSampler
does now work with SparseTensor
as input
ToUndirected
transform in order to convert directed graphs to undirected ones
GNNExplainer
does now allow for customizable edge and node feature loss reduction
aggr
can now passed to any GNN based on the MessagePassing
interface (thanks to @m30m )
Runtime improvements in SEAL
(thanks to @muhanzhang )
Runtime improvements in torch_geometric.utils.softmax
(thanks to @Book1996 )
GAE.recon_loss
now supports custom negative edge indices (thanks to @reshinthadithyan )
Faster spmm
computation and random_walk
sampling on CPU (torch-sparse
and torch-cluster
updates required)
DataParallel
does now support the follow_batch
argument
Parallel approximate PPR computation in the GDC
transform (thanks to @klicperajo)
Improved documentation by providing an autosummary of all subpackages (thanks to @m30m )
Improved documentation on how edge weights are handled in various GNNs (thanks to @m30m )
Bugfixes
Fixed a bug in GATConv
when computing attention coefficients in bipartite graphs
Fixed a bug in GraphSAINTSampler
that led to wrong edge feature sampling
Fixed the DimeNet
pretraining link
Fixed a bug in processing ego-twitter
and ego-gplus
of the SNAPDataset
collection
Fixed a number of broken dataset URLs (ICEWS18
, QM9
, QM7b
, MoleculeNet
, Entities
, PPI
, Reddit
, MNISTSuperpixels
, ShapeNet
)
Fixed a bug in which MessagePassing.jittable()
tried to write to a file without permission (thanks to @twoertwein )
GCNConv
does not require edge_weight
in case normalize=False
Batch.num_graphs
will now report the correct amount of graphs in case of zero-sized graphs
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