Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods

Overview

Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods

Introduction

Graph Neural Networks (GNNs) have demonstrated superior performance in node classification or regression tasks, and have emerged as the state of the art in several applications. However, (inductive) GNNs require the edge connectivity structure of nodes to be known beforehand to work well. This is often not the case in several practical applications where the node degrees have power-law distributions, and nodes with a few connections might have noisy edges. An extreme case is the strict cold start (SCS) problem, where there is no neighborhood information available, forcing prediction models to rely completely on node features only. To study the viability of using inductive GNNs to solve the SCS problem, we introduce feature-contribution ratio (FCR), a metric to quantify the contribution of a node's features and that of its neighborhood in predicting node labels, and use this new metric as a model selection reward. We then propose Cold Brew, a new method that generalizes GNNs better in the SCS setting compared to pointwise and graph-based models, via a distillation approach. We show experimentally how FCR allows us to disentangle the contributions of various components of graph datasets, and demonstrate the superior performance of Cold Brew on several public benchmarks

Motivation

Long tail distribution is ubiquitously existed in large scale graph mining tasks. In some applications, some cold start nodes have too few or no neighborhood in the graph, which make graph based methods sub-optimal due to insufficient high quality edges to perform message passing.

gnns

gnns

Method

We improve teacher GNN with Structural Embedding, and propose student MLP model with latent neighborhood discovery step. We also propose a metric called FCR to judge the difficulty in cold start generalization.

gnns

coldbrew

Installation Guide

The following commands are used for installing key dependencies; other can be directly installed via pip or conda. A full redundant dependency list is in requirements.txt

pip install dgl
pip3 install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html
pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+cu111.html
pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-1.9.0+cu111.html
pip install torch-geometric

Training Guide

In options/base_options.py, a full list of useable args is present, with default arguments and candidates initialized.

Comparing between traditional GCN (optimized with Initial/Jumping/Dense/PairNorm/NodeNorm/GroupNorm/Dropouts) and Cold Brew's GNN (optimized with Structural Embedding)

Train optimized traditional GNN:

python main.py --dataset='Cora' --train_which='TeacherGNN' --whetherHasSE='000' --want_headtail=1 --num_layers=2 --use_special_split=1 Result: 84.15

python main.py --dataset='Citeseer' --train_which='TeacherGNN' --whetherHasSE='000' --want_headtail=1 --num_layers=2 --use_special_split=1 Result: 71.00

python main.py --dataset='Pubmed' --train_which='TeacherGNN' --whetherHasSE='000' --want_headtail=1 --num_layers=2 --use_special_split=1 Result: 78.2

Training Cold Brew's Teacher GNN:

python main.py --dataset='Cora' --train_which='TeacherGNN' --whetherHasSE='100' --se_reg=32 --want_headtail=1 --num_layers=2 --use_special_split=1 Result: 85.10

python main.py --dataset='Citeseer' --train_which='TeacherGNN' --whetherHasSE='100' --se_reg=0.5 --want_headtail=1 --num_layers=2 --use_special_split=1 Result: 71.40

python main.py --dataset='Pubmed' --train_which='TeacherGNN' --whetherHasSE='111' --se_reg=0.5 --want_headtail=1 --num_layers=2 --use_special_split=1 Result: 78.2

Comparing between MLP models:

Training naive MLP:

python main.py --dataset='Cora' --train_which='StudentBaseMLP' Result on isolation split: 63.92

Training GraphMLP:

python main.py --dataset='Cora' --train_which='GraphMLP' Result on isolation split: 68.63

Training Cold Brew's MLP:

python main.py --dataset='Cora' --train_which="SEMLP" --SEMLP_topK_2_replace=3 --SEMLP_part1_arch="2layer" --dropout_MLP=0.5 --studentMLP__opt_lr='torch.optim.Adam&0.005' Result on isolation split: 69.57

Hyperparameter meanings

--whetherHasSE: whether cold brew's TeacherGNN has structural embedding. The first ‘1’ means structural embedding exist in first layer; second ‘1’ means structural embedding exist in every middle layers; third ‘1’ means last layer.

--se_reg: regularization coefficient for cold brew teacher model's structural embedding.

--SEMLP_topK_2_replace: the number of top K best virtual neighbor nodes.

--manual_assign_GPU: set the GPU ID to train on. default=-9999, which means to dynamically choose GPU with most remaining memory.

Adaptation Guide

How to leverage this repo to train on other datasets:

In trainer.py, put any new graph dataset (node classification) under load_data() and return it.

what to load: return a dataset, which is a namespace, called 'data', data.x: 2D tensor, on cpu; shape = [N_nodes, dim_feature]. data.y: 1D tensor, on cpu; shape = [N_nodes]; values are integers, indicating the class of nodes. data.edge_index: tensor: [2, N_edge], cpu; edges contain self loop. data.train_mask: bool tensor, shape = [N_nodes], indicating the training node set. Template class for the 'data':

class MyDataset(torch_geometric.data.data.Data):
    def __init__(self):
        super().__init__()

Citation

comming soon.
Comments
  • Bump tensorflow from 1.11.0 to 2.5.1

    Bump tensorflow from 1.11.0 to 2.5.1

    Bumps tensorflow from 1.11.0 to 2.5.1.

    Release notes

    Sourced from tensorflow's releases.

    TensorFlow 2.5.1

    Release 2.5.1

    This release introduces several vulnerability fixes:

    • Fixes a heap out of bounds access in sparse reduction operations (CVE-2021-37635)
    • Fixes a floating point exception in SparseDenseCwiseDiv (CVE-2021-37636)
    • Fixes a null pointer dereference in CompressElement (CVE-2021-37637)
    • Fixes a null pointer dereference in RaggedTensorToTensor (CVE-2021-37638)
    • Fixes a null pointer dereference and a heap OOB read arising from operations restoring tensors (CVE-2021-37639)
    • Fixes an integer division by 0 in sparse reshaping (CVE-2021-37640)
    • Fixes a division by 0 in ResourceScatterDiv (CVE-2021-37642)
    • Fixes a heap OOB in RaggedGather (CVE-2021-37641)
    • Fixes a std::abort raised from TensorListReserve (CVE-2021-37644)
    • Fixes a null pointer dereference in MatrixDiagPartOp (CVE-2021-37643)
    • Fixes an integer overflow due to conversion to unsigned (CVE-2021-37645)
    • Fixes a bad allocation error in StringNGrams caused by integer conversion (CVE-2021-37646)
    • Fixes a null pointer dereference in SparseTensorSliceDataset (CVE-2021-37647)
    • Fixes an incorrect validation of SaveV2 inputs (CVE-2021-37648)
    • Fixes a null pointer dereference in UncompressElement (CVE-2021-37649)
    • Fixes a segfault and a heap buffer overflow in {Experimental,}DatasetToTFRecord (CVE-2021-37650)
    • Fixes a heap buffer overflow in FractionalAvgPoolGrad (CVE-2021-37651)
    • Fixes a use after free in boosted trees creation (CVE-2021-37652)
    • Fixes a division by 0 in ResourceGather (CVE-2021-37653)
    • Fixes a heap OOB and a CHECK fail in ResourceGather (CVE-2021-37654)
    • Fixes a heap OOB in ResourceScatterUpdate (CVE-2021-37655)
    • Fixes an undefined behavior arising from reference binding to nullptr in RaggedTensorToSparse (CVE-2021-37656)
    • Fixes an undefined behavior arising from reference binding to nullptr in MatrixDiagV* ops (CVE-2021-37657)
    • Fixes an undefined behavior arising from reference binding to nullptr in MatrixSetDiagV* ops (CVE-2021-37658)
    • Fixes an undefined behavior arising from reference binding to nullptr and heap OOB in binary cwise ops (CVE-2021-37659)
    • Fixes a division by 0 in inplace operations (CVE-2021-37660)
    • Fixes a crash caused by integer conversion to unsigned (CVE-2021-37661)
    • Fixes an undefined behavior arising from reference binding to nullptr in boosted trees (CVE-2021-37662)
    • Fixes a heap OOB in boosted trees (CVE-2021-37664)
    • Fixes vulnerabilities arising from incomplete validation in QuantizeV2 (CVE-2021-37663)
    • Fixes vulnerabilities arising from incomplete validation in MKL requantization (CVE-2021-37665)
    • Fixes an undefined behavior arising from reference binding to nullptr in RaggedTensorToVariant (CVE-2021-37666)
    • Fixes an undefined behavior arising from reference binding to nullptr in unicode encoding (CVE-2021-37667)
    • Fixes an FPE in tf.raw_ops.UnravelIndex (CVE-2021-37668)
    • Fixes a crash in NMS ops caused by integer conversion to unsigned (CVE-2021-37669)
    • Fixes a heap OOB in UpperBound and LowerBound (CVE-2021-37670)
    • Fixes an undefined behavior arising from reference binding to nullptr in map operations (CVE-2021-37671)
    • Fixes a heap OOB in SdcaOptimizerV2 (CVE-2021-37672)
    • Fixes a CHECK-fail in MapStage (CVE-2021-37673)
    • Fixes a vulnerability arising from incomplete validation in MaxPoolGrad (CVE-2021-37674)
    • Fixes an undefined behavior arising from reference binding to nullptr in shape inference (CVE-2021-37676)
    • Fixes a division by 0 in most convolution operators (CVE-2021-37675)
    • Fixes vulnerabilities arising from missing validation in shape inference for Dequantize (CVE-2021-37677)
    • Fixes an arbitrary code execution due to YAML deserialization (CVE-2021-37678)
    • Fixes a heap OOB in nested tf.map_fn with RaggedTensors (CVE-2021-37679)

    ... (truncated)

    Changelog

    Sourced from tensorflow's changelog.

    Release 2.5.1

    This release introduces several vulnerability fixes:

    • Fixes a heap out of bounds access in sparse reduction operations (CVE-2021-37635)
    • Fixes a floating point exception in SparseDenseCwiseDiv (CVE-2021-37636)
    • Fixes a null pointer dereference in CompressElement (CVE-2021-37637)
    • Fixes a null pointer dereference in RaggedTensorToTensor (CVE-2021-37638)
    • Fixes a null pointer dereference and a heap OOB read arising from operations restoring tensors (CVE-2021-37639)
    • Fixes an integer division by 0 in sparse reshaping (CVE-2021-37640)
    • Fixes a division by 0 in ResourceScatterDiv (CVE-2021-37642)
    • Fixes a heap OOB in RaggedGather (CVE-2021-37641)
    • Fixes a std::abort raised from TensorListReserve (CVE-2021-37644)
    • Fixes a null pointer dereference in MatrixDiagPartOp (CVE-2021-37643)
    • Fixes an integer overflow due to conversion to unsigned (CVE-2021-37645)
    • Fixes a bad allocation error in StringNGrams caused by integer conversion (CVE-2021-37646)
    • Fixes a null pointer dereference in SparseTensorSliceDataset (CVE-2021-37647)
    • Fixes an incorrect validation of SaveV2 inputs (CVE-2021-37648)
    • Fixes a null pointer dereference in UncompressElement (CVE-2021-37649)
    • Fixes a segfault and a heap buffer overflow in {Experimental,}DatasetToTFRecord (CVE-2021-37650)
    • Fixes a heap buffer overflow in FractionalAvgPoolGrad (CVE-2021-37651)
    • Fixes a use after free in boosted trees creation (CVE-2021-37652)
    • Fixes a division by 0 in ResourceGather (CVE-2021-37653)
    • Fixes a heap OOB and a CHECK fail in ResourceGather (CVE-2021-37654)
    • Fixes a heap OOB in ResourceScatterUpdate (CVE-2021-37655)
    • Fixes an undefined behavior arising from reference binding to nullptr in RaggedTensorToSparse

    ... (truncated)

    Commits
    • 8222c1c Merge pull request #51381 from tensorflow/mm-fix-r2.5-build
    • d584260 Disable broken/flaky test
    • f6c6ce3 Merge pull request #51367 from tensorflow-jenkins/version-numbers-2.5.1-17468
    • 3ca7812 Update version numbers to 2.5.1
    • 4fdf683 Merge pull request #51361 from tensorflow/mm-update-relnotes-on-r2.5
    • 05fc01a Put CVE numbers for fixes in parentheses
    • bee1dc4 Update release notes for the new patch release
    • 47beb4c Merge pull request #50597 from kruglov-dmitry/v2.5.0-sync-abseil-cmake-bazel
    • 6f39597 Merge pull request #49383 from ashahab/abin-load-segfault-r2.5
    • 0539b34 Merge pull request #48979 from liufengdb/r2.5-cherrypick
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    dependencies 
    opened by dependabot[bot] 1
  • Bump pillow from 8.2.0 to 8.3.2

    Bump pillow from 8.2.0 to 8.3.2

    Bumps pillow from 8.2.0 to 8.3.2.

    Release notes

    Sourced from pillow's releases.

    8.3.2

    https://pillow.readthedocs.io/en/stable/releasenotes/8.3.2.html

    Security

    • CVE-2021-23437 Raise ValueError if color specifier is too long [hugovk, radarhere]

    • Fix 6-byte OOB read in FliDecode [wiredfool]

    Python 3.10 wheels

    • Add support for Python 3.10 #5569, #5570 [hugovk, radarhere]

    Fixed regressions

    • Ensure TIFF RowsPerStrip is multiple of 8 for JPEG compression #5588 [kmilos, radarhere]

    • Updates for ImagePalette channel order #5599 [radarhere]

    • Hide FriBiDi shim symbols to avoid conflict with real FriBiDi library #5651 [nulano]

    8.3.1

    https://pillow.readthedocs.io/en/stable/releasenotes/8.3.1.html

    Changes

    8.3.0

    https://pillow.readthedocs.io/en/stable/releasenotes/8.3.0.html

    Changes

    ... (truncated)

    Changelog

    Sourced from pillow's changelog.

    8.3.2 (2021-09-02)

    • CVE-2021-23437 Raise ValueError if color specifier is too long [hugovk, radarhere]

    • Fix 6-byte OOB read in FliDecode [wiredfool]

    • Add support for Python 3.10 #5569, #5570 [hugovk, radarhere]

    • Ensure TIFF RowsPerStrip is multiple of 8 for JPEG compression #5588 [kmilos, radarhere]

    • Updates for ImagePalette channel order #5599 [radarhere]

    • Hide FriBiDi shim symbols to avoid conflict with real FriBiDi library #5651 [nulano]

    8.3.1 (2021-07-06)

    • Catch OSError when checking if fp is sys.stdout #5585 [radarhere]

    • Handle removing orientation from alternate types of EXIF data #5584 [radarhere]

    • Make Image.array take optional dtype argument #5572 [t-vi, radarhere]

    8.3.0 (2021-07-01)

    • Use snprintf instead of sprintf. CVE-2021-34552 #5567 [radarhere]

    • Limit TIFF strip size when saving with LibTIFF #5514 [kmilos]

    • Allow ICNS save on all operating systems #4526 [baletu, radarhere, newpanjing, hugovk]

    • De-zigzag JPEG's DQT when loading; deprecate convert_dict_qtables #4989 [gofr, radarhere]

    • Replaced xml.etree.ElementTree #5565 [radarhere]

    ... (truncated)

    Commits
    • 8013f13 8.3.2 version bump
    • 23c7ca8 Update CHANGES.rst
    • 8450366 Update release notes
    • a0afe89 Update test case
    • 9e08eb8 Raise ValueError if color specifier is too long
    • bd5cf7d FLI tests for Oss-fuzz crash.
    • 94a0cf1 Fix 6-byte OOB read in FliDecode
    • cece64f Add 8.3.2 (2021-09-02) [CI skip]
    • e422386 Add release notes for Pillow 8.3.2
    • 08dcbb8 Pillow 8.3.2 supports Python 3.10 [ci skip]
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    dependencies 
    opened by dependabot[bot] 1
  • Bump joblib from 1.0.1 to 1.2.0

    Bump joblib from 1.0.1 to 1.2.0

    Bumps joblib from 1.0.1 to 1.2.0.

    Changelog

    Sourced from joblib's changelog.

    Release 1.2.0

    • Fix a security issue where eval(pre_dispatch) could potentially run arbitrary code. Now only basic numerics are supported. joblib/joblib#1327

    • Make sure that joblib works even when multiprocessing is not available, for instance with Pyodide joblib/joblib#1256

    • Avoid unnecessary warnings when workers and main process delete the temporary memmap folder contents concurrently. joblib/joblib#1263

    • Fix memory alignment bug for pickles containing numpy arrays. This is especially important when loading the pickle with mmap_mode != None as the resulting numpy.memmap object would not be able to correct the misalignment without performing a memory copy. This bug would cause invalid computation and segmentation faults with native code that would directly access the underlying data buffer of a numpy array, for instance C/C++/Cython code compiled with older GCC versions or some old OpenBLAS written in platform specific assembly. joblib/joblib#1254

    • Vendor cloudpickle 2.2.0 which adds support for PyPy 3.8+.

    • Vendor loky 3.3.0 which fixes several bugs including:

      • robustly forcibly terminating worker processes in case of a crash (joblib/joblib#1269);

      • avoiding leaking worker processes in case of nested loky parallel calls;

      • reliability spawn the correct number of reusable workers.

    Release 1.1.0

    • Fix byte order inconsistency issue during deserialization using joblib.load in cross-endian environment: the numpy arrays are now always loaded to use the system byte order, independently of the byte order of the system that serialized the pickle. joblib/joblib#1181

    • Fix joblib.Memory bug with the ignore parameter when the cached function is a decorated function.

    ... (truncated)

    Commits
    • 5991350 Release 1.2.0
    • 3fa2188 MAINT cleanup numpy warnings related to np.matrix in tests (#1340)
    • cea26ff CI test the future loky-3.3.0 branch (#1338)
    • 8aca6f4 MAINT: remove pytest.warns(None) warnings in pytest 7 (#1264)
    • 067ed4f XFAIL test_child_raises_parent_exits_cleanly with multiprocessing (#1339)
    • ac4ebd5 MAINT add back pytest warnings plugin (#1337)
    • a23427d Test child raises parent exits cleanly more reliable on macos (#1335)
    • ac09691 [MAINT] various test updates (#1334)
    • 4a314b1 Vendor loky 3.2.0 (#1333)
    • bdf47e9 Make test_parallel_with_interactively_defined_functions_default_backend timeo...
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    dependencies 
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  • Bump mako from 1.1.4 to 1.2.2

    Bump mako from 1.1.4 to 1.2.2

    Bumps mako from 1.1.4 to 1.2.2.

    Release notes

    Sourced from mako's releases.

    1.2.2

    Released: Mon Aug 29 2022

    bug

    • [bug] [lexer] Fixed issue in lexer where the regexp used to match tags would not correctly interpret quoted sections individually. While this parsing issue still produced the same expected tag structure later on, the mis-handling of quoted sections was also subject to a regexp crash if a tag had a large number of quotes within its quoted sections.

      References: #366

    1.2.1

    Released: Thu Jun 30 2022

    bug

    • [bug] [tests] Various fixes to the test suite in the area of exception message rendering to accommodate for variability in Python versions as well as Pygments.

      References: #360

    misc

    • [performance] Optimized some codepaths within the lexer/Python code generation process, improving performance for generation of templates prior to their being cached. Pull request courtesy Takuto Ikuta.

      References: #361

    1.2.0

    Released: Thu Mar 10 2022

    changed

    • [changed] [py3k] Corrected "universal wheel" directive in setup.cfg so that building a wheel does not target Python 2.

      References: #351

    • [changed] [py3k] The bytestring_passthrough template argument is removed, as this flag only applied to Python 2.

    ... (truncated)

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    dependencies 
    opened by dependabot[bot] 0
  • Bump certifi from 2021.5.30 to 2022.12.7

    Bump certifi from 2021.5.30 to 2022.12.7

    Bumps certifi from 2021.5.30 to 2022.12.7.

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    dependencies 
    opened by dependabot[bot] 0
  • Bump protobuf from 3.17.3 to 3.18.3

    Bump protobuf from 3.17.3 to 3.18.3

    Bumps protobuf from 3.17.3 to 3.18.3.

    Release notes

    Sourced from protobuf's releases.

    Protocol Buffers v3.18.3

    C++

    Protocol Buffers v3.18.2

    Java

    • Improve performance characteristics of UnknownFieldSet parsing (#9371)

    Protocol Buffers v3.18.1

    Python

    • Update setup.py to reflect that we now require at least Python 3.5 (#8989)
    • Performance fix for DynamicMessage: force GetRaw() to be inlined (#9023)

    Ruby

    • Update ruby_generator.cc to allow proto2 imports in proto3 (#9003)

    Protocol Buffers v3.18.0

    C++

    • Fix warnings raised by clang 11 (#8664)
    • Make StringPiece constructible from std::string_view (#8707)
    • Add missing capability attributes for LLVM 12 (#8714)
    • Stop using std::iterator (deprecated in C++17). (#8741)
    • Move field_access_listener from libprotobuf-lite to libprotobuf (#8775)
    • Fix #7047 Safely handle setlocale (#8735)
    • Remove deprecated version of SetTotalBytesLimit() (#8794)
    • Support arena allocation of google::protobuf::AnyMetadata (#8758)
    • Fix undefined symbol error around SharedCtor() (#8827)
    • Fix default value of enum(int) in json_util with proto2 (#8835)
    • Better Smaller ByteSizeLong
    • Introduce event filters for inject_field_listener_events
    • Reduce memory usage of DescriptorPool
    • For lazy fields copy serialized form when allowed.
    • Re-introduce the InlinedStringField class
    • v2 access listener
    • Reduce padding in the proto's ExtensionRegistry map.
    • GetExtension performance optimizations
    • Make tracker a static variable rather than call static functions
    • Support extensions in field access listener
    • Annotate MergeFrom for field access listener
    • Fix incomplete types for field access listener
    • Add map_entry/new_map_entry to SpecificField in MessageDifferencer. They record the map items which are different in MessageDifferencer's reporter.
    • Reduce binary size due to fieldless proto messages
    • TextFormat: ParseInfoTree supports getting field end location in addition to start.
    • Fix repeated enum extension size in field listener
    • Enable Any Text Expansion for Descriptors::DebugString()
    • Switch from int{8,16,32,64} to int{8,16,32,64}_t

    ... (truncated)

    Commits

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    dependencies 
    opened by dependabot[bot] 0
  • Bump oauthlib from 3.1.1 to 3.2.1

    Bump oauthlib from 3.1.1 to 3.2.1

    Bumps oauthlib from 3.1.1 to 3.2.1.

    Release notes

    Sourced from oauthlib's releases.

    3.2.1

    In short

    OAuth2.0 Provider:

    • #803 : Metadata endpoint support of non-HTTPS
    • CVE-2022-36087

    OAuth1.0:

    • #818 : Allow IPv6 being parsed by signature

    General:

    • Improved and fixed documentation warnings.
    • Cosmetic changes based on isort

    What's Changed

    New Contributors

    Full Changelog: https://github.com/oauthlib/oauthlib/compare/v3.2.0...v3.2.1

    3.2.0

    Changelog

    OAuth2.0 Client:

    • #795: Add Device Authorization Flow for Web Application
    • #786: Add PKCE support for Client
    • #783: Fallback to none in case of wrong expires_at format.

    OAuth2.0 Provider:

    • #790: Add support for CORS to metadata endpoint.
    • #791: Add support for CORS to token endpoint.
    • #787: Remove comma after Bearer in WWW-Authenticate

    OAuth2.0 Provider - OIDC:

    • #755: Call save_token in Hybrid code flow
    • #751: OIDC add support of refreshing ID Tokens with refresh_id_token
    • #751: The RefreshTokenGrant modifiers now take the same arguments as the AuthorizationCodeGrant modifiers (token, token_handler, request).

    ... (truncated)

    Changelog

    Sourced from oauthlib's changelog.

    3.2.1 (2022-09-09)

    OAuth2.0 Provider:

    • #803: Metadata endpoint support of non-HTTPS
    • CVE-2022-36087

    OAuth1.0:

    • #818: Allow IPv6 being parsed by signature

    General:

    • Improved and fixed documentation warnings.
    • Cosmetic changes based on isort

    3.2.0 (2022-01-29)

    OAuth2.0 Client:

    • #795: Add Device Authorization Flow for Web Application
    • #786: Add PKCE support for Client
    • #783: Fallback to none in case of wrong expires_at format.

    OAuth2.0 Provider:

    • #790: Add support for CORS to metadata endpoint.
    • #791: Add support for CORS to token endpoint.
    • #787: Remove comma after Bearer in WWW-Authenticate

    OAuth2.0 Provider - OIDC:

    • #755: Call save_token in Hybrid code flow
    • #751: OIDC add support of refreshing ID Tokens with refresh_id_token
    • #751: The RefreshTokenGrant modifiers now take the same arguments as the AuthorizationCodeGrant modifiers (token, token_handler, request).

    General:

    • Added Python 3.9, 3.10, 3.11
    • Improve Travis & Coverage
    Commits
    • 88bb156 Updated date and authors
    • 1a45d97 Prepare 3.2.1 release
    • 0adbbe1 docs: fix typos
    • 6569ec3 docs: Fix a few typos
    • bdc486e Fixed isort imports
    • 7db45bd Fix typo in server.rst
    • b14ad85 chore: s/bode_code_verifier/body_code_verifier/g
    • b123283 Allow non-HTTPS issuer when OAUTHLIB_INSECURE_TRANSPORT. (#803)
    • 2f887b5 Docs: fix Sphinx warnings for better ReadTheDocs generation (#807)
    • d4bafd9 Merge pull request #797 from cclauss/patch-2
    • Additional commits viewable in compare view

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    dependencies 
    opened by dependabot[bot] 0
  • Confuse about the dataset split

    Confuse about the dataset split

    Dear authors,

    Could you please provide the detailed training/val/test split you adopt in your paper? For example, for Cora, in your code, it seems you use the first 600 nodes as the training set while using the same validation and test set of the public split. But this may conflict with the isolation split you adopt (10% of lowest degree nodes) as there may be overlapped nodes between the isolation nodes and the training set.

    Thus, how do you conduct the evaluation on isolation split? Did you ignore all the isolation nodes in the training set during the evaluation?

    Looking forward to your reply! Many Thanks!

    opened by zyzisastudyreallyhardguy 0
  • Bump numpy from 1.19.5 to 1.22.0

    Bump numpy from 1.19.5 to 1.22.0

    Bumps numpy from 1.19.5 to 1.22.0.

    Release notes

    Sourced from numpy's releases.

    v1.22.0

    NumPy 1.22.0 Release Notes

    NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

    • Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.
    • A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.
    • NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.
    • New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.
    • A new configurable allocator for use by downstream projects.

    These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

    The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

    Expired deprecations

    Deprecated numeric style dtype strings have been removed

    Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

    (gh-19539)

    Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

    numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

    (gh-19615)

    ... (truncated)

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