GitLab

* Support multi-dimension node feature in SPMV

* fix as requested

* fix recv nodes are all 0deg; fix hybriddict does not through keyerror properly

* fallback to apply_nodes when all nodes are 0deg; WIP on pull spmv 0deg

* new 0deg behavior

* new 0deg behavior

* update mx utest for pull-0deg

* fix mx

* fix mx

* get rid of unnecessary sort-n-unique

* change the signature of node/edge filter

* upd filter

* executor api

* draft executor interface

* WIP

* revert changes to avoid conflict with api change

* core scheduling logic

* WIP: build graph adj

* incidence matrix for in edges

* support incidence matrix for partial recv nodes

* improve

* build adjmat in scheduler

* graph store

* get degree bucketing schedule

* connect to c++ degree bucketing

* conceptual executor creation code

* executor comments

* fix

* more executor comments

* WIP: full send_and_recv schedule

* most schedulers

* simplify scheduler

* executors

* runtime

* builtin function base class

* adj indices and shape

* completely refactor scheduler

* rename and move bundled out to function.py

* use_edge_feature in msg func

* rewrite scheduler

* node edge executor

* connect with graph api

* handle zero degree

* misc

* fix test cases

* fix a good many bugs...

* remove old scheduler

* push and pull

* fix send recv

* c++ lint

* fix batched send recv

* hot fix for mxnet

* typo

* write back executor

* apply node edge

* clean up, doc string

* fix as requested

* refactor

* fix

* WIP

* WIP

* ir draft

* more on ir

* WIP: spmv schedule

* WIP

* recv schedule

* refactor

* WIP

* snr degree bucketing

* snr scheduler

* move prog to graph.py; rename

* unittest for send/recv

* remove some legacy codes

* WIP: update_all

* pass test_basics

* passed all current utests

* more utests; fix mx utest

* WIP: fixing zero deg initial value

* some tests

* fix 0deg problem

* fix mx

* fix mx

* some notes

* fix as requested

* move initializer to column scheme; change num rows behavior

* poke mx ci

* fix mx utest in append

* fix bug in add edges

* utest for updating partial rows

* fix bug in from_networkx and from_scipy_matrix

* revert per-col initializer change

* fix pickle utest

* add a test.

* move file.

* debug

* add degree normalization.

* it can converge now.

* remove hidden_data.

* use readonly graph index.

* use subgraph loader.

* refactor code and remove intermediate data.

* split inference.

* mix cpu and gpu training.

* fix mxnet sparse_matrix constructor.

* convert tensor context.

* set up the prediction model.

* load mxnet csr directly.

* add timing.

* move test_sse.py to sse_batch.py

* fix the tensor version of SSE.

* update README.

addresses issue #156

* pickling support

* resorting to suggested way of pickling

* custom attribute pickling check

* working around a weird pytorch pickling bug

* including partial frame case

* pickling everything now

* fix as requested

* add neighbor sampler.

* fix sampler.

* handle non-existing nodes differently in MapParentIdToSubgraphId.

* fix sampling tests.

* parallelize sampling.

* add more tests.

* fix a bug in subgraph loader.

* get number of sampled nodes directly.

* load mxnet CSR to DGLGraph directly.

* create graph from mxnet csr array directly.

* use subgraph loader in SSE.

* fix a bug in sampling.

* output framework tensor.

* fix.

* remove unnecessary code.

* fix tests.

* add SSE README.

* move to contrib.

* address comments.

* add tests.

* refactor graph caching

* fix mx test

* fix typo

[DOC][Model] Tree-LSTM example and tutorial

* bfs with edges

* dfs toy test case

* clean up

* style fix

* bugfix

* updating docs for bfs_edges and readouts

* fixing builtin src*edge shape mismatch

* bundled function refactor (?)

* fixing names

* readout prototype

* oops

* more fixes

* removing readout prototype

* sum_on() with SPMV, fixing batching with 0 edges

* readouts with segmented sum

* typo (??????)

* fixes NLTK dependency (#125)

* misc fixes including #126 (pushing again)

* sanity check for mxnet

* fixes NLTK dependency (#125) and #126

* reverting to sum_nodes/edges

* add examples in traversal.py

* message propagate methods

* use the new message propagation for tree-lstm

* update to the new name

* update propagate API doc

* update doc

* add propagate utest

* refactor API doc; add markup link in tutorials

* fix readme

* move gat to the new api.

* fix gcn.

* update sse.

* fix dgl core.

* update sse.

* fix small bugs in dgl core.

* fix mxnet tests.

* retrigger

* address comments and fix more bugs.

* fix

* fix tests.

* bfs, dfs and topological traversal

* dfs and test cases

* Conflicts:
	python/dgl/graph.py
	src/graph/graph.cc
	src/graph/graph_op.cc

* documentation

* requested changes

* Conflicts:
	Jenkinsfile
	examples/pytorch/gcn/gcn.py
	examples/pytorch/gcn/gcn_spmv.py
	python/dgl/graph.py
	python/dgl/graph_index.py
	src/graph/graph.cc
	src/graph/graph_op.cc

* Conflicts:
	Jenkinsfile
	python/dgl/graph_index.py

* fix lint errors

* fix lint errors

* fix lint errors

* fix test cases

* requested changes

* traversal interface

* [Bug] fixed a typo that caused syntax error (#120)

* WIP

* bfs nodes generator works

* topological traversal

* WIP: dfs_edges

* dfs edges

* dfs labeled edges

* utest for traversal

* fix lint

* fix utest

* code clean

* changes as requested

* backend interface

* small fix

* more comments to the data type dict

* WIP

* convert_to and narrow

* WIP

* pytorch and numpy backend; WIP on mxnet backend

* mxnet backend

* narrow

* Fix all usages

* fix for mx

* fix for mx

* fix mx

* fix mx

* fix mx

* fix mx

* fix mx

* fix mx

* fix mx

* revert jenkins

* add sparse_matrix api

* sparse matrix api

* some fixme

* Fix as requested

* pretty printer

* Conflicts:
	python/dgl/data/sbm.py

* refined line_graph implementation

* fix broken api calls

* small fix to trigger CI

* requested change

* Add SH tutorials

* setup sphinx-gallery; work on graph tutorial

* draft dglgraph tutorial

* update readme to include document url

* rm obsolete file

* Draft the message passing tutorial

* Capsule code (#102)

* add capsule example

* clean code

* better naming

* better naming

* [GCN]tutorial scaffold

* fix capsule example code

* remove previous capsule example code

* graph struc edit

* modified:   2_graph.py

* update doc of capsule

* update capsule docs

* update capsule docs

* add msg passing prime

* GCN-GAT tutorial Section 1 and 2

* comment for API improvement

* section 3

* Tutorial API change (#115)

* change the API as discusses; toy example

* enable the new set/get syntax

* fixed pytorch utest

* fixed gcn example

* fixed gat example

* fixed mx utests

* fix mx utest

* delete apply edges; add utest for update_edges

* small change on toy example

* fix utest

* fix out in degrees bug

* update pagerank example and add it to CI

* add delitem for dataview

* make edges() return form that is compatible with send/update_edges etc

* fix index bug when the given data is one-int-tensor

* fix doc

* cherry picking optimization from jtnn

* unbatch by slicing frames

* reduce pack

* oops

* support frame read/write with slices

* reverting to unbatch by splitting; slicing is unfriendly to backward

* replacing lru cache with static object factory

* replacing Scheme object with namedtuple

* remove comment

* forgot the find edges interface

* subclassing namedtuple

* Fix

1. Fix two typos in gcn.py and gcn_spmv.py
2. Update README

* Fix GCN module

1. Update the outdated graph convolution layer class
2. Fix a bug in the code where dropout never works. Modules like dropout/batch norm depend on whether we are in the training stage or inference stage.

* Fix a bug in dropout

1. dropout depends on nn.Module.training

* Update GCN module

* Fix README

* Fix dropout & remove self.msg_field

* Fix

* Align with TF implementation

* Make g an argument for forward

* Remove features from the argument of GraphConv layer

* Support for create nodes/edges after setting representations

* Remove redundant commit

* Delete test_init_repr.py

* Test case for dynamic addition

* Base 'add_rows' upon 'append'

* Move test function

* Fix

* test by assertion

* changed add_rows to adding blank rows only; adding convert_to to backend

* moving test to basics

* oops mxnet

1. Update `examples/pytorch/gcn` and `python/dgl/nn/pytorch` based on the latest APIs
2. Add full support for dropout in `examples/pytorch/gcn` and `python/dgl/nn/pytorch`
3. Rename `GCN` class in `python/dgl/nn/pytorch` to be `GraphConvolutionLayer` class
4. Make node field an argument that can be configured by users in GraphConvolutionLayer

Note that adjacency normalization has not been supported yet in the examples. 

* add immutable graph index.

* update ImmutableGraphIndex.

* add benchmarks of subgraph generation.

* fix node_subgraphs.

* use scipy synthetic data in immutable graph.

* create immutable_graph.

* use spmv in gcn.

* fix immutable graph index.

* update graph index.

* create ImmutableSubgraphIndex.

* return subgraph node and edge mapping.

* fix benchmark.

* add mapping to subgraph nid.

* test in_edges and out_edges.

* update benchmark.

* Fix immutable subgraph.

* complete in_edges and out_edges.

* fix benchmarks.

* add test on node_subgraphs

* add sse.

* construct subgraphs in parallel.

* add in/out degree(s).

* make immutable graph index backend-specific.

* implement has_edge(s) and edge_id(s).

* Revert "use spmv in gcn."

This reverts commit 9cfed5f5fb3dd2ed9b98745348b1c0e9731ed7f7.

* implement node_subgraphs in GraphIndex.

* fix sse.

* address comments.

* address comments.

* Update mxnet docker.

* setup sphinx-gallery; work on graph tutorial

* draft dglgraph tutorial

* update readme to include document url

* rm obsolete file

* Draft the message passing tutorial

* fixing networkx conversion with 0 edges

* fixes as required

* remove obsolete comment

* CI for lint check

* init submodule before run lint

* install ubuntu core for lint docker

* remove generator import in dgl