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Lecture
11:
Graph
NeuralNetworksLecture11:GraphNeuralNetwor1Artificial
Intelligence2Natural
Language
Processing
?
Question
Answering
?
Information
Extraction
?
Machine
Translation
?
......
November
24,
2019ArtificialIntelligenceNatural2Artificial
Intelligence3Question
Answering
November
24,
2019ArtificialIntelligenceQuestio3Artificial
Intelligence4Information
Extraction
November
24,
2019ArtificialIntelligenceInforma4Artificial
Intelligence5Machine
Translation
November
24,
2019ArtificialIntelligenceMachine5Artificial
Intelligence6Graphs
are
everywhere
in
NLP
November
24,
2019ArtificialIntelligenceGraphs6Artificial
Intelligence7Deep
Learning
in
NLP
November
24,
2019ArtificialIntelligenceDeepLe7Artificial
Intelligence8Overview
November
24,
2019ArtificialIntelligenceOvervie8Artificial
Intelligence9Overview
November
24,
2019ArtificialIntelligenceOvervie9Artificial
Intelligence10November
24,
2019Data
Domain?Image,
volume,
video
lie
on?2D,
3D,
2D+1
Euclidean
domains?Sentence,
word,
sound
lie
on?1D
Euclidean
domain?These
domains
have
strong
regular
spatial
structures.?All
ConvNet
operations
are
mathematically
well
defined
and
fast(convolution,
pooling).ArtificialIntelligence10Novem10
Artificial
Intelligence11Graph
Structured
Data
November
24,
2019 ArtificialIntelligenceGraph1112November
24,
2019
Artificial
IntelligenceHow
CNNs
for
Graphs?
?
Translation
?
Downsampling
(Pooling)12November24,2019 Artificia1213November
24,
2019
Artificial
IntelligenceMotivating
Example
?
Co-authorship
Network
?
Nodes:
Authors,
Edges:
Co-authorship13November24,2019 Artifici1314November
24,
2019
Artificial
IntelligenceMotivating
Example:
Co-authorship
Network
?
Node
Classification:
(Semi-supervised
Learning)
?
Predict
research
area
of
unlabeled
authors14November24,2019 Artifici1415November
24,
2019
Artificial
IntelligenceMotivating
Example
?
Identify
Communities:
(Unsupervised)
?
Grouping
authors
with
similar
research
interests15November24,2019 Artifici1516November
24,
2019
Artificial
IntelligenceMotivating
Example
?
Graph
Classification:
(Supervised)
?
Identifying
class
of
each
community.16November24,2019 Artifici16
Artificial
Intelligence17Overview
November
24,
2019 ArtificialIntelligenceOvervi1718November
24,
2019
Artificial
IntelligenceEmbedding
Nodes
?
Goal
is
to
encode
nodes
so
that
similarity
in
the
embedding
space
(e.g.,
dot
product)
approximates
similarity
in
the
original
network.18November24,2019 Artifici18
Artificial
Intelligence19Embedding
Nodes
November
24,
2019 ArtificialIntelligenceEmbedd19Artificial
Intelligence20November
24,
2019Two
Key
Components?
Encoder
maps
each
node
to
a
low-dimensional
vector.?
Similarity
function
specifies
how
relationships
in
vector
space
map
to
relationships
in
the
original
network.ArtificialIntelligence20Novem20
Artificial
Intelligence21Two
Key
Components
?
Shallow
encoders:
November
24,
2019 ArtificialIntelligenceTwoKe21Artificial
Intelligence22November
24,
2019Two
Key
Components?
Limitations
of
shallow
encoding:?
O(|V|)
parameters
are
needed:
there
no
parameter
sharing
and
every
node
has
its
own
unique
embedding
vector.?
Inherently
“transductive”:
It
is
impossible
to
generate
embeddings
for
nodes
that
were
not
seen
during
training.?
Do
not
incorporate
node
features:
Many
graphs
have
features
that
we
can
and
should
leverage.ArtificialIntelligence22Novem22
Artificial
Intelligence23Graph
Neural
Network
?
Graph
Neural
Network
for
Deeper
encoding!
November
24,
2019 ArtificialIntelligenceGraph23
Artificial
Intelligence24Graph
Neural
Network
November
24,
2019 ArtificialIntelligenceGraph24Artificial
Intelligence25November
24,
2019Neighborhood
Aggregation?
Key
idea:
Generate
node
embeddings
based
on
local
neighborhoods.ArtificialIntelligence25Novem25Artificial
Intelligence26November
24,
2019Neighborhood
Aggregation?
Intuition:
Nodes
aggregate
information
from
their
neighbors
using
neural
networksArtificialIntelligence26Novem26Artificial
Intelligence27November
24,
2019Neighborhood
Aggregation?
Intuition:
Nodes
aggregate
information
from
their
neighbors
using
neural
networksArtificialIntelligence27Novem27
Artificial
Intelligence28Neighborhood
Aggregation
?
Intuition:
Network
neighborhood
defines
a
computation
graph
November
24,
2019 ArtificialIntelligenceNeighb28Artificial
Intelligence29November
24,
2019Neighborhood
Aggregation?
Nodes
have
embeddings
at
each
layer.?
Model
can
be
arbitrary
depth.?
“l(fā)ayer-0”
embedding
of
node
u
is
its
input
feature,
i.e.
xu.ArtificialIntelligence29Novem29Artificial
Intelligence30November
24,
2019Neighborhood
Aggregation?
Neighborhood
aggregation
can
be
viewed
as
a
center-
surround
filter.?
Mathematically
related
to
spectral
graph
convolutions
(
Bronstein
et
al.,
2017)ArtificialIntelligence30Novem30
Artificial
Intelligence31Neighborhood
Aggregation
?
Key
distinctions
are
in
how
different
approaches
aggregate
information
across
the
layers.
November
24,
2019 ArtificialIntelligenceNeighb31Artificial
Intelligence32November
24,
2019Neighborhood
Aggregation?
Basic
approach:
Average
neighbor
information
and
apply
a
neural
network.ArtificialIntelligence32Novem32Artificial
Intelligence33November
24,
2019Neighborhood
Aggregation?
Basic
approach:
Average
neighbor
information
and
apply
a
neural
network.ArtificialIntelligence33Novem33Artificial
Intelligence34November
24,
2019Training
the
Model?
How
do
we
train
the
model
to
generate
“high-quality”
embeddings?ArtificialIntelligence34Novem34
Artificial
Intelligence35Training
the
Model
November
24,
2019 ArtificialIntelligenceTraini35
Artificial
Intelligence36Training
the
Model
November
24,
2019 ArtificialIntelligenceTraini36Artificial
Intelligence37November
24,
2019Training
the
Model?
Alternative:
Directly
train
the
model
for
a
supervised
task
(e.g.,
node
classification):ArtificialIntelligence37Novem37Artificial
Intelligence38November
24,
2019Training
the
Model?
Alternative:
Directly
train
the
model
for
a
supervised
task
(e.g.,
node
classification):ArtificialIntelligence38Novem38
Artificial
Intelligence39Overview
of
Model
Design
November
24,
2019 ArtificialIntelligenceOvervi39
Artificial
Intelligence40Overview
of
Model
Design
November
24,
2019 ArtificialIntelligenceOvervi40
Artificial
Intelligence41Overview
of
Model
Design
November
24,
2019 ArtificialIntelligenceOvervi41Artificial
Intelligence42November
24,
2019Inductive
Capability?
The
same
aggregation
parameters
are
shared
for
all
nodes.?
The
number
of
model
parameters
is
sublinear
in
|V|and
we
can
generalize
to
unseen
nodes!ArtificialIntelligence42Novem42
Artificial
Intelligence43Inductive
Capability
November
24,
2019 ArtificialIntelligenceInduct43
Artificial
Intelligence44Inductive
Capability
November
24,
2019 ArtificialIntelligenceInduct4445November
24,
2019
Artificial
IntelligenceGraphConvolutionalNetworks(GCN)45November24,2019 Artificial4546November
24,
2019
Artificial
IntelligenceGraphConvolutionalNetworks(GCN)46November24,2019 Artificial4647November
24,
2019
Artificial
IntelligenceGraphConvolutionalNetworks(GCN)47November24,2019 Artificial4748November
24,
2019
Artificial
IntelligenceGraphConvolutionalNetworks(GCN)48November24,2019 Artificial4849November
24,
2019
Artificial
Intelligenceal.,
EMNLP
‘17]49November24,2019 Artificial4950November
24,
2019
Artificial
IntelligenceMessagePassingNeuralNetworks[Gilmeretal.,ICML‘17]50November24,2019 Artificial5051November
24,
2019
Artificial
IntelligenceMessagePassingNeuralNetworks[Gilmeretal.,ICML‘17]51November24,2019 Artificial5152November
24,
2019
Artificial
IntelligenceHypergraphConvolutionalNetwork(Yadatietal.NeurIPS‘19)52November24,2019 Artificial5253November
24,
2019
Artificial
IntelligenceExample:
GNNs
for
Semantic
Role
Labeling53November24,2019 Artificial53
Artificial
Intelligence54Overview
November
24,
2019 ArtificialIntelligenceOvervi5455November
24,
2019
Artificial
IntelligenceNeighborhood
Aggregations
in
GCNs???Standard
GCN
neighborhood
aggregationNo
restriction
on
influence
neighborhoodMethods:??Graph
Attention
Networks
(GAT)Confidence-based
GCN
(ConfGCN)55November24,2019 Artificial5556November
24,
2019
Artificial
IntelligenceGraph
Attention
Networks
(Velickovic′
et
al.
ICLR
‘18)56November24,2019 Artificial5657November
24,
2019
Artificial
IntelligenceGraph
Attention
Networks
(Velickovic′
et
al.
ICLR
‘18)57November24,2019 Artificial5758November
24,
2019
Artificial
IntelligenceGraph
Attention
Networks
(Velickovic′
et
al.
ICLR
‘18)58November24,2019 Artificial5859November
24,
2019
Artificial
IntelligenceGraph
Attention
Networks
(Velickovic′
et
al.
ICLR
‘18)59November24,2019 Artificial59
Artificial
Intelligence60Overview
November
24,
2019 ArtificialIntelligenceOvervi6061November
24,
2019
Artificial
IntelligenceMotivating
Example
?
Identify
Communities:
(Unsupervised)
?
Grouping
authors
with
similar
research
interests61November24,2019 Artifici6162November
24,
2019
Artificial
Intelligence
Unsupervised
Representation
Learning?
Labeled
data
is
expensive?
Allows
to
discover
interesting
structure
from
large-
scale
graphs62November24,2019 Artifici6263November
24,
2019
Artificial
Intelligence
Unsupervised
Representation
Learning?
Labeled
data
is
expensive?
Allows
to
discover
interesting
structure
from
large-scale
graphs?
Methods
?
GraphSAGE
?
Graph
Auto-Encoder
(GAE)
?
Deep
Graph
Infomax
(DGI)63November24,2019 Artific6364November
24,
2019
Artificial
IntelligenceGraphSAGE
[Hamilton
et
al.
NeurIPS
‘17]64November24,2019 Artificial6465November
24,
2019
Artificial
IntelligenceGraphSAGE
[Hamilton
et
al.
NeurIPS
‘17]65November24,2019 Artificial6566November
24,
2019
Artificial
Intelligence
Gated
Graph
Neural
Networks[Li
et
al.
ICLR
‘16]?
GCNs
and
GraphSAGE
generally
only
2-3
layers
deep.66November24,2019 Artificia6667November
24,
2019
Artificial
Intelligence
Gated
Graph
Neural
Networks[Li
et
al.
ICLR
‘16]?
But
what
if
we
want
to
go
deeper?67November24,2019 Artificia6768November
24,
2019
Artificial
Intelligence
Gated
Graph
Neural
Networks[Li
et
al.
ICLR
‘16]?
How
can
we
build
models
with
many
layers
of
neighborhood
aggregation??
Challenges:
?
Overfitting
from
too
many
parameters.
?
Vanishing/exploding
gradients
during
backpropagation.?
Idea:
Use
techniques
from
modern
recurrent
neural
networks!68November24,2019 Artific6869November
24,
2019
Artificial
Intelligence
Gated
Graph
Neural
Networks[Li
et
al.
ICLR
‘16]?
Idea
1:
Parameter
sharing
across
layers.69November24,2019 Artificia6970November
24,
2019
Artificial
Intelligence
Gated
Graph
Neural
Networks[Li
et
al.
ICLR
‘16]?
Idea
2:
Recurrent
state
update.70November24,2019 Artificia7071November
24,
2019
Artificial
Intelligence
Gated
Graph
Neural
Networks[Li
et
al.
ICLR
‘16]?
Intuition:
Neighborhood
aggregation
with
RNN
state
update.71November24,2019 Artificia7172November
24,
2019
Artificial
IntelligenceGated
Graph
Neural
Networks[Li
et
al.
ICLR
‘16]???Can
handle
models
with
>20
layers.Most
real-world
networks
have
small
diameters
(e.g.,
less
than
7).Allows
for
complex
information
about
global
graph
structure
to
bepropagated
to
all
nodes.72November24,2019 Artificial7273November
24,
2019
Artificial
IntelligenceGated
Graph
Neural
Networks[Li
et
al.
ICLR
‘16]73November24,2019 Artificial73
Artificial
Intelligence74Zero-shot
Learning
November
24,
2019 ArtificialIntelligenceZero-s74
Artificial
Intelligence75GCN
predicts
visual
classifier
November
24,
2019 ArtificialIntelligenceGCNpr75
Artificial
Intelligence76Visual
Question
Answering
November
24,
2019 ArtificialIntelligenceVisual76GCN
for
F-VQA
November
24,
2019
Artificial
Intelligence[Narasimhan
et
al.,
NeurIPS’18]
77GCNforF-VQA ArtificialInte77
Artificial
Intelligence78Summary
November
24,
2019 ArtificialIntelligenceSummar7879November
24,
2019
Artificial
IntelligenceSummary??Graphs
are
everywhere
and
effective
tool
for
exploiting
such
graphstructure
in
end-to-end
learning.GNNs
are
versatile,
can
be
applied
over???Learning
settings:
Semi-supervisedGraph
granularity:
node
level,
link,
subgraph,
whole
graphGraph
types:
undirected,
directed,
multi-relational??GNNs
have
achieved
considerable
success
on
several
tasks.Many
more
possibilities
ahead!79November24,2019 Artificial79Lecture
11:
Graph
NeuralNetworksLecture11:GraphNeuralNetwor80Artificial
Intelligence2Natural
Language
Processing
?
Question
Answering
?
Information
Extraction
?
Machine
Translation
?
......
November
24,
2019ArtificialIntelligenceNatural81Artificial
Intelligence3Question
Answering
November
24,
2019ArtificialIntelligenceQuestio82Artificial
Intelligence4Information
Extraction
November
24,
2019ArtificialIntelligenceInforma83Artificial
Intelligence5Machine
Translation
November
24,
2019ArtificialIntelligenceMachine84Artificial
Intelligence6Graphs
are
everywhere
in
NLP
November
24,
2019ArtificialIntelligenceGraphs85Artificial
Intelligence7Deep
Learning
in
NLP
November
24,
2019ArtificialIntelligenceDeepLe86Artificial
Intelligence8Overview
November
24,
2019ArtificialIntelligenceOvervie87Artificial
Intelligence9Overview
November
24,
2019ArtificialIntelligenceOvervie88Artificial
Intelligence10November
24,
2019Data
Domain?Image,
volume,
video
lie
on?2D,
3D,
2D+1
Euclidean
domains?Sentence,
word,
sound
lie
on?1D
Euclidean
domain?These
domains
have
strong
regular
spatial
structures.?All
ConvNet
operations
are
mathematically
well
defined
and
fast(convolution,
pooling).ArtificialIntelligence10Novem89
Artificial
Intelligence11Graph
Structured
Data
November
24,
2019 ArtificialIntelligenceGraph9012November
24,
2019
Artificial
IntelligenceHow
CNNs
for
Graphs?
?
Translation
?
Downsampling
(Pooling)12November24,2019 Artificia9113November
24,
2019
Artificial
IntelligenceMotivating
Example
?
Co-authorship
Network
?
Nodes:
Authors,
Edges:
Co-authorship13November24,2019 Artifici9214November
24,
2019
Artificial
IntelligenceMotivating
Example:
Co-authorship
Network
?
Node
Classification:
(Semi-supervised
Learning)
?
Predict
research
area
of
unlabeled
authors14November24,2019 Artifici9315November
24,
2019
Artificial
IntelligenceMotivating
Example
?
Identify
Communities:
(Unsupervised)
?
Grouping
authors
with
similar
research
interests15November24,2019 Artifici9416November
24,
2019
Artificial
IntelligenceMotivating
Example
?
Graph
Classification:
(Supervised)
?
Identifying
class
of
each
community.16November24,2019 Artifici95
Artificial
Intelligence17Overview
November
24,
2019 ArtificialIntelligenceOvervi9618November
24,
2019
Artificial
IntelligenceEmbedding
Nodes
?
Goal
is
to
encode
nodes
so
that
similarity
in
the
embedding
space
(e.g.,
dot
product)
approximates
similarity
in
the
original
network.18November24,2019 Artifici97
Artificial
Intelligence19Embedding
Nodes
November
24,
2019 ArtificialIntelligenceEmbedd98Artificial
Intelligence20November
24,
2019Two
Key
Components?
Encoder
maps
each
node
to
a
low-dimensional
vector.?
Similarity
function
specifies
how
relationships
in
vector
space
map
to
relationships
in
the
original
network.ArtificialIntelligence20Novem99
Artificial
Intelligence21Two
Key
Components
?
Shallow
encoders:
November
24,
2019 ArtificialIntelligenceTwoKe100Artificial
Intelligence22November
24,
2019Two
Key
Components?
Limitations
of
shallow
encoding:?
O(|V|)
parameters
are
needed:
there
no
parameter
sharing
and
every
node
has
its
own
unique
embedding
vector.?
Inherently
“transductive”:
It
is
impossible
to
generate
embeddings
for
nodes
that
were
not
seen
during
training.?
Do
not
incorporate
node
features:
Many
graphs
have
features
that
we
can
and
should
leverage.ArtificialIntelligence22Novem101
Artificial
Intelligence23Graph
Neural
Network
?
Graph
Neural
Network
for
Deeper
encoding!
November
24,
2019 ArtificialIntelligenceGraph102
Artificial
Intelligence24Graph
Neural
Network
November
24,
2019 ArtificialIntelligenceGraph103Artificial
Intelligence25November
24,
2019Neighborhood
Aggregation?
Key
idea:
Generate
node
embeddings
based
on
local
neighborhoods.ArtificialIntelligence25Novem104Artificial
Intelligence26November
24,
2019Neighborhood
Aggregation?
Intuition:
Nodes
aggregate
information
from
their
neighbors
using
neural
networksArtificialIntelligence26Novem105Artificial
Intelligence27November
24,
2019Neighborhood
Aggregation?
Intuition:
Nodes
aggregate
information
from
their
neighbors
using
neural
networksArtificialIntelligence27Novem106
Artificial
Intelligence28Neighborhood
Aggregation
?
Intuition:
Network
neighborhood
defines
a
computation
graph
November
24,
2019 ArtificialIntelligenceNeighb107Artificial
Intelligence29November
24,
2019Neighborhood
Aggregation?
Nodes
have
embeddings
at
each
layer.?
Model
can
be
arbitrary
depth.?
“l(fā)ayer-0”
embedding
of
node
u
is
its
input
feature,
i.e.
xu.ArtificialIntelligence29Novem108Artificial
Intelligence30November
24,
2019Neighborhood
Aggregation?
Neighborhood
aggregation
can
be
viewed
as
a
center-
surround
filter.?
Mathematically
related
to
spectral
graph
convolutions
(
Bronstein
et
al.,
2017)ArtificialIntelligence30Novem109
Artificial
Intelligence31Neighborhood
Aggregation
?
Key
distinctions
are
in
how
different
approaches
aggregate
information
across
the
layers.
November
24,
2019 ArtificialIntelligenceNeighb110Artificial
Intelligence32November
24,
2019Neighborhood
Aggregation?
Basic
approach:
Average
neighbor
information
and
apply
a
neural
network.ArtificialIntelligence32Novem111Artificial
Intelligence33November
24,
2019Neighborhood
Aggregation?
Basic
approach:
Average
neighbor
information
and
apply
a
neural
network.ArtificialIntelligence33Novem112Artificial
Intelligence34November
24,
2019Training
the
Model?
How
do
we
train
the
model
to
generate
“high-quality”
embeddings?ArtificialIntelligence34Novem113
Artificial
Intelligence35Training
the
Model
November
24,
2019 ArtificialIntelligenceTraini114
Artificial
Intelligence36Training
the
Model
November
24,
2019 ArtificialIntelligenceTraini115Artificial
Intelligence37November
24,
2019Training
the
Model?
Alternative:
Directly
train
the
model
for
a
supervised
task
(e.g.,
node
classification):ArtificialIntelligence37Novem116Artificial
Intelligence38November
24,
2019Training
the
Model?
Alternative:
Directly
train
the
model
for
a
supervised
task
(e.g.,
node
classification):ArtificialIntelligence38Novem117
Artificial
Intelligence39Overview
of
Model
Design
November
24,
2019 ArtificialIntelligenceOvervi118
Artificial
Intelligence40Overview
of
Model
Design
November
24,
2019 ArtificialIntelligenceOvervi119
Artificial
Intelligence41Overview
of
Model
Design
November
24,
2019 ArtificialIntelligenceOvervi120Artificial
Intelligence42November
24,
2019Inductive
Capability?
The
same
aggregation
parameters
are
shared
for
all
nodes.?
The
number
of
model
parameters
is
sublinear
in
|V|and
we
can
generalize
to
unseen
nodes!ArtificialIntelligence42Novem121
Artificial
Intelligence43Inductive
Capability
November
24,
2019 ArtificialIntelligenceInduct122
Artificial
Intelligence44Inductive
Capability
November
24,
2019 ArtificialIntelligenceInduct12345November
24,
2019
Artificial
IntelligenceGraphConvolutionalNetworks(GCN)45November24,2019 Artificial12446November
24,
2019
Artificial
IntelligenceGraphConvolutionalNetworks(GCN)46November24,2019 Artificial12547November
24,
2019
Artificial
IntelligenceGraphConvolutionalNetworks(GCN)47November24,2019 Artificial12648November
24,
2019
Artificial
IntelligenceGraphConvolutionalNetworks(GCN)48November24,2019 Artificial12749November
24,
2019
Artificial
Intelligenceal.,
EMNLP
‘17]49November24,2019 Artificial12850November
24,
2019
Artificial
IntelligenceMessagePassingNeuralNetworks[Gilmeretal.,ICML‘17]50November24,2019 Artificial12951November
24,
2019
Artificial
IntelligenceMessagePassingNeuralNetworks[Gilmeretal.,ICML‘17]51November24,2019 Artificial13052November
24,
2019
Artificial
IntelligenceHypergraphConvolutionalNetwork(Yadatietal.NeurIPS‘19)52November24,2019 Artificial13153November
24,
2019
Artificial
IntelligenceExample:
GNNs
for
Semantic
Role
Labeling53November24,2019 Artificial132
Artificial
Intelligence54Overview
November
24,
2019 ArtificialIntelligenceOvervi13355November
24,
2019
Artificial
IntelligenceNeighborhood
Aggregations
in
GCNs???Standard
GCN
neighborhood
aggregationNo
restriction
on
influence
neighborhoodMethods:??Graph
Attention
Networks
(GAT)Confidence-based
GCN
(ConfGCN)55November24,2019 Artificial13456November
24,
2019
Artificial
IntelligenceGraph
Attention
Networks
(Velickovic′
et
al.
ICLR
‘18)56November24,2019 Artificial13557November
24,
2019
Artificial
IntelligenceGraph
Attention
Networks
(Velickovic′
et
al.
ICLR
‘18)57November24,2019 Artificial13658November
24,
2019
Artificial
IntelligenceGraph
Attention
Networks
(Velickovic′
et
al.
ICLR
‘18)58November24,2019 Artificial13759November
24,
2019
Artificial
IntelligenceGraph
Attention
Networks
(Velickovic′
et
al.
ICLR
‘18)59November24,2019 Artificial138
Artificial
Intelligence60Overview
November
24,
2019 ArtificialIntelligenceOvervi13961November
24,
2019
Artificial
IntelligenceMotivating
Example
?
Identify
Communities:
(Unsupervised)
?
Grouping
authors
with
similar
research
interests61November24,2019 Artifici14062November
24,
2019
Artificial
Intelligence
Unsupervised
Representation
Learning?
Labeled
data
is
expensive?
Allows
to
discover
interesting
structure
from
large-
scale
graphs62November24,2019 Artifici14163November
24,
2019
Artificial
Intelligence
Unsupervised
Representation
Learning?
Labeled
data
is
expensive?
Allows
to
discover
interesting
structure
from
large-scale
graphs?
Methods
?
GraphSAGE
?
Graph
Auto-Encoder
(GAE)
?
Deep
Graph
Infomax
(DGI)63November24,2019 Artific14264November
24,
2019
Artificial
IntelligenceGraphSAGE
[Hamilton
et
al.
NeurIPS
‘17]64November24,2019 Artificial14365November
24,
2019
Artificial
IntelligenceGraphSAGE
[Hamilton
et
al.
NeurIPS
‘17]65November24,2019 Artificial14466November
24,
2019
Artificial
Intelligence
Gated
Graph
Neural
Networks[Li
et
al.
ICLR
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