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ing sets 81 k q, Dk = (xki , yik ) by converting each
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1: Summary
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methods.
j (i) denotes
Figure 2: Multi-label Table
classification
by
mining
het- the index set of instances
! Instance
and Label Correlations
on each label, using the extended training sets.
We have the following evalua
tion network 1 as Figure 1 that contains
abundant
i-th instance through meta-pathPublication
Pj0 one
2 Sclassifier
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where
Pj (k)knowledge
denotes the4 index
set of labels
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Method
Type
ofthat
Classification
Type of Correlations Exploited
Iterative
Inference: Overall, it is an iterative classification
erogeneous
information
networks.to the
• Micro F1 [10, 14, 21]: is
Experiments
Table 1: Summary of compared methods.
about the relationships among di↵erent
types
of entities
into the
k-th
label through
meta-path Pj 2 S` .
algorithm [22] for the inference step. During the inference,
average of Precision and Rec
Bsvm
Binary Classification
all independent
[2]
we iteratively update
the label set predictions of the testing
cluding chemical compounds and gene targets, we can make
that the score is first comput
3.4 The UnifiedType
Model
Method
Type of Classification
of Correlations Exploited
Publication
instances, and the relational features corresponding to the
then averaged with equal im
Ecc
Multi-Label
Classification
¨
label
correlcation
from
data
samples
[27]
use of the domain knowledge within this
network
to
facilitate
3.3 Meta-path-basedreasons:
Instance
Correlations
Bsvm
all independent
[2]
1) they
belong to the Binary
sameClassification
gene
2) perform
they
In family;
order to
multi-label
collective
label classification
and instance correlations.
multi-label classification.
PIsl
Collective Classification
≠ instance correlations from heterogeneous network
[18]
2
share similarclassification
pathways;
3)usually
theyMulti-Label
are inter-connected
through
Existing approaches for multi-label
more
e↵ectively
heterogeneous
information
networks, [27]
in
micro-F1(h, DU ) = Pn
Ecc
Classification
¨ in
label
correlcation from data
samples
First, the heterogeneous information network can provide
¨ label correlation
from can
data samples
i=
Compared
4. EXPERIMENTS
PPI
links,
etc.
information
networks
have i.i.d. assumptions, where
the
label
set Heterogeneous
predictions
on
this
paper,
we
explicitly
consider
both
meta-path-based
laPIsl
Collective
≠ instance
correlations
from
heterogeneous
[18]
Icml
Multi-Label Collective
Classification
Æ Classification
instance correlation from
homogeneous
network
[17] network
abundant knowledge about the relationships among di↵erThe
larger
the
value,
the
bet
provide
complex relationships among the label concepts,
inMethods
label correlation from data samples 4.1 Data Collections
ent gene targets. In the network, gene targets are inter• Hamming loss [8, 37]: ev
PIml
≠ instance correlations¨from
network
This paper
volvingMulti-Label
multiple Collective
types
ofClassification
label correlations
[28]. How
toheterogeneous
Icml
Multi-Label
Collective Classification
Æ instance
correlation from homogeneous network
[17]
between true labels and pred
In order to evaluate the performances of multi-label collecconnected with many other types of nodes, such as diseases
Pathway%
≠ meta-path-based
instance correlation
exploit the linkagePIml
semantics
is a very
challenging
problem
Multi-Label
Collective
Classification
≠ instance correlations from heterogeneous
network
This
paper
tive classification
in heterogeneous
information networks, we
PIPL
Multi-Label Collective Classification Ø label correlations from heterogeneous network
This paper
and pathways. The gene targets, that are linked with similar
HammingLoss(h, DU ) =
[28], which has not yet been explored in this context. ≠ meta-path-based instance correlationhad our algorithm tested on a bioinformatic dataset SLAP
diseases or pathways, are more likely to appear together in
[4],
which
heterogeneous
network composed by over
PIPL instance
Multi-Label
Collective
label correlations
from heterogeneous
network
paper
Mining
heterogeneous
correlations:
In(rank)
multi-label
Table 2:
Classification
performances
“average
score ±Classification
std
” onØ gene-disease
association
prediction
task.is a This
290Kbetter
nodes and 720K edges. As shown in Figure 1, the
the same label set than those without such connections. Sec- “#” indicates
where stands for the symm
Gene%Ontology%
the smaller
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better theinstances
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indicates
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classification,
the
label
sets
of di↵erent
can “"”
also
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Chemical%Ontology
%
SLAP
dataset
contains
integrated
data
related
to
chemiTask
1:
Gene-Disease
Association
Table
2:
Classification
performances
“average
score
”
on
gene-disease
association
prediction
task.
denotes the l1 -norm. The sm
ond, the heterogeneous information network can also provide the performance.
correlated with “#”
each
otherthe
through
types
of the
relaindicates
smaller multiple
the value the
better
performance; “"” indicates thecal
larger
the value
the diseases,
better side e↵ects, pathways etc.
compounds,
genes,
performance.
hasPathway0
methods
abundant knowledge about the relationships among di↵erthe performance.
Specifically, there are two di↵erent prediction tasks studied
• Subset 0/1 Loss [8, 10]: ev
tionships.
For example,
di↵erent chemical
compounds
can PIsl
criteria #label
Bsvm
Ecc
Icml
PIml
PIPL
ent chemical compounds. In the network, chemical commethods
in this
section:
set prediction.
be correlated
for various
reasons:
1) they
have
similar
side
hasGeneOntology0
PPI0
10
0.360±0.082
(6)
0.387±0.073
(4)
0.366±0.079
(5)
0.390±0.115
(3)
0.399±0.107
(2)
0.400±0.106
(1)
•
Gene-Disease
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Prediction:
The
first
task
we
criteria
#label
Bsvm
Ecc
Icml
PIsl
PIml
PIPL
pounds are also connected with other types of objects, such
e↵ects; 2) they
have
similar
3)(5) they
20
0.385±0.046
(6) chemical
0.406±0.043 ontologies;
(4)
0.389±0.066
0.417±0.055 (3) 0.426±0.055 (2) 0.433±0.066
(1)
is gene-disease
association prediction, where we treat
10
0.360±0.082
0.387±0.073
0.366±0.079
0.390±0.115 studied
0.399±0.107
0.400±0.106
as side e↵ects
and
chemical
ontologies.
The
chemical
comSubsetLoss(h, DU ) =
Micro-F1
"
30
0.317±0.035
(6)
0.359±0.027
(2)
0.343±0.039
(5)
0.342±0.037
(4)
0.355±0.013
(3)
0.360±0.007
(1)
hasChemicalOntology0
as the instances,
and diseases as the labels. In SLAP
have similar40substructures,
etc.20 Heterogeneous
information
0.385±0.046
0.406±0.043
0.389±0.066
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pounds, that are linked with similar side e↵ects or chemical
Micro-F1 " 30
0.317±0.035
0.359±0.027
0.343±0.039
0.342±0.037 dataset,
0.355±0.013
0.360±0.007
each
gene
can
cause or be related to multiple disPIPL%outperforms%others%on%both%
networks can
complex
among
di↵erent
50 provide
0.303±0.055
(6) relationships
0.346±0.059
(4)
0.321±0.063
(5)
0.348±0.064
(3)
0.360±0.075
(2)
0.366±0.078
(1)
40
0.342±0.045
0.386±0.032
0.339±0.042
0.382±0.034
0.387±0.030
0.391±0.030
eases
simultaneously.
The label set of each gene is defined
I(·) denotes the indicator fun
ontologies, are more likely tocauseSideEffect0
have similar label sets than the
50
0.346±0.059
0.321±0.063
0.348±0.064
0.360±0.075
0.366±0.078
datasets,%indicaSng%that%PIPL%can%
instances, involving
multiple(1)types
of0.303±0.055
correlations.
10
0.011±0.002
0.013±0.002
(6)
0.011±0.002
(1)
0.011±0.003
(1)
0.011±0.003
(1)
0.011±0.002
(1)
inTissue0
chemicals without such connections.
20
0.008±0.001 (1) 10
(6)
0.008±0.000
0.008±0.001
(1)
0.008±0.001
(1)
0.008±0.001
(1)
0.011±0.002
0.013±0.002
0.011±0.002
0.011±0.003
0.011±0.003
0.011±0.002
exploit%HIN%to%extract%correlaSons%
In this paper,
we
study how 0.010±0.000
we can
facilitate
the (1)
multiTable
4:
Examples
of
meta-paths
used in PIPL method
Hamming Loss # 30
0.008±0.000 (5) 20
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(6)
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0.010±0.000
0.008±0.000
0.008±0.001
0.008±0.001
0.008±0.001
By mining the linkage structure of heterogeneous informaamong%instances%and%labels%for%
40
0.007±0.000
(4)
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(4)
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(4)
0.006±0.000
(1)
0.006±0.000
(1)
0.006±0.000
(1)
label classification
process
by
the
correlations
among
Hamming
Loss
# mining
30
0.008±0.000
0.009±0.000
0.007±0.000
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0.007±0.000
Task
Meta-path
Correlation
tionbind0
networks, multiple types of relationships among di↵er50
0.006±0.001 (1) 40
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0.006±0.000
mulS8label%classificaSon.
instances and labels from heterogeneous
information
net- 0.006±0.001
treated
treat
50
0.006±0.001
0.007±0.001Disease
0.006±0.001
0.006±0.001
0.006±0.001
!Chemical
compound
!Disease
label correlation
ent class labels and data samples can be extracted. Such re10
0.108±0.023 (5) 0.125±0.020 (6) 0.107±0.020 (4) 0.103±0.024 (1)
0.103±0.024
(1)
0.103±0.023
(1)
inGeneFamily0
works. We propose
a novel (4)
solution,
called
PIPL,
to (5)
assign
treated
has0.103±0.024
in0.103±0.023
treat
10
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20
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1):
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stances
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public datasets related to systems chemical biology: such as
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stance correlation): This method is extended from PIsl
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400
600
800
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1600
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stance correlation): This
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instance-cross-label
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ensemble
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training
lective classification
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intion):
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tion
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stances with random label orders.
Figure 5: Micro-F1 scores with di↵erent
number of features.
the instance correlations from heterogeneous networks.
stances with random label orders.
classification in heterogenous information networks. The
However,
label can
correlations
in this method
geous networks (i.e., PIsl,
PIml,the
PIPL)
achieve used
better
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based in-the label correlations
in from
this method
are directly used
derived
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di↵erence
PIPL +
and
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performances than the Icml that only exploits the homoge• PIsl
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We compare
with another
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5:
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relatedofdatasets
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are network
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heterogeneous
Acknowledgements
does
not consider the meta-path based label correlaneous
among instances.
stance correlation): We compare
another
base- For each ing
line using with
binary
decomposition.
binary
clasheterogeneous
type PIPL
of
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tion.
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that
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line using binary decomposition.
each
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best
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Especially,
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outsification task, we use the meta-path based collective
• PIPL
(meta-path
based instance and
label correlaFor a fair comparison, we use LibSVM [3] with linear ker- Geneperforms
PIsl, by takingsingle-label
meta-path based(1,1)
label
[34] PIml andhomogeneous
1,243
nel and default parameter as the base classifier for all the Citeseer
correlation
heterogeneous informa[22] into consideration.
homogeneous In single-label
(1,1)
3,312
compared methods. The maximum number of iterations in WebKB
tion networks,
bothhomogeneous
of instances and
candidate labels
can
[7]
single-label
(1,1)
3,877
the methods PIPL, PIml and PIsl are all set as 10.
be correlated
with homogeneous
each other via diverse
semantic meanDBLP
[17]
multi-label
(1,1)
4638
show four examples
of the meta (5,5)
paths
ACMings. In
[18]Table 4, weheterogeneous
single-label
12,499
4.4 Performances of Multi-Label Classification Coraused by
[23]PIPL method
heterogeneous
single-label
(5,5)to
4,330
in both tasks,
which correspond
[1]
heterogeneous
single-label
1,382
label correlations
and
instance correlations
separately.(5,5)
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We first study the e↵ectiveness of the proposed PIPL IMDB
NASD
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heterogeneous single-label
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Node Types
This%work%is%supported%in%part%by%NSF%through%grants%IIS80905215,%CNS81115234,%IIS80914934,%DBI80960443,%
gene
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