C OMPOSITION - BASED M ULTI -R ELATIONAL G RAPH C ONVOLUTIONAL N ETWORKS
A BSTRACT
Graph Convolutional Networks (GCNs) have recently been shown to be quite successful in modeling graph-structured data.
However, the primary focus has been on handling simple undirected graphs.
Multi-relational graphs are a more general and prevalent form of graphs where each edge has a label and direction associated with it.
Most of the existing approaches to handle such graphs suffer from over-parameterization and are restricted to learning representations of nodes only.
In this paper, we propose C OMP GCN, a novel Graph Convolutional framework which jointly embeds both nodes and relations in a relational graph.
C OMP GCN leverages a variety of entity-relation composition operations from Knowledge
Graph Embedding techniques and scales with the number of relations.
It also generalizes several of the existing multi-relational GCN methods.
We evaluate our proposed method on multiple tasks such as node classiﬁcation, link prediction, and graph classiﬁcation, and achieve demonstrably superior results.
We make the source code of C OMP GCN available to foster reproducible research.
I NTRODUCTION
Graphs are one of the most expressive data-structures which have been used to model a variety of problems.
Traditional neural network architectures like Convolutional Neural Networks (Krizhevsky et al., 2012) and Recurrent Neural Networks (Hochreiter & Schmidhuber, 1997) are constrained to handle only Euclidean data.
Recently, Graph Convolutional Networks (GCNs) (Bruna et al., 2013;
Defferrard et al., 2016) have been proposed to address this shortcoming, and have been successfully applied to several domains such as social networks (Hamilton et al., 2017), knowledge graphs (Schlichtkrull et al., 2017), natural language processing (Marcheggiani & Titov, 2017), drug discovery (Ramsundar et al., 2019) and natural sciences (Fout et al., 2017).
However, most of the existing research on GCNs (Kipf & Welling, 2016; Hamilton et al., 2017;
Veliˇckovi´c et al., 2018) have focused on learning representations of nodes in simple undirected graphs.
A more general and pervasive class of graphs are multi-relational graphs 1 .
A notable example of such graphs is knowledge graphs.
Most of the existing GCN based approaches for handling relational graphs (Marcheggiani & Titov, 2017; Schlichtkrull et al., 2017) suffer from overparameterization and are limited to learning only node representations.
Hence, such methods are not directly applicable for tasks that require relation embedding vectors such as link prediction.
Initial attempts at learning representations for relations in graphs (Monti et al., 2018; Beck et al., 2018) have shown some performance gains on tasks like node classiﬁcation and neural machine translation.
There has been extensive research on embedding Knowledge Graphs (KG) (Nickel et al., 2016;
Wang et al., 2017) where representations of both nodes and relations are jointly learned.
These methods are restricted to learning embeddings using link prediction objective.
Even though GCNs can learn from task-speciﬁc objectives such as classiﬁcation, their application has been largely restricted to non-relational graph setting.
Thus, there is a need for a framework which can utilize KG embedding techniques for learning task-speciﬁc node and relation embeddings.
In this paper, we propose
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CompGCN Update C OMP GCN, a novel GCN framework for multi-relational graphs which systematically leverages entity-relation composition operations from knowledge graph embedding techniques. C OMP GCN addresses the shortcomings of previously proposed GCN models by jointly learning vector representations for both nodes and relations in the graph.
An overview of C OMP GCN is presented in
Figure 1.
The contributions of our work can be summarized as follows:
We propose C OMP GCN, a novel framework for incorporating multi-relational information in Graph Convolutional Networks which leverages a variety of composition operations from knowledge graph embedding techniques to jointly embed both nodes and relations in a graph.
We demonstrate that C OMP GCN framework generalizes several existing multi-relational GCN methods (Proposition 4.1) and also scales with the increase in number of relations in the graph (Section 6.3).
Through extensive experiments on tasks such as node classiﬁcation, link prediction, and graph classiﬁcation, we demonstrate the effectiveness of our proposed method.
The source code of C OMP GCN and datasets used in the paper have been made available at http: //github.com/malllabiisc/CompGCN .
R ELATED W ORK
Graph Convolutional Networks: GCNs generalize Convolutional Neural Networks (CNNs) to non-Euclidean data.
GCNs were ﬁrst introduced by Bruna et al.
and later made scalable through efﬁcient localized ﬁlters in the spectral domain (Defferrard et al., 2016).
A ﬁrst-order approximation of GCNs using Chebyshev polynomials has been proposed by Kipf & Welling (2016).
Recently, several of its extensions have also been formulated (Hamilton et al., 2017; Veliˇckovi´c et al., 2018; Xu et al., 2019).
Most of the existing GCN methods follow Message Passing Neural
Networks (MPNN) framework (Gilmer et al., 2017) for node aggregation.
Our proposed method can be seen as an instantiation of the MPNN framework.
However, it is specialized for relational graphs.
GCNs for Multi-Relational Graph: An extension of GCNs for relational graphs is proposed by
Marcheggiani & Titov (2017).
However, they only consider direction-speciﬁc ﬁlters and ignore relations due to over-parameterization.
Schlichtkrull et al.
(2017) address this shortcoming by proposing basis and block-diagonal decomposition of relation speciﬁc ﬁlters.
Weighted Graph Convolutional
Network (Shang et al., 2019) utilizes learnable relational speciﬁc scalar weights during GCN ag- gregation.
While these methods show performance gains on node classiﬁcation and link prediction, they are limited to embedding only the nodes of the graph.
Contemporary to our work, Ye et al.
(2019) have also proposed an extension of GCNs for embedding both nodes and relations in multirelational graphs.
However, our proposed method is a more generic framework which can leverage any KG composition operator.
We compare against their method in Section 6.1.
Knowledge Graph Embedding: Knowledge graph (KG) embedding is a widely studied ﬁeld (Nickel et al., 2016;
Wang et al., 2017) with application in tasks like link prediction and question answering (Bordes et al., 2014).
Most of KG embedding approaches deﬁne a score function and train node and relation embeddings such that valid triples are assigned a higher score than the invalid ones.
Based on the type of score function, KG embedding method are classiﬁed as translational (Bordes et al., 2013; Wang et al., 2014b), semantic matching based (Yang et al., 2014;
Nickel et al., 2016) and neural network based (Socher et al., 2013; Dettmers et al., 2018).
In our work, we evaluate the performance of C OMP GCN on link prediction with methods of all three types.
B ACKGROUND
In this section, we give a brief overview of Graph Convolutional Networks (GCNs) for undirected graphs and its extension to directed relational graphs.
GCN on Undirected Graphs:
Given a graph G =
( V , E , X ) , where V denotes the set of vertices,
E is the set of edges, and X ∈ R |V|× d 0 represents d 0 -dimensional input features of each node.
The node representation obtained from a single GCN layer is deﬁned as: H = f
( ˆ A X W ) .
Here, ˆ A = (cid:101) D −
( A + I ) (cid:101)
D − 12 is the normalized adjacency matrix with added self-connections and (cid:101)
D is deﬁned as (cid:101) D ii =
(cid:80) j
( A + I ) ij .
The model parameter is denoted by W ∈ R d 0 × d 1 and f is some activation function.
The GCN representation H encodes the immediate neighborhood of each node in the graph.
For capturing multi-hop dependencies in the graph, several GCN layers can be stacked, one on the top of another as follows:
H k +
=
f
( ˆ AH k W k ) , where k denotes the number of layers,
W k ∈ R
d k × d k +1 is layer-speciﬁc parameter and H
= X .
GCN on Multi-Relational Graphs: For a multi-relational graph G =
( V , R , E , X ) , where R denotes the set of relations, and each edge ( u, v, r ) represents that the relation r ∈ R exist from node u to v .
The GCN formulation as devised by Marcheggiani & Titov (2017) is based on the assumption that information in a directed edge ﬂows along both directions.
Hence, for each edge ( u, v, r ) ∈ E , an inverse edge ( v, u, r − 1 ) is included in G .
The representations obtained after k layers of directed GCN is given by
[Equation]
Here, W kr denotes the relation speciﬁc parameters of the model.
However, the above formulation leads to over-parameterization with an increase in the number of relations and hence, Marcheggiani & Titov (2017) use direction-speciﬁc weight matrices.
Schlichtkrull et al.
(2017) address overparameterization by proposing basis and block-diagonal decomposition of W kr .
C OMP GCN D ETAILS
In this section, we provide a detailed description of our proposed method, C OMP GCN.
The overall architecture is shown in Figure 1.
We represent a multi-relational graph by G =
( V , R , E , X , Z ) as deﬁned in Section 3 where Z ∈ R |R|× d 0 denotes the initial relation features.
Our model is motivated by the ﬁrst-order approximation of GCNs using Chebyshev polynomials (Kipf & Welling, 2016).
Following Marcheggiani & Titov (2017), we also allow the information in a directed edge to ﬂow along both directions.
Hence, we extend E and R with corresponding inverse edges and relations, i.e.,
[Equation]
and R (cid:48)
=
R ∪ R inv ∪ {(cid:62)} , where R inv = { r − 1 | r ∈ R} denotes the inverse relations and (cid:62) indicates the self loop. [Table]
4. R ELATION - BASED C OMPOSITION
Unlike most of the existing methods which embed only nodes in the graph
[Table]
C OMP GCN learns a d -dimensional representation h r ∈ R d , ∀ r ∈ R along with node embeddings h v ∈ R d , ∀ v ∈ V .
Representing relations as vectors alleviates the problem of over-parameterization while applying
GCNs on relational graphs.
Further [Table] it allows C OMP GCN to exploit any available relation features ( Z ) as initial representations.
To incorporate relation embeddings into the GCN formulation, we leverage the entity-relation composition operations used in Knowledge Graph embedding approaches (Bordes et al., 2013; Nickel et al., 2016), which are of the form
[Equation]
Here, φ : R d × R d → R d is a composition operator, s , r , and o denote subject, relation and object in the knowledge graph and e (
· ) ∈ R
d denotes their corresponding embeddings.
In this paper, we restrict ourselves to non-parameterized operations like subtraction (Bordes et al., 2013), multiplication (Yang et al., 2014) and circular-correlation (Nickel et al., 2016).
However, C OMP GCN can be extended to parameterized operations like Neural Tensor Networks (NTN) (Socher et al., 2013) and
ConvE (Dettmers et al., 2018).
We defer their analysis as future work.
As we show in Section 6, the choice of composition operation is important in deciding the quality of the learned embeddings.
Hence, superior composition operations for Knowledge Graphs developed in future can be adopted to improve C OMP GCN’s performance further.
4. C OMP GCN U PDATE E QUATION
The GCN update equation (Eq. 1) deﬁned in Section 3 can be re-written as
[Equation]
where N ( v ) is a set of immediate neighbors of v for its outgoing edges.
Since this formulation suffers from over-parameterization, in C OMP GCN we perform composition ( φ ) of a neighboring node u with respect to its relation r as deﬁned above.
This allows our model to be relation aware while being linear ( O ( |R| d ) ) in the number of feature dimensions.
Moreover, for treating original, inverse, and self edges differently, we deﬁne separate ﬁlters for each of them.
The update equation of C OMP GCN is given as:
[Equation]
where x u , z r denotes initial features for node u and relation r respectively, h v denotes the updated representation of node v , and W λ ( r ) ∈ R d 1 × d 0 is a relation-type speciﬁc parameter.
In C OMP GCN, we use direction speciﬁc weights, i.e., λ ( r ) = dir( r ) , given as:
[Equation]
Methods
W kλ ( r ) φ ( h ku , h kr )
Kipf-GCN (Kipf & Welling, 2016)
W k h ku
Relational-GCN (Schlichtkrull et al., 2017)
W kr h ku
Directed-GCN (Marcheggiani & Titov, 2017)
W k dir( r ) h ku
Weighted-GCN (Shang et al., 2019)
W k
α kr h ku
Further, in C OMP GCN, after the node embedding update deﬁned in Eq. 2, the relation embeddings are also transformed as follows:
[Equation] where W rel ∈ R d 1 × d 0 is a learnable transformation matrix which projects the relations to the same embedding space as nodes and allows them to be utilized in the next C OMP GCN layer.
In Table 1, we present a contrast between C OMP GCN and other existing methods in terms of their features and parameter complexity.
Scaling with Increasing Number of Relations To ensure that C OMP GCN scales with the increasing number of relations, we use a variant of the basis formulations proposed in Schlichtkrull et al.
Instead of independently deﬁning an embedding for each relation, they are expressed as a linear combination of a set of basis vectors.
Formally, let { v 1 , v 2 , ..., v B } be a set of learnable basis vectors.
Then, initial relation representation is given as:
[Equation]
Here, α br ∈ R is relation and basis speciﬁc learnable scalar weight.
On Comparison with Relational-GCN Note that this is different from the basis formulation in
Schlichtkrull et al.
(2017), where a separate set of basis matrices is deﬁned for each GCN layer.
In contrast, C OMP GCN uses embedding vectors instead of matrices, and deﬁnes basis vectors only for the ﬁrst layer.
The later layers share the relations through transformations according to Equation 4.
This makes our model more parameter efﬁcient than Relational-GCN.
We can extend the formulation of Equation 2 to the case where we have k -stacked C OMP GCN layers.
Let h
k
+1 v denote the representation of a node v obtained after k layers which is deﬁned as
[Equation]
Similarly, let h k +1 r denote the representation of a relation r after k layers.
Then,
[Equation]
Here, h 0 v and h 0 r are the initial node ( x v ) and relation ( z r ) features respectively.
Proposition 4.1.
C OMP GCN generalizes the following Graph Convolutional based methods: KipfGCN (Kipf & Welling, 2016), Relational GCN (Schlichtkrull et al., 2017), Directed GCN (Marcheggiani & Titov, 2017), and Weighted GCN (Shang et al., 2019).
Proof.
For Kipf-GCN, this can be trivially obtained by making weights ( W λ ( r ) ) and composition function ( φ ) relation agnostic in Equation 5, i.e., W λ ( r )
=
W and φ ( h u , h r ) =
h u .
Similar reductions can be obtained for other methods as shown in Table 2.
[Table] [Table]
MRR
MR
[Table]
MR
[Table]
TransE (Bordes et al., 2013) .
- - DistMult (Yang et al., 2014) .
.39 ComplEx (Trouillon et al., 2016) .
.41 R-GCN (Schlichtkrull et al., 2017) .
- - - - KBGAN (Cai & Wang, 2018) .
- - ConvE (Dettmers et al., 2018) .
.40 ConvKB (Nguyen et al., 2018) .
.057 SACN (Shang et al., 2019) .
.43 HypER (Balaževi´c et al., 2019) .
RotatE (Sun et al., 2019) .
.428 ConvR (Jiang et al., 2019) .
.443 VR-GCN (Ye
[Table]
E XPERIMENTAL S ETUP 5. E VALUATION TASKS
In our experiments, we evaluate C OMP GCN on the below-mentioned tasks.
• Link Prediction is the task of inferring missing facts based on the known facts in Knowledge Graphs.
In our experiments, we utilize FB15k-237 (Toutanova & Chen, 2015) and WN18RR (Dettmers et al., 2018) datasets for evaluation.
Following Bordes et al.
(2013), we use ﬁltered setting for evaluation and report Mean Reciprocal Rank (MRR), Mean Rank (MR) and Hits@N. • Node Classiﬁcation is the task of predicting the labels of nodes in a graph-based on node features and their connections.
Similar to Schlichtkrull et al.
(2017), we evaluate C OMP GCN on MUTAG (Node) and AM (Ristoski & Paulheim, 2016) datasets.
• Graph Classiﬁcation , where, given a set of graphs and their corresponding labels, the goal is to learn a representation for each graph which is fed to a classiﬁer for prediction.
We evaluate onbioinformatics dataset: MUTAG (Graph) and PTC (Yanardag & Vishwanathan, 2015).
A summary statistics of the datasets used is provided in Appendix A.
5. B ASELINES
Across all tasks, we compare against the following GCN methods for relational graphs: (1)
Relational-GCN ( R-GCN ) (Schlichtkrull et al., 2017) which uses relation-speciﬁc weight matrices that are deﬁned as a linear combinations of a set of basis matrices.
(2) Directed-GCN ( D-GCN ) (Marcheggiani & Titov, 2017) has separate weight matrices for incoming edges, outgoing edges, and self-loops.
It also has relation-speciﬁc biases.
(3) Weighted-GCN ( W-GCN ) (Shang et al., 2019) assigns a learnable scalar weight to each relation and multiplies an incoming "message" by this weight.
Apart from this, we also compare with several task-speciﬁc baselines mentioned below.
Link prediction: For evaluating C OMP GCN, we compare against several non-neural and neural baselines:
TransE Bordes et al.
, DistMult (Yang et al., 2014), ComplEx (Trouillon et al., 2016), R-GCN (Schlichtkrull et al., 2017), KBGAN (Cai & Wang, 2018), ConvE (Dettmers et al., 2018), ConvKB (Nguyen et al., 2018), SACN (Shang et al., 2019), HypER (Balaževi´c et al., 2019),
RotatE (Sun et al., 2019), ConvR (Jiang et al., 2019), and VR-GCN (Ye et al., 2019).
Node and Graph Classiﬁcation:
For node classiﬁcation, following Schlichtkrull et al.
(2017), we compare with Feat (Paulheim & Fümkranz, 2012), WL (Shervashidze et al., 2011), and RDF2Vec (Ristoski & Paulheim, 2016).
Finally, for graph classiﬁcation, we evaluate against P ACHY SAN (Niepert et al., 2016), Deep Graph CNN (DGCNN)
(Zhang et al., 2018), and Graph Isomorphism
Network (GIN [Table] [Table]
Please refer to Section 6.1 for more details.
[Figure]
[Figure]
R ESULTS
In this section we attempt to answer the following questions.
Q1.
How does C OMP GCN perform on link prediction compared to existing methods?
(6.1) Q2.
What is the effect of using different GCN encoders and choice of the compositional operator in C OMP GCN on link prediction performance?
(6.1) Q3.
Does C OMP GCN scale with the number of relations in the graph? (6.3) Q4.
How does C OMP GCN perform on node and graph classiﬁcation tasks?
P ERFORMANCE C OMPARISON ON L INK P REDICTION
In this section, we evaluate the performance of C OMP GCN and the baseline methods listed in Section 5.2 on link prediction task.
The results on FB15k-237 and WN18RR datasets are presented in Table 3.
The scores of baseline methods are taken directly from the previous papers (Sun et al., 2019; Cai & Wang, 2018;
Shang et al., 2019; Balaževi´c et al., 2019; Jiang et al., 2019; Ye et al., 2019).
However, for ConvKB, we generate the results using the corrected evaluation code 2 .
Overall, we ﬁnd that C OMP GCN outperforms all the existing methods in 4 out of 5 metrics on FB15kand in 3 out of 5 metrics on WN18RR dataset.
We note that the best performing baseline RotatE uses rotation operation in complex domain.
The same operation can be utilized in a complex variant of our proposed method to improve its performance further.
We defer this as future work.
[Figure]
N u m b e r
o f R e l a t i o n s
[Figure] [Figure] [Figure]
[Figure]
[Figure]
Next, we evaluate the effect of using different GCN methods as an encoder along with a representative score function (shown in Figure 2) from each category: TransE (translational), DistMult (semantic-based) and ConvE (neural network-based).
In our results, X + M (Y) denotes that method
M is used for obtaining entity embeddings (and relation embeddings in the case of C OMP GCN) with
X as the score function as depicted in Figure 2.
Y denotes the composition operator in the case of
C OMP GCN.
We evaluate C OMP GCN on three non-parametric composition operators inspired from
TransE (Bordes et al., 2013), DistMult (Yang et al., 2014), and HolE (Nickel et al., 2016) deﬁned as
• Subtraction (Sub): φ ( e s , e r ) =
e s − e r .
• Multiplication (Mult): φ ( e s , e r ) =
e s ∗ e r .
• Circular-correlation (Corr): φ ( e s , e r ) =
e s (cid:63) e r
The overall results are summarized in Table 4.
Similar to Schlichtkrull et al.
(2017), we ﬁnd that utilizing Graph Convolutional based method as encoder gives a substantial improvement in performance for most types of score functions.
We observe that although all the baseline GCN methods lead to some degradation with TransE score function, no such behavior is observed for C OMP GCN.
On average, C OMP GCN obtains around 6 %, 4 % and 3 % relative increase in MRR with TransE,
DistMult, and ConvE objective respectively compared to the best performing baseline.
The superior performance of C OMP GCN can be attributed to the fact that it learns both entity and relation embeddings jointly thus providing more expressive power in learned representations.
Overall, we ﬁnd that
C OMP GCN with ConvE (highlighted using · ) is the best performing method for link prediction.
Effect of composition Operator: The results on link prediction with different composition operators are presented in Table 4.
We ﬁnd that with DistMult score function, multiplication operator (Mult) gives the best performance while with ConvE, circular-correlation surpasses all other operators.
Overall, we observe that more complex operators like circular-correlation outperform or perform comparably to simpler operators such as subtraction.
6. S CALABILITY OF C OMP GCN
In this section, we analyze the scalability of C OMP GCN with varying numbers of relations and basis vectors.
For analysis with changing number of relations, we create multiple subsets of
FB15k-237 dataset by retaining triples corresponding to top- m most frequent relations, where m = { 10 , 25 , 50 , 100 , 237 } .
For all the experiments, we use our best performing model (ConvE + C OMP GCN (Corr)).
Effect of Varying Relation Basis Vectors: Here, we analyze the performance of C OMP GCN on changing the number of relation basis vectors ( B ) as deﬁned in Section 4.
The results are summarized in Figure 3.
We ﬁnd that our model performance improves with the increasing number of basis [Table] vectors.
We note that with B = 100 , the performance of the model becomes comparable to the case where all relations have their individual embeddings.
In Table 4, we report the results for the best performing model across all score function with B set to 50 .
We note that the parameter-efﬁcient variant also gives a comparable performance and outperforms the baselines in all settings.
Effect of Number of Relations: Next, we report the relative performance of C OMP GCN usingrelation basis vectors ( B = 5 ) against C OMP GCN, which utilizes a separate vector for each relation in the dataset.
The results are presented in Figure 5.
Overall, we ﬁnd that across all different numbers of relations, C OMP GCN, with a limited basis, gives comparable performance to the full model.
The results show that a parameter-efﬁcient variant of C OMP GCN scales with the increasing number of relations.
Comparison with R-GCN: Here, we perform a comparison of a parameter-efﬁcient variant of
C OMP GCN ( B = 5 ) against R-GCN on different number of relations.
The results are depicted in Figure 4.
We observe that C OMP GCN with limited parameters consistently outperforms R-GCN across all settings.
Thus, C OMP GCN is parameter-efﬁcient and more effective at encoding multirelational graphs than R-GCN.
6. E VALUATION ON N ODE AND G RAPH C LASSIFICATION
In this section, we evaluate C OMP GCN on node and graph classiﬁcation tasks on datasets as described in Section 5.1.
The experimental results are presented in Table 5.
For node classiﬁcation task, we report accuracy on test split provided by Ristoski et al.
(2016), whereas for graph classiﬁcation, following Yanardag & Vishwanathan (2015) and Xu et al.
(2019), we report the average and standard deviation of validation accuracies across the 10 folds cross-validation.
Overall, we ﬁnd that C OMP GCN outperforms all the baseline methods on node classiﬁcation and gives a comparable performance on graph classiﬁcation task.
This demonstrates the effectiveness of incorporating relations using C OMP GCN over the existing GCN based models.
On node classiﬁcation, compared to the best performing baseline, we obtain an average improvement of 3 % across both datasets while on graph classiﬁcation, we obtain an improvement of 3 % on PTC dataset.
C ONCLUSION
In this paper, we proposed C OMP GCN, a novel Graph Convolutional based framework for multirelational graphs which leverages a variety of composition operators from Knowledge Graph embedding techniques to jointly embed nodes and relations in a graph.
Our method generalizes several existing multi-relational GCN methods.
Moreover, our method alleviates the problem of over-parameterization by sharing relation embeddings across layers and using basis decomposition.
Through extensive experiments on knowledge graph link prediction, node classiﬁcation, and graph classiﬁcation tasks, we showed the effectiveness of C OMP GCN over existing GCN based methods and demonstrated its scalability with increasing number of relations. A CKNOWLEDGMENTS
We thank the anonymous reviewers for their constructive comments.
This work is supported in part by the Ministry of Human Resource Development (Government of India) and Google PhD
Fellowship.
A A PPENDIX A. E VALUATION BY R ELATION C ATEGORY
In this section, we investigate the performance of C OMP GCN on link prediction for different relation categories on FB15k-237 dataset.
Following Wang et al.
(2014a); Sun et al.
(2019), based on the average number of tails per head and heads per tail, we divide the relations into four categories: one-to-one, one-to-many, many-to-one and many-to-many.
The results are summarized in Table 6.
We observe that using GCN based encoders for obtaining entity and relation embeddings helps to improve performance on all types of relations.
In the case of one-to-one relations, C OMP GCN gives an average improvement of around 10 % on MRR compared to the best performing baseline (ConvE + W-GCN).
For one-to-many, many-to-one, and many-to-many the corresponding improvements are 10 .
5 %, 7 . 5 %, and 4 %.
These results show that C OMP GCN is effective at handling both simple and complex relations.
[Table]
compared to existing methods.
Please refer to Section A
1 for more details.
A. D ATASET D ETAILS
In this section, we provide the details of the different datasets used in the experiments.
For link prediction, we use the following two datasets:
• FB15k-
(Toutanova & Chen, 2015) is a pruned version of FB15k (Bordes et al., 2013) dataset with inverse relations removed
to prevent direct
inference.
• WN18RR
(Dettmers et al., 2018), similar to FB15k-237, is a subset from WN18 (Bordes et al., 2013) dataset which is derived from WordNet (Miller, 1995).
For node classiﬁcation, similar to Schlichtkrull et al.
(2017), we evaluate on the following two datasets:
• MUTAG (Node) is a dataset from DL-Learner toolkit 4 .
It contains relationship between complex molecules and the task is to identify whether a molecule is carcinogenic or not.
• AM dataset contains relationship between different artifacts in Amsterdam Museum (de
Boer et al., 2012).
The goal is to predict the category of a given artifact based on its links and other attributes.
Finally, for graph classiﬁcation, similar to Xu et al.
(2019), we evaluate on the following datasets:
• MUTAG (Graph) Debnath et al.
(1991) is a bioinformatics dataset of 188 mutagenic aromatic and nitro compounds.
The graphs need to be categorized into two classes based on their mutagenic effect on a bacterium.
[Table]
• PTC Srinivasan et al.
1997) is a dataset consisting of 344 chemical compounds which indicate carcinogenicity of male and female rats.
The task is to label the graphs based on their carcinogenicity on rodents.
A summary statistics of all the datasets used is presented in
Table 7.
A. H YPERPARAMETERS
Here, we present the implementation details for each task used for evaluation in the paper.
For all the tasks, we used C OMP GCN build on PyTorch geometric framework (Fey & Lenssen, 2019).
Link Prediction: For evaluation, 200 -dimensional embeddings for node and relation embeddings are used.
For selecting the best model we perform a hyperparameter search using the validation data over the values listed in Table 8.
For training link prediction models, we use the standard binary cross entropy loss with label smoothing Dettmers et al.
Node
Classiﬁcation
: Following Schlichtkrull et al.
, we use 10 % training data as validation for selecting the best model for both the datasets.
We restrict the number of hidden units to 32 .
We use cross-entropy loss for training our model.
Graph Classiﬁcation: Similar to Yanardag & Vishwanathan (2015); Xu et al.
(2019), we report the mean and standard deviation of validation accuracies across the 10 folds cross-validation.
Crossentropy loss is used for training the entire model.
For obtaining the graph-level representation, we use simple averaging of embedding of all nodes as the readout function, i.e.,
[Equation]
where h v is the learned node representation for node v in the graph.
For all the experiments, training is done using Adam optimizer (Kingma & Ba, 2014) and Xavier initialization (Glorot & Bengio, 2010) is used for initializing parameters.
[Table]