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Title: Composition-based Multi-Relational Graph Convolutional Networks

Paper content:
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 CompGCN, a novel Graph Convolutional framework which jointly embeds both nodes and relations in a relational graph.
CompGCN 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 CompGCN available to foster reproducible research.
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; Shang et al., 2019) , natural language processing (Marcheggiani & Titov, 2017; Vashishth et al., 2018a; b; , drug discovery (Ramsundar et al., 2019) , crystal property prediction (Sanyal et al., 2018) , and natural sciences (Fout et al., 2017) .
However, most of the existing research on GCNs (Kipf & Welling, 2016; Hamilton et al., 2017; Veličković 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 such as link prediction which require relation embedding vectors.
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) 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
1. We propose COMPGCN, 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.
2. We demonstrate that COMPGCN 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).
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 COMPGCN and datasets used in the paper have been made available at http: //github.com/malllabiisc/CompGCN.
In this paper, we proposed COMPGCN, 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.
, 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, COMPGCN 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 COMPGCN is effective at handling both simple and complex relations.
Table 6 : Results on link prediction by relation category on FB15k-237 dataset.
Following Wang et al. (2014a) , the relations are divided into four categories: one-to-one (1-1), one-to-many (1-N), manyto-one (N-1), and many-to-many (N-N).
We find that COMPGCN helps to improve performance on all types of relations compared to existing methods.
Please refer to Section A.1 for more details.

assistant: text: The authors develop GCN on multi-relational graphs and propose CompGCN, which leverages insights from knowledge graph embeddings and learns node and relation representations to alleviate the problem of over-parameterization.