Abstract
Graph Neural Networks (GNNs) are powerful tools that are nowadays reaching state of the art performances in a plethora of different tasks such as node classification, link prediction and graph classification. A challenging aspect in this context is to redefine basic deep learning operations, such as convolution, on graph-like structures, where nodes generally have unordered neighborhoods of varying size. State-of-the-art GNN approaches such as Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs) work on monoplex networks only, i.e., on networks modeling a single type of relation among an homogeneous set of nodes. The aim of this work is to generalize such approaches by proposing a GNN framework for representation learning and semi-supervised classification in multilayer networks with attributed entities, and arbitrary number of layers and intra-layer and inter-layer connections between nodes. We instantiate our framework with two new formulations of GAT and GCN models, namely ML-GCN and ML-GAT, specifically devised for general, attributed multilayer networks. The proposed approaches are evaluated on an entity classification task on nine widely used real-world network datasets coming from different domains and with different structural characteristics. Results show that both our proposed ML-GAT and ML-GCN methods provide effective and efficient solutions to the problem of entity classification in multilayer attributed networks, being faster to learn and offering better accuracy than the competitors. Furthermore, results show how our methods are able to take advantage of the presence of real attributes for the entities, in addition to arbitrary inter-layer connections between the nodes in the various layers.
Highlights
The topic of graph representation learning and its impact on related analysis tasks in network data has attracted great attention over the past few years, leading to one of the fastest growing subfields of research in deep learning
The graph convolutional network (GCN) model proposed by Kipf and Welling (2017), where convolution on graphs is carried out by aggregating the values of each node’s features along with its neighbors’ features, paved the way for the development of further methods based on GCNs, for end-to-end learning tasks or focusing on the low-dimensional embedding generation; in the latter case, for instance, the graph autoencoder (GAE) model (Kipf and Welling 2016) is one of the earliest approaches for unsupervised learning, clustering and link prediction on graphs based on GCNs
Unlike existing Graph Neural Network (GNN) approaches for multiplex or multirelational graphs, we propose to aggregate topological neighborhood information from different layers directly into the propagation rule of the GNN component, i.e., during its forward learning phase, in order to make the embedding of an entity in a particular layer depending on both its neighbors in that layer and on its neighbors located in other layers where the entity occurs
Summary
The topic of graph representation learning and its impact on related analysis tasks in network data has attracted great attention over the past few years, leading to one of the fastest growing subfields of research in deep learning. Network following a transductive semi-supervised learning approach In this context, class labels are known at training time only for a relatively small amount of nodes in the multilayer network, while all available structural information and node attributes can be exploited for learning, and the goal is to predict the labels of the unlabeled nodes. 2. We propose a representation learning and node classification framework based on GNN models and designed for arbitrary multilayer attributed networks. 4. Our designed GNN components in the proposed framework are able to incorporate external information associated with the multilayer network, in the form of attributes that can be available at entity-level or at node-level for each particular layer of the input network. Our methods were compared with two recently proposed methods for multirelational networks based on a GAT model, named GrAMME-SG and GrAMME-Fusion (Shanthamallu et al 2020): our methods are able to achieve accuracy as good as or better than the competitors (up to 13% of accuracy improvement), while outperforming them in terms of efficiency (with a training time which is two orders of magnitude lower than GrAMME methods in most cases)
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