Abstract
Network representation learning aims to map nodes in the network into low-dimensional dense vectors, which can be widely used to solve the network analysis tasks. Existing methods mainly focus on single-layer homogeneous networks. However, many real-world networks consist of multiple types of nodes and edges, which are called multilayer networks. The problem of how to capture node information and use multi-type relational information is a major challenge of multilayer network representation learning. To address this problem, we propose a method of random walk of multiple information, called IFMNE, to efficiently preserve and learn node information and multi-type relational information into a unified space. This method combines node structure information with network topology information to obtain the node random walk sequence, and trains the node walk sequence on the neural network model. Experimental results are performed on five real multilayer networks, and the embedding vectors were evaluated by link prediction task. The accuracy was significantly improved on the basis of low time complexity compared with the baseline methods.
Highlights
Network data can naturally express the relation between objects, which are ubiquitous in our daily life and work, such as infrastructure networks, social networks and so on
We demonstrate that the node sequence obtained by random walk is guided to be applied on the Skip-Gram model by fusing node structure information and network topology information
We propose a randomwalk multilayer network representation learning method that fuses node structure information and network topology information
Summary
Network data can naturally express the relation between objects, which are ubiquitous in our daily life and work, such as infrastructure networks, social networks and so on. The second category of representation learning method of the multilayer network is based on the random walk strategy. This category of method uses the random walk within the layer to search in each layer of the network to learn the individual embedding of nodes of different layers, such as OhmNet [7] and MNE [8], etc. To solve the above problems, this paper proposes a multilayer network presentation learning method IFMNE(base on information fusion in multilayer network embedding) that integrates node structure information and network topology information. By introducing node structure information and combining network topology information in PMEN method, our model can learn more appropriate representation from multilayer networks.
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