Gated Multi-channel Network Embedding for Large-scale Mobile App Clustering

• Yeo-Chan Yoon,
• Soo Kyun Kim,

Vol. 17, No. 6, June 30, 2023
10.3837/tiis.2023.06.005, Download Paper (Free):

• Mobile App Clustering
• Network embedding
• Nodes Clustering
• Deep Learning

Abstract

This paper studies the task of embedding nodes with multiple graphs representing multiple information channels, which is useful in a large volume of network clustering tasks. By learning a node using multiple graphs, various characteristics of the node can be represented and embedded stably. Existing studies using multi-channel networks have been conducted by integrating heterogeneous graphs or limiting common nodes appearing in multiple graphs to have similar embeddings. Although these methods effectively represent nodes, it also has limitations by assuming that all networks provide the same amount of information. This paper proposes a method to overcome these limitations; The proposed method gives different weights according to the source graph when embedding nodes; the characteristics of the graph with more important information can be reflected more in the node. To this end, a novel method incorporating a multi-channel gate layer is proposed to weigh more important channels and ignore unnecessary data to embed a node with multiple graphs. Empirical experiments demonstrate the effectiveness of the proposed multi-channel-based embedding methods.

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