A multi-granular network representation learning method
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ORIGINAL PAPER
A multi-granular network representation learning method Jie Chen1,2 • Ziwei Du1,2 • Xian Sun1,2 • Shu Zhao1,2
•
Yanping Zhang1,2
Received: 28 May 2019 / Accepted: 16 August 2019 Ó Springer Nature Switzerland AG 2019
Abstract Granular computing (GrC) as a problem-solving concept and new information processing paradigm is deeply rooted in human thinking, which has attracted many researchers to study it theoretically, and has gradually applied to data-driven problems. Network embedding, as known as network representation learning, aiming to map nodes in network into a lowdimensional representation, is a data-driven problem. Most existing methods are based on a single granular, which learn representations from local structure of nodes. But global structure is important information on the network and has been proven to facilitate several network analysis tasks. Therefore, how to introduce GrC into network embedding to obtain a multi-granular network representation that preserves the global and local structure of nodes is a meaningful and tough task. In this paper, we introduce Quotient Space Theory, one of the GrC theories into network embedding and propose a MultiGranular Network Representation Learning method based on Quotient Space Theory (MG_NRL, for short), which can preserve global and local structure at different granularities. Firstly, we granulate the network repeatedly to obtain a multigranular network. Secondly, the embedding of the coarsest network is computed using any existing embedding method. Finally, the network representation of each granular layer is learned by recursively refining method from the coarsest network to original network. Experimental results on multi-label classification task demonstrate that MG_NRL significantly outperforms other state-of-the-art methods. Keywords Granular computing Quotient space theory Network embedding Multi-granular Multi-label classification
1 Introduction Granular computing (GrC) is originally called information granularity or information granulation related to fuzzy sets research (Zadeh 1997). It is generally accepted that 1997 is regarded as the birth of GrC. In recent years, the basic notions and principles of GrC occurred under various forms in many disciplines and fields (Zadeh 1997; Yao 2001). We have witnessed a rapid development and fast growing interest in this topic (Bargiela and Pedrycz 2008; Qian et al. 2010; Wang 2017; Zhao et al. 2017; Wang et al. 2017a). In the existing Granular Computing theories, it is
& Shu Zhao [email protected] 1
Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei 230601, Anhui Province, China
2
School of Computer Science and Technology, Anhui University, Hefei 230601, Anhui, People’s Republic of China
well accepted that the theories of fuzzy sets (Zadeh 1965) and rough sets (Pawlak 1982) are two primary contributions that have occurred from the emergence of GrC (Lin 1999, 2003). Insp
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