Research On Multi-granular Network Representation Learning

Network representation learning,also called network embedding,aiming to learn low dimensional vectors for nodes while preserving essential properties of the network,such as structural similarity,attribute similarity,etc.The low-dimensional vector of the node can be used as the input of the machine learning algorithm and applied to a lot of downstream tasks,such as node classification and link prediction,benefits plenty of practical applications.In recent years,network representation learning has attracted great attention from researchers,making great achievements on single-granularity network embedding.In the real world,many networks present multi-granularity structure,which has been shown to contribute to the completion of network analysis tasks.Therefore,how to quickly obtain the multi-granularity structure of the network and preserve the multi-granularity structure of the network is a meaningful and tough task in the field of network representation learning.Most existing methods are based on single-granularity,which learn representations from local structure of nodes(such as first-order proximity,second-order proximity,and community structure)or single-granularity attribute information.Multi-granularity network representation learning has been least studied.In this dissertation,we combine granular computing to research multi-granularity network representation learning methods.Constructing a multi-granularity network by granulation model,which can simplify the network scale.And then learn on the coarsest network to get the approximate solution of the original network.Finally,the approximate solution is refined from coarse to fine,and the final solution of the original network is obtained.However,the real-world network not only has a network structure,but also has rich node attribute information and many networks present multi-granularity structure.This work further studies the representation learning method of multi-granularity attribute network by fusing structure and node attribute information.The main work of this dissertation is as follows:(1)We propose a hierarchical structure network embedding framework(HSNE),which not only preserves the local structure,but also preserves the multi-granularity structure.First,construct different granules according to the relationship between the network structure,construct the granular layer according to the edges,thereby construct a multi-granularity network with a gradually decreasing network scale,and then learn on the coarsest network to get the approximate solution of the original network.Finally,according to the relationship between different granular layers,the approximate solution is refined from coarse to fine,and the final solution of the original network is obtained.Experimental results show that the proposed method can preserve the multi-granularity structure well.(2)We propose a hierarchical attributed network embedding framework HANE to preserve the multi-granularity attributed information.Specifically,for an attributed network,HANE designs a granulation method that combines structure and node attributes.After using any unsupervised network embedding method(such as structure-only network embedding or attributed network embedding)to learn node representations of the coarsest network.HANE refines the nodes representations of the hierarchical attributed network from coarse to fine.HANE improves the speed of network representation learning while maintaining its performance and the representation learning method of the coarsest network is flexible.We conduct extensive evaluations for the proposed framework HANE on six datasets and two benchmark applications.Experimental results demonstrate that HANE achieves significant improvements compared to the state-of-the-art network embedding methods in efficiency and effectiveness.