Multi-granularity Information Fusion Based Network Representation Learning

With the development of modern artificial intelligence,intelligent information services and large-scale data are constantly updated,complex information networks have been formed in various fields.Mining the effective information in the network and analyzing the nature of the network entity are conducive to various fields to grasp the market dynamics and social trends in the industry.Network representation learning which has been regarded as the basis of complex information network analysis,aims to represent the massive sparse data in the network as low-dimensional dense real-valued vectors,which is suitable for practical tasks such as link prediction and node classification.With the support of distributed technology,large-scale complex network representation learning has been developed in recent years.Existing researches mainly focus on single coarse-grained,in another word,it relies on static network topology,which rarely consider external knowledge such as additional information of the network,rich external knowledge can provided to network representation learning research and subsequent tasks theory deduction ability and interpretability.Obtaining the correlation between fine-grained external knowledge and Coarse-grained semantic information,and establishing the corresponding relationship between entities and network nodes,directly determines the network topology and properties,which greatly improves the relational reasoning and prediction capabilities of network representation learning.Therefore,it is of great significance to integrate a variety of fine-grained external information and Coarse-grained semantic information to perform network representation learning in a compatible form.In response to the above issues,this article combines the idea of multi-granularity cognition to conduct in-depth research on network representation learning.The main contents include:1.A network representation learning algorithm based on multi-granularity information fusion is proposed.First,the algorithm combine the idea of multi-granularity cognitive computing to refine the granularity of complex networks.The algorithm not only considers the topological structure of the nodes,but also include the rich content of the nodes.A fusion mechanism of the relevance of structure and attribute information is proposed,which could obtain not only a unified representation through deep learning technology,but also the potential similarity and semantic information of nodes.In the downstream node classification and link prediction verification experiments,the performance of the proposed algorithm on the real network data sets better compared with other benchmark algorithms,and it can effectively perform network representation learning.2.A multi-view attribute network representation learning algorithm based on attention mechanism is proposed.In order to solve the issue that the different importance of attribute information in network representation methods and the insufficient retention of global potential information,this algorithm proposes a multi-view attribute enhancement mechanism.First,the self-attention strategy is used to perform weighted summation of the neighbor nodes and the additional attributes of similar communities in the first-order topology according to different weights.At the same time,the attribute similarity measurement is performed on the non-neighbor nodes of each node,and a certain number of high-level nodes are not directly related continue to perform weighted attribute weighting.Finally,the two views are fused to further improve the presentation effect.Experimental results show that the algorithm effectively integrates network structure and additional attributes to improve the performance of subsequent tasks.