Study on Multi-source Data Fusion Framework Based on Graph

Abstract

When analyzing various data in a given task,most of current researches only analyze single-source data and lack me-thods applied to multi-source data.But now data are becoming more abundant,therefore,this paper proposes a multi-source data fusion framework for fusing data from multiple network platforms.The data of the same platform contains text and various attri-butes,and there are also great differences in content and form among data of different platforms.Most existing network information mining methods only use part of the data in the same platform for analysis,and even ignore the interaction between the data of different platforms.Therefore,this paper proposes a data fusion framework,which can not only use more features of the same platform to improve the performance of a single platform,but also fuse the data features of different platforms to complement each other,thereby improving the performance of multiple platforms.This paper uses the task of event classification,and the abundant features effectively improve the F1 value,which verifies the effectiveness of the proposed multi-source data framework.

Keywords

• fusion representation|multi-source|graph fusion