TY - GEN
T1 - Analysis of investment relationships between companies and organizations based on knowledge graph
AU - Hu, Xiaobo
AU - Tang, Xinhuai
AU - Tang, Feilong
N1 - Publisher Copyright:
© Springer International Publishing AG 2018.
PY - 2017
Y1 - 2017
N2 - Investment relationship investigation is an important part of financial investigation. However, extraction and analysis of investment relationships becomes more difficult since the increment of the number of enterprises, complexity of company structure, and diversity of investment. This paper uses a knowledge graph of companies to find whether investment relationships between two companies and common investors among target companies exist. In this paper, firstly, find investment relationships between two companies by path finding algorithm to check whether investment paths exist between them. Secondly, use Depth First Search algorithm to check whether common investors exist. Thirdly, knowledge graph contains a great number of companies and relationships between them so that it is inefficient and difficult to compute directly from all the companies nodes. To overcome this problem, this paper will use graph compression to decrease the number of nodes to compute.
AB - Investment relationship investigation is an important part of financial investigation. However, extraction and analysis of investment relationships becomes more difficult since the increment of the number of enterprises, complexity of company structure, and diversity of investment. This paper uses a knowledge graph of companies to find whether investment relationships between two companies and common investors among target companies exist. In this paper, firstly, find investment relationships between two companies by path finding algorithm to check whether investment paths exist between them. Secondly, use Depth First Search algorithm to check whether common investors exist. Thirdly, knowledge graph contains a great number of companies and relationships between them so that it is inefficient and difficult to compute directly from all the companies nodes. To overcome this problem, this paper will use graph compression to decrease the number of nodes to compute.
UR - https://www.scopus.com/pages/publications/85026421962
U2 - 10.1007/978-3-319-61542-4_20
DO - 10.1007/978-3-319-61542-4_20
M3 - 会议稿件
AN - SCOPUS:85026421962
SN - 9783319615417
T3 - Advances in Intelligent Systems and Computing
SP - 208
EP - 218
BT - Innovative Mobile and Internet Services in Ubiquitous Computing - Proceedings of the 11th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, IMIS 2017
A2 - Enokido, Tomoya
A2 - Barolli, Leonard
PB - Springer Verlag
T2 - 11th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, IMIS 2017
Y2 - 10 July 2017 through 12 July 2017
ER -