NFRR: A Novel Family Relationship Recognition Algorithm Based on Telecom Social Network Spectrum

Kun NIU  Haizhen JIAO  Cheng CHENG  Huiyang ZHANG  Xiao XU  

IEICE TRANSACTIONS on Information and Systems   Vol.E102-D   No.4   pp.759-767
Publication Date: 2019/04/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2018DAP0008
Type of Manuscript: Special Section PAPER (Special Section on Data Engineering and Information Management)
telecom social networks,  relational spectrum matrix,  specialized family spectrum,  

Full Text: PDF(659.2KB)>>
Buy this Article

There are different types of social ties among people, and recognizing specialized types of relationship, such as family or friend, has important significance. It can be applied to personal credit, criminal investigation, anti-terrorism and many other business scenarios. So far, some machine learning algorithms have been used to establish social relationship inferencing models, such as Decision Tree, Support Vector Machine, Naive Bayesian and so on. Although these algorithms discover family members in some context, they still suffer from low accuracy, parameter sensitive, and weak robustness. In this work, we develop a Novel Family Relationship Recognition (NFRR) algorithm on telecom dataset for identifying one's family members from its contact list. In telecom dataset, all attributes are divided into three series, temporal, spatial and behavioral. First, we discover the most probable places of residence and workplace by statistical models, then we aggregate data and select the top-ranked contacts as the user's intimate contacts. Next, we establish Relational Spectrum Matrix (RSM) of each user and its intimate contacts to form communication feature. Then we search the user's nearest neighbors in labelled training set and generate its Specialized Family Spectrum (SFS). Finally, we decide family relationship by comparing the similarity between RSM of intimate contacts and the SFS. We conduct complete experiments to exhibit effectiveness of the proposed algorithm, and experimental results also show that it has a lower complexity.