  In recent years, with the growing number of online social networks, these
networks have become one of the best markets for advertising and commerce, so
studying these networks is very important. Forecasting new edges in online
social networks can give us a better understanding of the growth of these
networks. There have been many studies of link prediction in the field of
engineering and humanities. Scientists attribute the existence of a new
relationship between two individuals for two reasons: 1) Proximity to the graph
(structure) 2) Similar properties of the two individuals (Homophile law).
However, studying the impact of the two approaches working together to create
new edges remains an open problem. Similarity metrics can also be divided into
two categories; Neighborhood-based and path-based. So far, the above two
theoretical approaches (proximity and homophile) have not been found together
in the neighborhood-based metrics. In this paper, we first attempt to provide a
solution to determine importance of the proximity to the graph and similar
features in the connectivity of the graphs. Then obtained weights are assigned
to both proximity and homophile. Then the best similarity metric in each
approach are obtained. Finally, the selected metric of homophily similarity and
structural similarity are combined with the obtained weights. The results of
this study were evaluated on two datasets; Zanjan University Graduate School of
Social Sciences and Pokec online Social Network. The first data set was
collected for this study and then the questionnaires and data collection
methods were filled out. Since this dataset is one of the few Iranian datasets
that has been compiled with its users' specifications, it can be of great
value. In this paper, we have been able to increase the accuracy of
Neighborhood-based similarity metric by using two proximity in graph and
homophily approaches.
