A key problem in online social networks is the identification of user characteristics and the analysis of how these are reflected in the graph structure evolution. The basis to tackle this issue is user similarity measures. In this paper, we propose a novel user similarity measure for online social networks, which combines both network and profile similarity. Since user profile data could be missing proposed measure is complemented by a technique to infer missing items from profile of the user’s contacts. The second main contribution of this paper is an extensive performance evaluation of the proposed measures with respect to some of the most relevant measures already proposed in the literature. The performance evaluation study has been conducted on a variety of data sets (i.e., Facebook, Youtube, Epinions and DBLP data sets) to see how different scenarios and graph characteristics affect the measures’ performance.
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User inputs with Facebook auto-completion and aggregation was imposed just in recent years. Before this, users could enter unstructured texts.
where |Sb| = 1 in case of non-structured items, like gender.
This is done by creating the graph using only edges established before time T.
In the table, precision is the correct inferrals over all inferrals, i.e., precision = #correct inferrals/#all inferrals.
If there is no profile information, a generic list of celebrity accounts are offered.
Related videos list of a video can not be determined by the user who uploaded the video: http://support.google.com/youtube/bin/answer.py?hl=en&answer=92651
In Graph theory, triadic closure is used to refer to predictions for such graphs where two pairs of nodes have strong ties, and a weak tie among them is expected, i.e., the dashed line already exists or it is expected to be formed in future. Our experiments try to predict this edge.
Values are rounded to two decimal points.
This problem is known as the cold start problem in recommender systems.
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The research presented in this paper was partially funded by a Google Research Award.
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Akcora, C.G., Carminati, B. & Ferrari, E. User similarities on social networks. Soc. Netw. Anal. Min. 3, 475–495 (2013). https://doi.org/10.1007/s13278-012-0090-8
- Profile Similarity
- Target User
- Network Similarity
- Profile Information
- Social Graph