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Inferring directions of undirected social ties
Zhang J., Wang C., Wang J., Yu J., Chen J., Wang C. IEEE Transactions on Knowledge and Data Engineering28 (12):3276-3292,2016.Type:Article
Date Reviewed: Apr 26 2017

With the advent of social networks, there are many opportunities available for researchers to research and analyze social ties. This is especially true with social networking sites like LinkedIn. In this paper, the authors aim to analyze the directionality of the social ties associated with individuals in a social network.

The interesting aspect of this work is that it introduces the concept of “directionality of relationships” in social networks. Specifically, the authors consider that social networks consist of proposer (passive friend)responder (active friend) directed relations. Accordingly, the authors analyze the directionality of real-world social networks and identify four consistency patterns: degree consistency, triad status consistency, similarity consistency, and collaborative consistency. They describe these consistencies as follows: i) degree consistency for uv has a lower in-degree but higher out-degree for v; ii) the triad status consistency represents a triangular relationship; iii) the similarity consistency dwells on passive and active friends, where a node may be more related to another node’s passive friends than active friends; and iv) collaborative consistency indicates nodes having similar active friends. Based on these distinct relationships, the authors present mathematical formulas for calculating consistency values.

In the paper, social relationships are represented using mathematical vectors, providing information about their directionality. The authors propose a family of approaches for discovering the direction of relationships of social ties, based on the topology of the social network. Through experimental evaluation, the authors show that the proposed approaches can effectively uncover the direction of relationships in social graphs. The results of the performed analysis are nicely presented using graphs and tables.

Overall, this is a very well-written paper that advances the science of data mining and social network analysis.

Reviewer:  Varadraj Gurupur Review #: CR145224 (1707-0480)
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Social Networking (H.3.4 ... )
 
 
Data Mining (H.2.8 ... )
 
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