Summaries - Office of Research & Innovation
Back Inference on Missing Information on a Social Network
|Division||Research & Sponsored Programs|
|Department||NPS Naval Research Program|
|Sponsor||NPS Naval Research Program (Navy)|
Networks and graphs have long been a subject of study, but with an explosion in the amount of available data to describe them, machine learning (ML) methods have become a popular compliment to traditional network analysis techniques. This is particularly true when the challenge of uncertainty enters the picture, but can be overcome with the application of ML methods with large amounts of data. Understanding a social network between workers can be used for modeling social relationship factorial analysis to improve connectedness of Sailors and reduce destructive behaviors including connectedness of all Sailors, and reducing many destructive behaviors in N17, such as alcohol/drug use, suicide, and others, in addition to sexual harassment and assault. In order to analyze as accurate as possible, it is important to have an accurate social network which a model will be based on and we assume that the observed social network is built with a complete information. However, often a victim of a sexual harassment or a work harassment never reports their relations with their attackers. In a reality an observed social network is very often built with missing information.
We propose here to infer connectedness of the social networks in a community within the Navy from a data set with missing information. After correctly selecting a model to infer missing part of the social network we will analyze strength of relationships between workers. In this social network we set workers in a group as nodes and we draw edge between nodes if they have some social interaction between them. By this way we can construct several social networks, like communication networks. Then based on the social network we reconstruct we will conduct logistic regressions to see which factors contributing to each relationship.
In this project we will have several phases: (1) using Navy data or social network data available in publish, such as data repository in Stanford University https://snap.stanford.edu/data/#socnets we will apply our method to select a model to infer the missing part of a social network; (2) we will conduct analysis on the strength of the connection in each edge to predict relationship between people (i.e., nodes) in the network; and (3) with the logistic regression, we will see which factors contribute the strength in the relationship.
|Publications||Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal|
|Data||Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal|