A Machine Learning Approach to Predicting Continuous Tie Strengths
- URL: http://arxiv.org/abs/2101.09417v1
- Date: Sat, 23 Jan 2021 05:01:05 GMT
- Title: A Machine Learning Approach to Predicting Continuous Tie Strengths
- Authors: James Flamino, Ross DeVito, Boleslaw K. Szymanski, Omar Lizardo
- Abstract summary: Relationships between people constantly evolve, altering interpersonal behavior and defining social groups.
Relationships between nodes in social networks can be represented by a tie strength, often empirically assessed using surveys.
We propose a system that allows for the continuous approximation of relationships as they evolve over time.
- Score: 0.4014524824655105
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Relationships between people constantly evolve, altering interpersonal
behavior and defining social groups. Relationships between nodes in social
networks can be represented by a tie strength, often empirically assessed using
surveys. While this is effective for taking static snapshots of relationships,
such methods are difficult to scale to dynamic networks. In this paper, we
propose a system that allows for the continuous approximation of relationships
as they evolve over time. We evaluate this system using the NetSense study,
which provides comprehensive communication records of students at the
University of Notre Dame over the course of four years. These records are
complemented by semesterly ego network surveys, which provide discrete samples
over time of each participant's true social tie strength with others. We
develop a pair of powerful machine learning models (complemented by a suite of
baselines extracted from past works) that learn from these surveys to interpret
the communications records as signals. These signals represent dynamic tie
strengths, accurately recording the evolution of relationships between the
individuals in our social networks. With these evolving tie values, we are able
to make several empirically derived observations which we compare to past
works.
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