Time-varying social networks in a graph database: a Neo4j use case by Ciro Cattuto, Marco Quaggiotto, André Panisson, and Alex Averbuch.
Representing and efficiently querying time-varying social network data is a central challenge that needs to be addressed in order to support a variety of emerging applications that leverage high-resolution records of human activities and interactions from mobile devices and wearable sensors. In order to support the needs of specific applications, as well as general tasks related to data curation, cleaning, linking, post-processing, and data analysis, data models and data stores are needed that afford efficient and scalable querying of the data. In particular, it is important to design solutions that allow rich queries that simultaneously involve the topology of the social network, temporal information on the presence and interactions of individual nodes, and node metadata. Here we introduce a data model for time-varying social network data that can be represented as a property graph in the Neo4j graph database. We use time-varying social network data collected by using wearable sensors and study the performance of real-world queries, pointing to strengths, weaknesses and challenges of the proposed approach.
A good start on modeling networks that vary based on time.
If the overhead sounds daunting, remember the graph data used here measured the proximity of actors every 20 seconds for three days.
Imagine if you added social connections between those actors, attended the same schools/conferences, co-authored papers, etc.
We are slowly loosing our reliance on simplification of data and models to make them computationally tractable.