Time-varying Social Networks in a Graph Database – A Neo4j Use Case

C. Cattuto, A. Panisson, M. Quaggiotto, A. Averbuch, , Proc. of the GRADES2013 workshop on Graph Data-management Experiences and Systems at SIGMOD2013

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.

URL: http://dl.acm.org/citation.cfm?id=2484442

PDF: http://event.cwi.nl/grades2013/11-averbuch.pdf

BIBTEX:

@inproceedings{Cattuto:2013:TSN:2484425.2484442,
 author = {Cattuto, Ciro and Quaggiotto, Marco and Panisson, Andr{'e} and Averbuch, Alex},
 title = {Time-varying social networks in a graph database: a Neo4j use case},
 booktitle = {First International Workshop on Graph Data Management Experiences and Systems},
 series = {GRADES '13},
 year = {2013},
 isbn = {978-1-4503-2188-4},
 location = {New York, New York},
 pages = {11:1--11:6},
 articleno = {11},
 numpages = {6},
 url = {http://doi.acm.org/10.1145/2484425.2484442},
 doi = {10.1145/2484425.2484442},
 acmid = {2484442},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {Neo4j, graph database, graph databases, social networks, temporal networks, wearable sensors},
}