NDLI: Time-varying social networks in a graph database: a Neo4j use case

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Time-varying social networks in a graph database: a Neo4j use case

Content Provider	ACM Digital Library
Author	Quaggiotto, Marco Panisson, André Averbuch, Alex Cattuto, Ciro
Abstract	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.
Starting Page	1
Ending Page	6
Page Count	6
File Format	PDF
ISBN	9781450321884
DOI	10.1145/2484425.2484442
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-06-23
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Neo4j Temporal networks Social networks Graph database Graph databases Wearable sensors
Content Type	Text
Resource Type	Article

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