Are social network analysis and graph analytics the same thing? If not, what are the differences? Is social network analysis perhaps a subset of graph analytics?

Are they just modern extensions of graph theory which have become relevant due to modern types of data and data analysis?

What role does software like NetworkX and neo4j play?


2 Answers 2


No, social network analysis and graph analytics are not the same things.

Graph analytics are a general set of tools to understand graph structure (e.g., nodes and edges). Social network analysis is more specific to understanding the relationships within a social structure.

Social networks are often formulated as graphs but not always. If a social network is modeled as a graph, then a subset of graph tools can be applied. One of the biggest disconnects is that graph analytics tends to be theoretical, whereas social network analytics is more often empirical.

NetworkX and neo4j are a specific set of empirical tools to analyze graphs.


Social network analysis (SNA) investigates social structures through the use of networks and graph theory.

Graph Algorithms or Graph Analytics are analytic tools used to determine the strength and direction of relationships between objects in a graph. The focus of graph analytics is on the pairwise relationship between two objects at a time and the structural characteristics of the graph as a whole.

Neo4j is a graph database management system developed by Neo4j, Inc. Described by its developers as an ACID-compliant transactional database with native graph storage and processing. Neo4j facilitates personal data storage and management: it allows you to track where private information is stored and which systems, applications, and users access it. The graph data model helps visualize personal data and allows for data analysis and pattern detection.

NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. It provides:

  • tools for the study of the structure and dynamics of social, biological, and infrastructure networks;

  • a standard programming interface and graph implementation that is suitable for many applications;

  • a rapid development environment for collaborative, multidisciplinary projects;

  • an interface to existing numerical algorithms and code written in C, C++, and FORTRAN; and

  • the ability to painlessly work with large nonstandard data sets.

With NetworkX you can load and store networks in standard and nonstandard data formats, generate many types of random and classic networks, analyze network structure, build network models, design new network algorithms, draw networks, and much more.


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