egonetworks – Python package for Ego network structural analysis¶
This package contains classes and functions for the structural analysis of ego networks.
An ego network is a simple model that represents a social network from the point of view of an individual. This model considers only the social relationships that a focal node in the network (termed ego) maintains with other nodes (termed alters). Note that the model supported by this package does not consider relationships between alters (aka mutual friendship relationships), but only the star topology of alters connected to the ego. This ego network model is known as “Dunbar’s ego network”. See [1] and [2] for additional information about ego networks and ego network analysis.
The package offers several methods for the static and dynamic analysis of ego networks. For example, the package provides a function to obtain the “social circles” of the ego network, which are discrete groups of alters at similar level of tie strength with the ego. In addition, there are functions to analyse the dynamic evolution of ego networks and to calculate their stability over time. These functions are useful, for example, for the analysis of human behaviour in different social environments as well as to identify particularly active, dynamic or sociable people from their communication traces.
The package offers specialised classes for building and studying ego networks from Twitter data and from coauthorship or collaboration networks (i.e. networks where the ego is an author and the alters are people with whom he or she coauthored publications).
These are the main modules of the package:
- egonetworks.core – Basic ego network classes and functions
- egonetworks.twitter – Twitter ego networks
- egonetworks.coauthorship – Coauthorship ego networks
- egonetworks.generic – Generic classes and functions
- egonetworks.similarity – similarity and dissimilarity measures
- egonetworks.error – Exceptions
References¶
| [1] | R.I.M. Dunbar, V. Arnaboldi, M. Conti, A. Passarella, “The Structure of Online Social Networks Mirrors Those in the Offline World”, Social Networks, Vol. 43, October 2015, Pages 39-47 |
| [2] |
|