Network Analysis on #Badminton tweets on twitter

  1. The tidyverse :- The tidyverse is an opinionated collection of R packages designed for data science.
  2. igraph :- It was used to plot the graph for analyzing the built network.
  3. CINNA :- It was used to check the giant component in the network.
  4. ERGM :- Exponential family random graph models (ERGM)are build to explain the global structure of a network while allowing inference on tie prediction on a micro level.
  5. Tidygraph :- It is used to provide tidy framework to manipulate different data frame.( as in the context used).
  1. Tidygraph
  2. Igraph
  3. Ggraph
  4. Dplyr
  5. CINNA
  • The graph is an undirected graph. If a screen_name mentions the other screen_name, it means they have some relation between them. Either they can be team partners or opponents. It is a mutual relationship; hence graph is undirected.
  • The graph is not a connected graph. Connected graphs are the graphs where we can make a walk from one vertex to another.
  • The network has a low edge density. It is sparse. Sparse network is the network where number of links are close to minimum number of possible links. The edge density of the network is 0.0013
  • The network has many giant components but no single giant component that can cover most of the networks.
  • If we observe a single giant component, it contains many nodes but still not cover the whole network. This means that there are many nodes in the network that may not contain even a single link.
  • Motifs are NA. Motifs are the subgraphs that repeat themselves in the network. Since, the sub graphs are not connected, so it is NA.
  • Neighbours of the nodes are the adjacent node of the node. There are more than 1 neighbour for many nodes (even 5,6 in many cases). However, there are also lot of cases where neighbour of the node is only 1.
  • Degree which is the number of links a node has is variable in each case.
  • The average degree of the network is 2.13, this means on an average, each node has two links.




Masters in Applied data science, University of Canterbury, New Zealand. Data scientist who loves to play with the data and make sense from it.

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Aki Kapoor

Aki Kapoor

Masters in Applied data science, University of Canterbury, New Zealand. Data scientist who loves to play with the data and make sense from it.

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