The Network Analysis of Amazon Co-purchasing was done to produce high profits for vendors. It is a smart technique to identify the sale of those products, which eventually leads to the co-purchase of other related products. Amazon used its customer's profile, buying history, and browsing history to examine their purchasing behavior. The project was done using Gephi Software to analyze the data.

1. Amazon
Co-
Purchasing
Network
Team 8
Disha Bhatnagar
Padma Shneha
Swati Lathwal
University of Central Florida

2. Summary
Purpose:
• The purpose of this network analysis is
to educate ourselves about the buying
pattern of Amazon online store users.
• We will be able to predict the central
entities and group of such entities
from the network to recommend
how promoting some of the co-
purchased items may increase the
sales of the selected items

3. Data Laboratory

4. Original Graph
• This is the network of items on Amazon that have been
mentioned by amazon’s “people who bought X also bought Y”
function.
• This view is very cluttered. The nodes are too close to
each other.
• Node are the products and edges mean co purchase.
• The network has no multiple edges.
• If an edge starts from source node to sink node , then the
product that denotes the source node, is independently
bought and item at sink node is co-purchased.
• The frequency of co purchase is not available . Hence , the
graph is unweighted.

5. Frutchermann
Reingold
• It is a force directed algorithm so all the
nodes are attracted towards the center.
• Degree of a node in this network depicts
the extent of an item’s Co – purchasability.
• Red color nodes are the nodes with extent
of co-purchase degree as one . Means they are
only co purchased with on item.
• Green are the ones which are co purchased
few times .

6. Yifan hu Layout
• In this network , the red color
nodes are the nodes with maximum
degree. Around 10 or 11 , which
means that there are few such
nodes with that degree and most
nodes are the ones with degree one
to five.
• A node with higher out degree
shows that when that item was
bought , many items were co
purchased , which could be the
accessories of the original product.
• A node with higher indegree
shows that the item is co purchased
with many other items.

7. Open Ord
• The bigger nodes are the nodes which are
connected to a lot of other nodes.
• The big clusters are few and mostly there are
small clusters which means higher number of co-
purchased patterns are few .
• The gap shows that there are no edges between
the clusters. so there’s no co purchasing link
between them.
• In this network , there can be many such incidental
nodes which are bought together but there isn’t any
pattern of co purchasing between them.

8. Modularity
• Most popular products form
communities within
themselves.
• Modularity: 0.995
• Number of Communities:
1135

9. Clustering
Coefficient
• Popular products tend to be
co-purchased with other
popular products forming
clusters
• Average Clustering
Coefficient: 0.051

10. Average Degree
• Degree of a node represents an
item's co-purchasability.
• The significance of in-degree and
out-degree of a node would be
different.
• A node with higher out-degree
means that when an item is
bought many other items are co-
purchased.
• A node with higher in-degree
means that the item is co-
purchased with many other
items.

11. Eigenvector Centrality
• It is the influence a node has on a
network.
• Popular products tend to have a
higher eigenvector centrality.
• Purple= 0.94, with highest
eigenvector centrality.

12. Closeness Centrality
• Node with highest centrality is the
closest to all other node
• Green nodes represent highest
closeness centrality
• Higher closeness centrality means
a popular product will be bought
with many other products

13. Filters : Giant component

14. Sub-filter : Degree Range

15. Filter : In Degree Range

16. Filter : Out Degree Range

17. Filter : Out Degree Range

18. Overall findings:
The Directed Graph of this Network depicts that
there are nodes with large number of out-
degree and less number of in-degree , which
means that when any item was bought , many
items were co purchased.
The Network turned out to be a Bus Network if
we remove the highest degree node the entire
network will fall apart.

19. Overall findings
• Openord Layout helped us know that there
are incidental purchases in the graph which is
why most of the nodes have less number of
degrees in the network.

20. Contemplation
• We have analyzed the data to
understand the significance of high in
degree and out degree nodes and how
such products with high co purchasing
degree can help us know the patterns of
users who co purchase products from
amazon.

21. Contemplation cont..
• These patterns can be very useful in marketing
and customising ads according to the products
so the sales can go up.There are some
incidental
• shopping cases also but if we look at the
frequency of the co purchase then data can be
analysed
• better to help make the marketing of co
purchase products which can definitely affect
the sales of