BIA 658

1. Transitivity and Reciprocity
of Interpersonal Trust
on Epinions.com
By: Jake Fagen, Nikhil Kumar, Spencer
Deuidicibus, Abhishek Desai

2. Introduction
▪ Epinions.com was a consumer review website founded in 1999.
▪ The website had a massive amount of goods/services/businesses
that consumers could leave reviews on.
▪ Once reviews are published they could be voted on by other users to
determine the visibility of said opinion.
▪ In May 2018 Epinions was shutdown.

3. Purpose Statement
▪ To determine how users trust one another on Epinions.com
▪ Many users may leave reviews but they can have biases or
contain false information
▪ Is trust reciprocal on Epinions.com and does trust travel
through communities of the network or only user to user?

4. Dataset Explanation
▪ The dataset comes from the website Epinions. Epinions is an online social network of general
consumer reviews. Users of the website can choose to “trust” other users on the website.
These trust relationships then form the web of trust which is combined with review rating to
determine which reviews to show the user.
▪ The edge in the network is trust, with the nodes being individual users of the website.
▪ It is worth noting that due to the volume of the network, visualizations have been created
through a randomly selected set of edges while some calculations had to have been done
differently (such as transitivity and reciprocity) and some being outright impossible
(betweenness/closeness centrality, ERGM). This is due to the random selections not being
compatible with these forms of analysis, and the main dataset requires billions of
calculations for each one, which is not feasible.

7. Descriptive Statistics

9. Analytical Approach
By taking the edgelist, incrementing it’s node values by 1 (to prevent errors with a
node value of 0), and then turning it into a graph we can get the following values
for the entire dataset:
▪ Transitivity of 0.0657 and Graph Density of 0.000008; very low, shows that
nodes are extremely spread out
▪ Reciprocity of 0.405; fairly high, 40.5% of connections go both ways
▪ Network centralization degree of 0.0202, Eigenvector Centralization of 0.99
▪ Node with highest degree is 0 with 3079, and node with highest Eigenvector
centralization is also 0 with 1.
ERGM and closeness/betweenness centralization impossible due to size of
network, functions will not work properly unless non-randomized set of nodes are
selected at which point results are useless.

10. Key Findings
From the Analytical Approach, we found that the network is extremely
spread out with the only exception being a large connection of nodes
centered around node 0. This means that overall, trust does not travel
through communities and only on a person by person basis with the
exception being node 0, which has formed a sort of miniature community
around it. In addition, with a significant reciprocal value of 40.5%, there
is a good chance if Person A trusts Person B, it will also go the other way
around.

11. Interpretations of Findings
The key takeaways from our findings are that:
▪ Trust between users is commonly reciprocal in the Epinions network which means
that users that are trusted by another user usually trust them back. This makes
sense as users may choose to trust those who have similar reviews and opinions on
the website.
▪ Node 0 is a broker within a network with no other important nodes. This creates an
artificial community of people who trust this actor, and who are heavily influenced
by them but not necessarily trusting of each other (Ex: Influencer and fans), and
implies other smaller “communities” simulate this behavior.
Recommendations:
▪ People connected to same broker most likely have similar/aligned interests, which
can form actual communities, so by utilizing the limited number of brokers within
network (e.g. Node 0) we can create trust (edges) among it’s direct connections
▪ Prevents widespread disruption from a broker going rogue by transferring
reciprocality across an entire community rather than on a person by person basis.

12. THANK YOU
Stevens Institute of Technology
1 Castle Point Terrace, Hoboken, NJ 07030