1) The document discusses predicting the strength of ties between individuals in online social networks using measures of their ego network structures. It analyzes how factors like degree centrality, betweenness centrality, and dispersion within ego networks correlate with perceived tie strength. 2) An empirical study collected data on Facebook ego networks and tie strength perceptions from 41 respondents. Network measures like betweenness in ego networks were highly predictive of close ties. 3) The findings suggest that ego network analyses can reveal close friendships even without direct disclosure of that information, with implications for data privacy, understanding peer influence, and analyzing other networked data.

1. Predicting Tie Strength With Ego Network Structures
Simon Stolz*, Christian Schlereth
Public slides, available via slideshare.com
Forthcoming at Journal of Interactive Marketing
(* corresponding author)

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Introduction
Identification of perceived strong ties
Images from: Author Facebook page, nounproject.com
Online social networks host large and steadily growing lists of friends and acquaintances….
… but who are the few closest ones?

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The Two Approaches to Tie Strength
Tie strength assessments are important - but different approaches exist
Social Advertisements Word of Mouth (Offline)Viral Marketing (eWOM)
E.g., Bakshy et al. (2012) E.g., Hayes, King, Ramirez (2016),
Aral and Walker (2014)
E.g., Brown and Reingnen (1987),
Chu and Kim (2011)
“Revealed Preference” Measures ≠ Perceived Tie Strength
See Bapna et al. (2017),
Wiese et al. (2015)
(Similarity, Interactions, and Networks)
Online Offline
This question matters! Various papers show that tie strength is a fundamental metric in marketing
Two distinct views have emerged:
Online studies use “revealed preference” measures, whereas offline studies were able to ask for “perceptions”
Prior “revealed preference” measures have limitations and are different to the actual perceived tie strength

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Tie Strength Prediction Models
A high-level overview of how “revealed preference” predictors
Paper (Respondents)
Ties
Predictors
Similarity
Interaction
Network
Common
contacts
Full
network
measures
Ego
network
bridging
positions
Our paper
(41)
18,541
✔ ✔ ✔ - ✔
Rotabi et al. (2017)
(-)
Undisclosed
- - ✔ ✔ -
Backstrom and Kleinberg (2014)
(-)
~ 379m
- ✔ ✔ - ✔
Jones et al. (2013)
(789)
1,587
✔ ✔ - - -
Arnaboldi, Guazzini and
Passarella (2013)
(30)
7,103
✔ ✔ - - -
Gilbert and Karahalios (2009)
(35)
2,184
✔ ✔ ✔ - -
Kahanda and Neville (2009)
(-)
8,766
✔ ✔ ✔ ✔ -
These papers all seek to predict the individual
perceptions of tie strength and build on predictors
from the three pillars:
• Similarity
• Interactions
• Network
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Approach
Social network analysis and the ego network perspective
While social network analysis (SNA) commonly look at full, sociocentric, or sampled networks,
we propose to change the perspective and look into the microcosm of “ego networks”
The ego network contains all first-degree friends of a person (the ego).
For simplicity let’s look at the following synthetic network that contains a family and university friends:

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Motivation of Structural Embeddedness
Degree in ego networks
Degree Centrality (hereinafter Degree) reflects, for example for node C it simply reflects.
Alter D has 3 common contacts with ego, hence the degree of alter C is 3 (when excluding ego)
Already Granovetter (1973) argues that strong ties spend large amounts of time with each other. Hence, if
two friends spend a lot of time together, they will also have the chance to meet another person and be
connected.

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Implemented in
Motivation of Bridging Positions
Betweenness and dispersion
Betweenness Centrality (hereinafter Betweenness) reflects the probability of a node to lie on one of the shortest paths in the network
Alter D lies on 12 of the shortest paths among first degree contacts of ego, hence the betweenness of alter C is 12 (when excluding ego)*
(* = Note that an easy way to see this is that 4 nodes are on the left of D, and 3 nodes are on the right: 3⋅4 = 12)
Dispersion (Backstrom & Kleinberg, 2014) reflects if mutual friends of ego and one alter are not well connected.
Not all of the mutual friends of ego and alter D know each, C – Z and B – Z are unconnected and also have no other neighbors in common,
therefore D has a dispersion of 2.
Maintaining relationships requires investments (Lin, 1999). While members within social circles often
already know each other, events like birthday parties - or even following an invitation that coincidentally
involves multiple social circles are signs of such an investment into relationships.
University friend D is special, because the friend is the only one to know a family member.
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Empirical Approach
Facebook user survey and data extraction
Anonymized research data is hosted on Mendeley Data
https://data.mendeley.com/datasets/hr9tjzj72v/
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Who of the contacts in your Facebook profile would you consider as very close to you?
Netvizz (Rieder, 2013) is an open-source Facebook application that
extracts Facebook networks and basic user information for
scientific use.
Through the indirect data collection we can identify n = 18,541 individual data points (dyads) through the 41 responses.

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Validating the Ego Perspective
All network based measures have superior performance in ego networks
Following our line of reasoning, merging networks should not benefit the tie strength prediction task
to test this empirically we compare sampled and ego perspectives via ROC curves
A very good explanation of ROC curves is
Fawcett (2004) in Machine Learning
All network-based measures
perform best in the ego
network perspective.
Particularly betweenness
achieves a very high overall
ROC-AUC value
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We formulate a prediction algorithm via logistic regression (also evaluate random forest and gradient boosting with similar or inferior results)
Predictive Approach
Utilizing 5-fold cross-validation we test the predictive ability in combined models
and predict who are the closest friends via 5 randomly selected hold-out sets.
Insight
ROC curves and precision scores
(with varying thresholds) agree that
ego network measures (especially
bridging positions) have high
predictive power to identify the rare
closest friends.

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Practical demonstration: ability to identify the few rare positives via precision score
All Combined (Threshold = 258)
Actual
Prediction No Strong Tie Strong Tie
No Strong Tie 18,140 143
Strong Tie 143 115
Precision: 45% = 115 / (115 + 143)
Precision Scores Sensitivity Analysis

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Implications
Our findings have implications for data privacy, peer influence research, and other networks
2 Data Privacy Even when users do not knowingly disclose who their closest friends are, platform providers can predict this
information through ego network data. While prior research has emphasized the ability to predict personality traits via
Facebook Likes (e.g., Youyou 2015), our research points to a similar application: The ability to infer closest friends (i.e., strong
ties) from networks.
1 Peer Influence Research Previously revealed preference studies have predominantly used interactions to explain peer
influence through tie strength. To the best of our knowledge, bridging positions in ego networks have not yet been used to
explain peer influence, but have strong potential to do so. We encourage researchers to integrate ego network bridging
positions into their portfolio of revealed preference measures for tie strength.
Extension to Other Networks Beyond the classical online social networks, like Facebook, LinkedIn and Twitter, various on-
and offline services host similar network data that can benefit substantially from knowing which of the users are closest to each
other. For example long-term call records, phone directories, and apps that integrate networking functionality are likely to host
similar structures in which social circles are visible.
3

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Research Data on Mendeley Data
Get in Touch
simon.stolz@whu.edu
Research Data on Mendeley Data
Stolz, Simon; Schlereth, Christian (2020), “Predicting Tie Strength with Ego Network
Structures”, Mendeley Data, https://data.mendeley.com/datasets/hr9tjzj72v/
whu.edu/digital@SimStolz on Twitter
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