1. Who to Follow and Why:
Link Predictions and Explanations
(http://www.francescobonchi.com/frp1266-barbieri.pdf)
Presented By:
Shivangi Bansal,
Suhas Suresh,
Rashmi Puttur
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2. Overview
WTFW : A user recommendation system in Social Networks.
Model predicts links, determines it’s type and justifies the prediction.
Key for the growth and sustenance of Social Networks
Work was partially supported by MULTISENSOR project, funded by ‘European
Commission’
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3. Introduction
1. Link Creation :
Common Identity
Common Bond
Topical and Social Links :
Topical : Recommended based on user interests
Social : Recommended based on users’ social circles.
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2. Link Explanation :
Topical Link : Top k-features associated with the topic of interest.
Social Link : Top k-common neighbors

4. Latent factor Modeling
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Blue Links : Bond Based
Green and Orange Links : Identity Based
Bond Based :
High density
High reciprocality
Identity Based :
High directionality

5. Three Ds :
Different Communities
Different Roles
Different Degrees
Role and degree of involvement depend on :
Authoritativeness
Susceptibility
Social Tendency
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6. 6
The WTFW Model
● A Directed Graph
● Users : Nodes
● Each node has a set of binary
features
Users : {1, 2, 3, 4, 5, 6, 7}
Features : {a, b, c, d, e, f}

7. Notations Used
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Graph : G(V, E)
Neighborhood : N(u)
Feature set : F
F(u) : Set of features adopted by a node , u
V(f) : Set of nodes adopting a feature, f
Π : A multinomial distribution over a set of latent factors, k
Ak,u : Degree of Authoritativeness of u in a topic, k
Sk,u : Degree of interest of u in topic, k

8. ϴk,u : Social Tendency of u
Φk,f : Importance of feature, f in topic, k
δk : Degree of Sociality
τk : Degree of Authoritativeness
xu,v : Latent Variable that represents the social or topical nature
yu,f : Status of latent variable : Authoritative or Susceptible
za and zl : Latent community assignments for link, l and feature, a
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9. The model is represented by the tuple : Θ = {Π, δ, τ, ϴ, A , S }
Probability of observing a link : l = (u,v):
Probability of feature adoption : (u,f) :
Link Prediction
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10. Inference and parameter estimation in WTFW are done using Dirichlet/Beta Priors
The overall learning process is performed using Gibbs Sampling algorithm
Some of the parameters that are estimated by the algorithm :
Learning Phase
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11. To produce an explanation, it is important to determine nature of link.
Probability that link is social :
Probability that link is topical:
1. Social Link Explanation : Set of most prospective neighbors according to a score:
2. Topical Link Explanation : List of attributes that represent user’s topics of interests
Link Labeling and Explanations
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13. Model is evaluated on the following parameters :
Accuracy
Scalability and Stability
Quality
Dataset : Twitter and Flickr datasets are used because:
Identity and bond factors are present
Roles of users vary
Features :
Twitter : hashtags and mentions by users
Flickr : tags assigned by users
Experimental Evaluation
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Dataset statistics

14. 1. Link Prediction :
Twitter : Network data is randomly split into test and train data.
Measure accuracy by varying proportions of train and test data
Flickr : Every link creation has a timestamp
Older links become train data
Newer links become test data
Competitors to WTFW model :
Common Neighbors and Features (CNF)
Adamic Adar on Neighbors and Features (AA-NF)
Joint SVD (JSVD)
Accuracy
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15. 15
Link Prediction : Twitter(left) and Flickr(right)
K = 256

16. 2. Link Labeling
Probability that a link is social :
The probability tends towards 0.5
May have a negative effect on link labeling.
Scalability and Sensitivity Analysis
Gibbs Sampling algorithm tends to converge to a stable and accurate value rapidly
2000 iterations , a good number for trade off between accuracy and learning time
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17. Twitter
Connections are highly topical.
More authoritativeness or followers
Each community is characterized by a strong set of features
Flickr
Connections are highly social
No strong characterization in terms of features for communities
Qualitative Analysis
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18. WTFW jointly factorizes feature associations and social associations
Model provides accurate link predictions and personalized explanations to support user
recommendations
Future Work :
1. Study user perception of explanations
2. Explore alternate mechanisms to provide explanations
3. Explore alternative mathematical frameworks
Conclusion and Future Work
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