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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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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Introduction1. Link Creation :

Common Identity Common Bond Topical and Social Links :

Topical : Recommended based on user interestsSocial : 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

Latent factor Modeling

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Blue Links : Bond BasedGreen and Orange Links : Identity Based

Bond Based :High densityHigh reciprocality

Identity Based :

High directionality

Three Ds : Different Communities

Different RolesDifferent Degrees

Role and degree of involvement depend on :

AuthoritativenessSusceptibilitySocial Tendency

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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}

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

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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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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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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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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Model is evaluated on the following parameters :AccuracyScalability and StabilityQuality

Dataset : Twitter and Flickr datasets are used because:Identity and bond factors are presentRoles of users vary

Features :Twitter : hashtags and mentions by usersFlickr : tags assigned by users

Experimental Evaluation

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Dataset statistics

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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15Link Prediction : Twitter(left) and Flickr(right)

K = 256

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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Twitter Connections are highly topical.

More authoritativeness or followersEach community is characterized by a strong set of features

FlickrConnections are highly socialNo strong characterization in terms of features for communities

Qualitative Analysis

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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 explanations2. Explore alternate mechanisms to provide explanations3. Explore alternative mathematical frameworks

Conclusion and Future Work

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