modeling relationship strength in online social networks rongjing xiang: purdue university jennifer...
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Modeling Relationship Strength in Online Social Networks
Rongjing Xiang: Purdue UniversityJennifer Neville: Purdue University
Monica Rogati: LinkedInWWW 2010
Presenter: Chenghui RENSupervisors: Dr Ben Kao, Prof David Cheung
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Why do we care aboutRelationship Strength?
• Various aspects of online social networks (OSNs) are based on relationship strength:– Link prediction
• Suggesting new people with top relationship strength to users
– Item recommendation• Items may be groups to join, articles to read…
– Newsfeeds• Real-time updates about status change, activities, new posts…
– People search– …
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What has been done onRelationship Strength?
• Previous work analyzing OSNs has focused on binary friendship relations – E.g., friends or not
• Low cost of link formation leads to networks with different relationship strengths– E.g. close friends and acquaintances
• Treating all relationships as equal will increase the level of noise in a learned model and degrade performance.
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Problem• Typically, an OSN contains:– Profiles– Interaction activities
To propose a method to infer a continuous-valued relationship strength for links based on the factors above
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Roadmap
• Motivation• Latent Variable Model• Experimental Evaluation• Conclusions
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Latent Variable Model: Introduction
• The homophily is common in OSNs– People tend to form ties with other people who have similar
characteristics– The stronger the tie, the higher the similarity
• Relationship strength is modeled as a hidden effect of nodal profile similarities– E.g. the schools and companies the users attended– E.g. the online groups they joined– E.g. the geographic locations that they belong to
• Relationship strength is modeled as a hidden cause of user interactions– E.g. profile viewing activities– E.g. picture tagging
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Model Introduction (Cont’d)
Profile attributes
Relationship strength
User interactions
Have effect on
Cause of
Visible
Visible
Invisible
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Model: Introduction (Cont’d)
Goal: Estimate z to maximize the overall observed data likelihood
Figure 1: Graphical model representation of the general relationship strength model
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Model Specification
Profile attributes
Relationship strength
Affect
Visible
Invisible
First model this part
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Model Specification (Cont’d)Using Gaussian distribution to model the conditional probability of z given profile similarities:
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Model Specification (Cont’d)
Relationship strength
User interactions
Cause of
Visible
Invisible
Then model this part
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Model Specification (Cont’d)
Using a logistic function to model the conditional probability of y given u: Figure 2: Graphical model
representation of the specific instantiation
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Model Specification (Cont’d)To avoid over-fitting, L2 regularizers are put on the parameters w and θ, which can be regarded as Gaussian priors:
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Model InferenceTwo ways to estimate a latent variable model
Future work
Accepted!
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Model Inference (Cont’d)
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Roadmap
• Motivation• Latent Variable Model• Experimental Evaluation• Conclusions
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Experimental EvaluationDataset:Purdue facebook data#nodes: 4500#links: 144,712
Three profile similarity measures:
Two types of user interactions: Auxiliary variables: #people whose wall i has posted i has tagged in pictures
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Experiment Evaluation (Cont’d)
• Use the proposed latent variable model to estimate the relationship strengths for the 144,712 pairs of users
How to evaluate the estimated weighted graph? Apply the estimated weighted
graph in a number of collective classification tasks.
Gender: Male? Relationship status: Single? Political views: Conservative? Religious views: Christian?
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Classification Algorithm
• Gaussian Random Field Model– Autocorrelation is present in the graph– Information is propagated from the labeled
portion of the graph to infer the values for unlabeled nodes
• Vary the proportion of labeled nodes in the graph from 30% to 90%
• Measure the resulting classification rankings using area under the ROC curve
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ROC curve
x-axis: False positive rate
y-axis: True positive rateThe larger the area
under the ROC curve, the higher the overall accuracy of the classification
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Comparisons to Six Graphs
• Four observed graphs– Friendship graph– Top-friend graph– Wall graph– Picture graph
• Two additional graphs– Profile-similarity graph, which weights each link by – Interaction-count graph, which sums the links in
the wall
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Results
Collective classification performance on various Facebook graphsCurves for the wall graph and the picture graph lie well below other curves, and are then omitted
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Roadmap
• Motivation• Latent Variable Model• Experimental Evaluation• Conclusions
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Conclusions
• A latent variable model was proposed to estimate relationship strength in OSNs
• The weighted graph formed by the estimated relationship strengths give rise to higher autocorrelation and better classification
• The model can facilitate many graph learning and social network mining tasks
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Q&AThanks!