Transcript Link Prediction

A Graph-Based
Approach to Link
Prediction in Social
Networks Using a
Pareto-Optimal Genetic
Algorithm
Jeff Naruchitparames
University of Nevada, Reno - CSE
CS 790: Complex Networks, Fall 2010
• biological
social
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very dynamic
‣ Social networks =heterogeneous
‣ Dynamic, judgmental environment
‣ Affect friendships over time
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‣ 1-2 hop distance only
‣ Friend-of-friend
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‣ Multiple hops; >1
‣ Structural; purely graphbased
‣ No explicit correlation
between potential
friends...
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‣ Silva, et. al.,
‣
A Graph-based Recommendation System Using Genetic Algorithms,
2010
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Friends-of-Friends
2 hops
Filter
Order
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Filtering
“It’s more probable that you know a friend of your
friend than any other random person”
Mitchell M., Complex Systems: Network Thinking, 2006.
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Indexes
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What’s missing?
‣ Heterogeneity
‣ Human behavior and preferences
‣ Multiple hops
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My approach
• Pretty much a filtering problem...
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My approach
‣ Components (for filtering)
‣ Betweenness centrality
‣ Community detection
‣ Clique Percolation Method (CPM)
‣ Friends of friends
‣ 10-dimensional Pareto-optimal
genetic algorithm
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Betweenness Centrality
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Community Detection
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‣ Remove duplicates
‣ Remove our test cases
‣ (More on this later...)
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The Genetic Algorithm
Part
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Pareto Fronts
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The Features
1.
# of shared friends
2.
location
3.
age range
4.
general interest
5.
music
6.
attended same events
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groups
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movies
9.
education
10. religion/politics
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Pareto Optimality
‣
Localized to implementation of selection
‣
‣
Feature subset selection
We want to find the best combination of these subsets that can
give us the best solutions for how we determine friendships
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Pareto Optimality and
Feature Subset Selection
F1 F2 F3 F4 F5 F6 F7 F8 F9 F10
C1 0
1 0 1 0 0 0 1 1
0
C2 1
1 0 0 0 1 0 1 0
1
.
.
.
Cn 0
0 0 0 1 0 0 1 0
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0
A Point System
F1 F2 F3 F4 F5 F6 F7 F8 F9 F10
U1 -
3
- 11 -
-
- 20 44 -
U2 -
1
- 13 -
-
- 31 9
-
- 49 61 -
-
.
.
.
Un - 10 - 14 -
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Pareto Optimality
‣
‣
Compare with the test cases we removed earlier...
For all chromosomes in population, do:
‣
If ALL test cases ≥ optimal Pareto front
‣
Calculate fitness
‣
‣
Good to go
Else
‣
‣
Calculate fitness
Continue onto next chromosome
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Fitness Function
• ∑ ∑ p ln( f
n
10
i
i=1
j
p
) i-1
j=1
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Continuing on with the
Evolutionary Process
‣
‣
‣
‣
‣
Apply fitness proportional selection
Randomly select 2 parents to mate
Apply 1-point crossover (82% chance)
Bit mutation (0.05% chance)
Do this until ALL test cases better than Pareto front OR fitness does
not improve for 5 consecutive generations
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1-Point Crossover
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Conclusion
‣
Complex network theory + Genetic algorithm + social theory
‣
Betweenness centrality
‣
Community detection
‣
‣
Binary 10-dimensional Pareto-optimal genetic algorithm
‣
‣
Clique Percolation Method
Dominant, fitness proportional selection
Several levels of filtering and selection (aka filtering ☺)
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Future Work
‣
Better fitness function (need to ask Sociologists)
‣
Weighted chromosome for Pareto optimization (as opposed to binary)
‣
Prove all this stuff actually works (sociology standpoint??)
‣
Parallelize or GPU-ize the code (it’s in Python)
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