1. The document discusses supervised learning methods for link recommendation in co-authorship networks. 2. It compares algorithms like decision trees, naive Bayes, neural networks, random forests and bagging using metrics like AUC, precision, recall and F1-measure. 3. The experiments show that random forests and bagging outperform other methods, particularly when dealing with redundant features. The core size parameter k and time intervals also impact recommendation quality.

1. Introduction Link Prediction and Metrics Results Conclusions
Supervised Learning Link Recommendation in the
DBLP co-authoring network
Gabriel P Gimenes, Hugo Gualdron, Thiago R Raddo, Jose F
Rodrigues Jr
Instituto de Ci^encias Matematicas e de Computac~ao
Universidade de S~ao Paulo
Av. Trabalhador S~ao-carlense, 400-Centro, S~ao Carlos, SP, Brasil
Click for paper:
http://www.icmc.usp.br/pessoas/junio/PublishedPapers/Gimenes_et_al_IEEE-PerCom-SCI2014.pdf
This work has

2. nanctial support from FAPESP (2013/10026-7 2011/13724-1)
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3. Introduction Link Prediction and Metrics Results Conclusions
Summary
1 Introduction
2 Link Prediction and Metrics
3 Results
4 Conclusions
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4. Introduction Link Prediction and Metrics Results Conclusions
Context
Advances in the WWW led to improved mechanisms for users
to interact
Data became abundant in several scenarios
social networks, co-authoring networks, recommender systems,
communication networks
Need for tools that can assist in the decision making process
Most of the networks produced on our daily lives are dynamic
- Link Recommendation
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5. Introduction Link Prediction and Metrics Results Conclusions
Objectives
Analysis of the Link Recommendation task on a co-authoring
network - DBLP
Comparison between the most used algorithms in supervised
learning using performance metrics (AUC, F-measure,
Precision e Recall)
Including the use of meta-classi

6. ers such as Bagging and
Random Forest
Detailed study of the parameters involved on the technique -
Core(k) and the intervals
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7. Introduction Link Prediction and Metrics Results Conclusions
Link Prediction and Metrics
1 Introduction
2 Link Prediction and Metrics
3 Results
4 Conclusions
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8. Introduction Link Prediction and Metrics Results Conclusions
Problem De

9. nition
It is possible to model a co-authoring network as a graph,
nodes represent individuals and edges indicate a collaboration
between them
The idea is to predict/recommend new edges using only past
and present informations about the network using supervised
learning techniques
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10. Introduction Link Prediction and Metrics Results Conclusions
Problem De

11. nition
Applications exist in dierent domains such as:
Forecasting suspect behavior on social networks, terrorism, for
example
Identifying interactions that would need intense
experimentation in biology
Suggesting new collaborations/interactions to individuals on
co-authoring networks
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12. Introduction Link Prediction and Metrics Results Conclusions
Problem De

13. nition
Given a snapshot of a network on time t, we are interested in
the edges that most likely should/could exist in t', where
t t0.
Training a supervised classi

14. er using topological features
extracted from the network to be able to analyze its dynamics
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15. Introduction Link Prediction and Metrics Results Conclusions
Problem De

16. nition
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17. Introduction Link Prediction and Metrics Results Conclusions
Core
Core(k) is the subset of nodes of interest
Nodes that have at least k edges on training and test intervals
are considered to be in Core(k), the other nodes are not used
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18. Introduction Link Prediction and Metrics Results Conclusions
Topological Features
Metric Equation
Common Neighbours CN(x; y) = j(x) (y)j
Jaccard Coe

19. cient JC(x; y) = j(x)(y)j
j(x)[(y)j
Preferential Attachment PA(x; y) = j(x)j j(y)j
Adamic-Adar Coe

20. cient AA(x; y) =
P
z2(x)(y)
1
logj(z)j
Geodesic Distance shortest path between x and y
Resource Allocation Index RA(x; y) =
P
z2(x)(y)
1
j(z)j
Local Paths LP(x; y) =