Neural Networks in Social Networks

1. Neural Networks in Social Networks Student:Nikolić Filip nf143006m@student.etf.rs Professor: Veljko Milutinović Assistant: Bojan Furlan

2. Data Mining – Neural Networks School of Electrical Engineering, Belgrade Tasks: Introducing What isNeuralNetworks? Training Feature Conclusion • Classification friend into group (family, friend, colleague…) • Recognition of friends • Find new friend • Find all friends • Automatic add friend with new profile

3. Data Mining – Neural Networks School of Electrical Engineering, Belgrade Existing algorithms: Introducing What isNeuralNetworks? Training Feature Conclusion • Facebook-People You May Know • LinkedIn-connection you may know • Decision Tree • DataBase Query People You May Know looks at, among other things, your current friend list and their friends, your education info and your work info. If you are already friends on Facebook with some people from your last job, for example, you may find some more of your former coworkers Anything you do on LinkedIn site is tracked. For every action, you get points. 5 order LinkedIn suggestion effects.

4. Data Mining – Neural Networks School of Electrical Engineering, Belgrade What is Neural Networks? Introducing What isNeuralNetworks? Training Feature Conclusion • Analogy -Neuron in the brain

5. Data Mining – Neural Networks School of Electrical Engineering, Belgrade What is Neural Networks? Introducing What isNeuralNetworks? Training Feature Conclusion • Neuron model: Logistic unit input hidden output family address neighbords faculty colleagues work black list surname birthday education where are you from hobby friends from university childhood friends sports friends ex-girlfriend/boyfriend

6. Data Mining – Neural Networks 1 School of Electrical Engineering, Belgrade What is Neural Networks? Introducing What isNeuralNetworks? Training Feature Conclusion z • Neuron model: Simple example-AND bias -30 +20 - family surname +20 where are you from if • if 0 (-30) 0 (-10) 0 (-10) 1 (+10)

7. Data Mining – Neural Networks School of Electrical Engineering, Belgrade Training a Neural Network Introducing What isNeuralNetworks? Training Feature Conclusion • Pick a parameters of network architecture Layer 1 Layer 2 Class 1 Input 1 Class 2 Input 2 Class 3 Input 3 Class 4

8. Data Mining – Neural Networks School of Electrical Engineering, Belgrade Training a Neural Network Introducing What isNeuralNetworks? Training Feature Conclusion • Randomly initialize weights Layer 1 Layer 2 Class 1 Input 1 Class 2 Input 2 Class 3 Input 3 Class 4

9. Data Mining – Neural Networks School of Electrical Engineering, Belgrade Training a Neural Network Introducing What isNeuralNetworks? Training Feature Conclusion • Forward propagation by Training Set Layer 1 Layer 2 Class 1 Input 1 Class 2 Input 2 Class 3 Input 3 Class 4 Training Set:

10. Data Mining – Neural Networks School of Electrical Engineering, Belgrade Training a Neural Network Introducing What isNeuralNetworks? Training Feature Conclusion • Backward propagation Layer 1 Layer 2 Class 1 Input 1 Class 2 Input 2 Class 3 Input 3 Class 4 Training Set:

11. Data Mining – Neural Networks School of Electrical Engineering, Belgrade Training a Neural Network Introducing What isNeuralNetworks? Training Feature Conclusion • Loop through training set Layer 1 Layer 2 Class 1 Input 1 Class 2 Input 2 Class 3 Input 3 Class 4 Training Set:

12. Data Mining – Neural Networks School of Electrical Engineering, Belgrade Advantages Introducing What isNeuralNetworks? Training Feature Conclusion • A neural network can perform tasks that a linear program cannot. • When an element of the neural network fails, it can continue without any problem by their parallel nature. • A neural network learns and does not need to be reprogrammed. • It can be implemented in any application and without any problem. • Neural networks are the closest thing to having an actual human operate a system (i.e., they can "learn") • Easy implementation in parallel calculation process.

13. Data Mining – Neural Networks School of Electrical Engineering, Belgrade Disadvantages Introducing What isNeuralNetworks? Training Feature Conclusion • The neural network needs training to operate. • The operation of neural networks is limited to the training process. • The architecture of a neural network is different from the architecture of microprocessors; therefore, needs to be emulated. • Neural networks are difficult to design. • Requires high processing time for large neural networks.

14. Data Mining – Neural Networks School of Electrical Engineering, Belgrade Conclusion Introducing What isNeuralNetworks? Training Feature Conclusion • Neural Networks are an imitation of the biological neural networks, but much simpler ones. • Social Networks behave as Neural Networks. • Neural networks can imitatecomplex mathematical functions, biological functions, and sociological behavior. • If given sufficient training set:The enemy of my enemy is my friend. • Literature:https://class.coursera.org/ml-007/lecture/indexhttp://home.etf.bg.ac.rs/~vm/os/dmsw/osdmswpreddatamining.html Q&A