神经网络该如何看待社会网络

1 .How Neural Networks See Social Networks Daniel Darabos, Janos Maginecz #DLSAIS11 

2 .Context Lynx does social network analytics for telcos & others. We use graph convolutional neural networks. Good predictions of missing data and future behavior. We want to understand how the network does it. #DLSAIS11 2 

3 .Graph Convolutional Network #DLSAIS11 3 

4 .input network prediction #DLSAIS11 4 

5 . repeat n times state new state input network prediction #DLSAIS11 5 

6 . repeat n times state new state vertex features network for each known vertex labels prediction neighbor vertex state edges #DLSAIS11 6 

7 .“Neural Network That Learns From a Huge Graph” Spark Summit East 2017 #DLSAIS11 7 

8 .Running Examples #DLSAIS11 8 

9 .complete real data network prediction #DLSAIS11 9 

10 . ✔ Validated against ✔ known labels ARTIAL real data Pcomplete network prediction #DLSAIS11 10 

11 . ✔ Validated against ✔ known labels ARTIAL real data Pcomplete network prediction attribution & feature explanation visualization #DLSAIS11 11 

12 . ✔ Validated against ✔ known labels t he t ic complete real data network prediction syn ✔ Validated against ✔ known rules attribution & feature explanation visualization #DLSAIS11 12 

13 .Running example: Three Friends Real social network edges. positive Synthetic features: random “talent”, 0–1. Synthetic labels: “three friends are rockstars” or not. “rockstar” means “talent” > θ rockstar #DLSAIS11 13 

14 . node rockstar color is class subject node #DLSAIS11 14 

15 . direct neighbor Running example: Friends of Friends rockstar Real social network edges. Synthetic features: random “talent”, 0–1. Synthetic labels: “distance to a rockstar is 2” or not. “rockstar” means “talent” > θ positive #DLSAIS11 15 

16 .Running example: age prediction 10–20 Real social network edges. 29–69 No features. Real labels: age bucket (4 equal-sized buckets) 20–24 24–29 #DLSAIS11 16 

17 .Running example: gender prediction Real social network edges. No features. Real labels: gender female male #DLSAIS11 17 

18 .Feature Visualization #DLSAIS11 18 

19 .Feature visualization What fictional inputs exemplify a class the most? “Feature Visualization” Chris Olah, et al., Distill #DLSAIS11 19 

20 .Creating examples random noise optimized image backprop on input to maximize class probability “Visualizing Higher-Layer Features of a Deep Network” Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pascal Vincent #DLSAIS11 20 

21 .Creating graph examples Initial state: complete graph with 10 vertices features randomized, 0–1 Backprop: features adjacency matrix #DLSAIS11 21 

22 .Feature visualization: Friends of Friends #DLSAIS11 22 

23 .Feature visualization: Friends of Friends #DLSAIS11 23 

24 .Feature visualization: Three Friends #DLSAIS11 24 

25 .Feature visualization: age prediction 24–29 99.2% confidence #DLSAIS11 25 

26 .Feature visualization: age prediction 29–69 99.9998% confidence #DLSAIS11 26 

27 .Feature visualization: age prediction 99.99% 24–29 confidence 29–69 10–20 #DLSAIS11 27 

28 .Feature visualization: age prediction 99.99% 24–29 confidence 10–20 #DLSAIS11 28 

29 .Feature visualization: age prediction 99.99% 24–29 confidence 10–20 #DLSAIS11 29