Meta-path based Multi-Network Collective Link Prediction

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1 Meta-path based Multi-Network Collective Link Prediction Jiawei Zhang 1,2, Philip S. Yu 1, Zhi-Hua Zhou 2 University of Illinois at Chicago 2, Nanjing University 2

2 Traditional social link prediction in one single social network Locations Social Links Temporal Activities 8 AM 12 PM 4 PM 8 PM 11 PM Contents: Tweets

3 Users use multiple social networks simultaneously Temporal Activities 8 AM 12 PM 4 PM 8 PM 11 PM Locations User Accounts anchor links User Accounts Temporal Activities anchor 8 AM 12 PM 4 PMusers 8 11 locate Locations non-anchor users Aligned Social Networks locate Tips Tweets

4 Predicting social links in multiple aligned networks simultaneously anchor link existing social links social links to be predicted Network 1 Network 2

5 Disadvantages of Supervised Link class imbalance problem negative instances >> positive instances Prediction Setting non-existing links!= negative links network structure u5 u4 u3 u2 u1 link to be predicted (u5, u4) existing links non-existing links information used to extract feature vectors for these links link features label (u1, u2) +1 (u3, u5) +1 (u3, u4) -1 (u4, u2) -1 supervised learning model non-existing links should be unlabeled links Supervised link prediction ==> Positive Unlabeled (PU) link prediction PU Learning: How to find reliable negative links label/score

6 Reliable Negative Links Extraction Existing-Spy { training set P N test set P { { { N U Spy U Spy classification results { RN { Spy Positive Links classification boundary Unlabeled Links Feature Space Reliable Negative Links

7 PU Link Prediction Setting network structure u5 u4 u3 u2 u1 existing links what kind of information! reliable negative links information used to extract feature vectors for these links are there in the network link features label (u1, u2) +1 (u3, u5) +1 (ux, uy) -1 (ux, uy) -1 link to be predicted (u5, u4) supervised learning model scores

8 Heterogeneous Information Locations Social Links Temporal Activities what kind of features can! be extracted 8 AM 12 PM 4 PM 8 PM 11 PM Contents: Tweets

9 Network Schema 8 AM 8 PM social network schema User follow/follow -1 write -1 write Word contain Post written at contain -1 written at -1 Time! stamp checkin at -1 checkin at Location

10 Intra-network social meta paths User follow/follow -1 write -1 write Word contain Post written at contain -1 written at -1 Time! stamp checkin at -1 checkin at Location

11 New network problem anchor link existing social links New network social links to be predicted Network 1 information in other aligned networks can Network 2 sparse information ==> sparse feature be transferred to the new network or not

12 Anchor Meta path & Inter-network social meta paths

13 Inter-network social meta path instances Links: anchor link social link potential social link Paths: B 1 to A 1 C 1 tod 1 C 1 to E 1 Network 1 C 1 D 1 A 1 B 1 E 1 Network 2 H 2 D 2 A 2 F 2 B 2 G 2

14 Meta path selection

15 Multi-network collective link Network 1 prediction framework feature extraction y(p 1 ),y(u 1 ) x (P 1 ), x (U 1 ) x (L 1 ) x (P 1 ), x (U 1 ) x (L 1 ) build update network P 1, U 1 L 1 M 1, MS 1 y(l 1 ) p(l 1 ) predict update network Network 2 y(p 2 ),y(u 2 ) x (P 2 ), x (U 2 ) x (L 2 ) x (P 2 ), x (U 2 ) x (L 2 ) P 2, U 2 build M 2, MS 2 L 2 predict y(l 2 ) p(l 2 ) update network Network N y(p n ),y(u n ) x (P n ), x (U n ) x (L n ) P n, U n build M n, MS n L n predict y(l n ) p(l n ) x (P n ), x (U n ) x (L n )

16 Foursquare and Twitter Dataset

17 Experiment Settings Ground truth: existing social link among users hide part of the existing links in the test set build model to discover these links! Comparison Methods MLI (Multi-network Link Identifier) LI (Link Identifier): predict links in each network independently SCAN(Supervised Cross-Aligned-Network link prediction): supervised link prediction, no meta path selection, SCAN_s (SCAN with source network): features are extracted based on inter-network meta paths SCAN_t (SCAN with target network): features are extracted based on intranetwork meta paths! Evaluation Metrics AUC, Accuracy, F1

18 collective link prediction is better than independent link prediction Experiment Results PU link prediction setting and meta path selection can improve the results using features based on intra-network meta paths and inter-network meta paths simultaneously can achieve better results

19 Parameter Analysis ratio of anchor links the more anchor links we have, the better performance we can achieve

20 Convergence Analysis converge quickly, in less than 10 iterations

21 Conclusions Problem studied: collective link prediction across multiple aligned social networks Proposed Method: PU Link Prediction Setting Intra-network & Inter-network Meta Path based Feature Extraction Meta path selection Multi-network Collective PU Link Prediction Framework Experiment Results: Collective Link Prediction is better than Independent Link Prediction PU Link Prediction & Meta Path Selection can improve the results Using information across networks can achieve better results MLI can perform well consistently for different anchor link ratios & can converge quickly

22 Q&A

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