Evaluating link prediction methods

Knowledge and Information Systems

Volumn 45, Issue 3, 2015, Pages 751-782

  Yang, Yang ^a,b   Lichtenwalter, Ryan N ^a,b   Chawla, Nitesh V ^a,b  

^a University of Notre Dame   (United States)

^b UNIVERSITY OF NOTRE DAME   (United States)

Author keywords

Class imbalance; Link prediction and Evaluation; Sampling; Temporal effects on link prediction; Threshold curves

Indexed keywords

INFORMATION SYSTEMS; SAMPLING;

CLASS IMBALANCE; EVALUATION METRICS; IN NETWORKS; LINK PREDICTION; PERFORMANCE POTENTIALS; RECEIVER OPERATING CHARACTERISTIC CURVES; REPRODUCIBILITIES; THRESHOLD CURVES;

FORECASTING;

EID: 84944355719     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-014-0789-0     Document Type: Article

References (56)

1. Abu-Mostafa YS, Magdon-Ismail M, Lin HT (2012) Learning from data: a short course. AMLBook
2. Adamic L (2001) Friends and neighbors on the web. Soc Netw 25(3):211–230
3. Al-Hasan M, Chaoji V, Salem S, Zaki M (2006) Link prediction using supervised learning. In: Proceedings of SDM’06 workshop on link analysis, counterterrorism and security
4. Backstrom L, Leskovec J (2011) Supervised random walks: predicting and recommending links in social networks. In: Proceedings of the fourth ACM international conference on Web search and data mining, pp 635–644
5. Barabási AL, Albert R (1999) Emergence of scaling in random networks. Science 286:509–512
6. Barabási A-L, Jeong H, Néda Z, Ravasz E, Schubert A, Vicsek T (2002) Evolution of the social network of scientific collaboration. Phys A Stat Mech Appl 311(3–4):590–614
7. Clauset A, Moore C, Newman MEJ (2008) Hierarchical structure and the prediction of missing links in networks. Nature 453(7191):98–101
8. Clauset A, Shalizi CR, Newman MEJ (2009) Power-law distributions in empirical data. SIAM Rev 51(4):661–703
9. Davis D, Lichtenwalter R, Chawla NV (2011) Multi-relational link prediction in heterogeneous information networks. In: Proceedings of the 2011 international conference on advances in social networks analysis and mining, pp 281–288
10. Davis J, Goadrich M (2006) The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd international conference on machine learning, pp 233–240
11. Deng H, Han J, Zhao B, Yu Y, Lin CX (2011) Probabilistic topic models with biased propagation on heterogeneous information networks. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1271–1279
12. Dong Y, Tang J, Wu S, Tian J, Chawla NV, Rao J, Cao H (2012) Link prediction and recommendation across heterogeneous social networks. In: Proceedings of the 2012 international conference on data mining, pp 181–190
13. Drummond C, Holte RC (2006) Cost curves: an improved method for visualizing classifier performance. Mach Learn 65(1):95–130
14. Fawcett T (2004) ROC graphs: notes and practical considerations for researchers. ReCALL 31(HPL-2003-4), pp 1–38
15. Fletcher RJ, Acevedo MA, Reichert BE, Pias KE, Kitchens WM (2011) Social network models predict movement and connectivity in ecological landscapes. Proc Natl Acad Sci 108(48):19282–19287
16. Getoor L (2003) Link mining: a new data mining challenge. ACM SIGKDD Explor Newsl 5(1):84–89
17. Goldberg DS, Roth FP (2003) Assessing experimentally derived interactions in a small world. Proc Natl Acad Sci 100(8):4372–4376
18. Hand DJ (2009) Measuring classifier performance: a coherent alternative to the area under the ROC curve. Mach Learn 77(1):103–123
19. Hoeffding W (1963) Probability inequalities for sums of bounded random variables. J Am Stat Assoc 58(301):13–30
20. Hopcroft J, Lou T, Tang J (2011) Who will follow you back? Reciprocal relationship prediction. In: Proceedings of the 20th ACM international conference on Information and knowledge management, pp 1137–1146
21. Huang Z, Li X, Chen H (2005) Link prediction approach to collaborative filtering. In: Proceedings of the 5th ACM/IEEE-CS joint in proceedings on digital libraries, pp 7–11
22. Leroy V, Cambazoglu BB, Bonchi F (2010) Cold start link prediction. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, pp 393–402
23. Leskovec J, Backstrom L, Kumar R, Tomkins A (2008) Microscopic evolution of social networks. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, pp 462–470
24. Leskovec J, Lang K, Dasgupta A, Mahoney M (2009) Community structure in large networks: natural cluster sizes and the absence of large well-defined clusters. Internet Math 6(1):29–123
25. Leskovec J, Huttenlocher D, Kleinberg J (2010) Predicting positive and negative links in online social networks. In: Proceedings of the 19th international conference on world wide web, pp 641–650
26. Lichtenwalter RN, Lussier JT, Chawla NV (2010) New perspectives and methods in link prediction. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, pp 243–252
27. Lichtenwalter RN, Chawla NV (2012) Link prediction: fair and effective evaluation. In: IEEE/ACM international conference on social networks analysis and mining, pp 376–383
28. Liben-Nowell D, Kleinberg J (2003) The link prediction problem for social networks. In: Proceedings of the twelfth international conference on information and knowledge management, pp 556–559
29. Liben-Nowell D, Kleinberg J (2007) The link-prediction problem for social networks. J Am Soc Inf Sci Technol 58(7):1019–1031
30. Liu X, He Q, Tian Y, Lee WC, McPherson J, Han J (2012) Event-based social networks: linking the online and offline social worlds. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1032–1040
31. Lu L, Zhou T (2011) Link prediction in complex networks: a survey. Phys A 390(6):1150–1170
32. Martinez ND, Hawkins BA, Dawah HA, Feifarek BP (1999) Effects of sampling effort on characterization of food-web structure. Ecology 80:1044–1055
33. Murata T, Moriyasu S (2007) Link prediction of social networks based on weighted proximity measures. In: Proceedings of the IEEE/WIC/ACM international conference on web intelligence, pp 85–88
34. Mason SJ, Graham NE (2002) Areas beneath the relative operating characteristics (roc) and relative operating levels (rol) curves: statistical significance and interpretation. Q J R Meteorol Soc 2002:2145–2166
35. Narayanan A, Shi E, Rubinstein BIP (2011) Link prediction by de-anonymization: how we won the Kaggle social network challenge. Arxiv preprint arXiv:1102.4374
36. Newman MEJ (2001) Clustering and preferential attachment in growing networks. Phys Rev Lett E 64(2):025102
37. Newman MEJ (2001) The structure of scientific collaboration networks. Proc Natl Acad Sci 98:404–409
38. O’Madadhain J, Hutchins J, Smyth P (2005) Prediction and ranking algorithms for event-based network data. ACM SIGKDD Explor Newsl 7(2):23–30
39. O’Madadhain J, Smyth P, Adamic L (2005) Learning predictive models for link formation. In: International sunbelt social network conference
40. Papadopoulos F, Kitsak M, Serrano M, Boguna M, Krioukov D (2012) Popularity versus similarity in growing networks. Nature 489(7417):537–540
41. Raeder T, Hoens TR, Chawla NV (2010) Consequences of variability in classifier performance estimates. In: Proceedings of the 10th IEEE international conference on data mining, pp 421–430
42. Sarukkai RR (2000) Link prediction and path analysis using Markov Chains. In: Proceedings of the 9th international WWW inproceedings on computer networks: the international journal of computer and telecommunications networking, pp 377–386
43. Scellato S, Mascolo C, Musolesi M, Latora V (2010) Distance matters: geo-social metrics for online social networks. In: Proceedings of the 3rd conference on online social networks, pp 8–8
44. Scellato S, Noulas A, Mascolo C (2011) Exploring place features in link prediction on location-based social networks. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1046–1054
45. Scripps J, Tan PN, Chen F, Esfahanian AH (2008) A matrix alignment approach for link prediction. In: Proceedings of the 19th international conference on pattern recognition, pp 1–4
46. Stumpf MPH, Wiuf C, May RM (2005) Subnets of scale-free networks are not scale-free: sampling properties of networks. Proc Natl Acad Sci 102(12):4221–4224
47. Sprinzak E, Sattath S, Margalit H (2003) How reliable are experimental protein–protein interaction data? J Mol Biol 327(5):919–923
48. Sun Y, Han J, Aggarwal CC, Chawla NV (2012) When will it happen? Relationship prediction in heterogeneous information networks. In: Proceedings of the fifth ACM international conference on web search and data mining, pp 663–672
49. Szilágyi A, Grimm V, Arakaki AK, Skolnick J (2005) Prediction of physical protein–protein interactions. Phys Biol 2(2):S1–16
50. Taskar B, Wong MF, Abbeel P, Koller D (2003) Link prediction in relational data. In: Proceedings of the conference on neural information processing systems
51. Viswanath B, Mislove A, Cha M, Gummadi KP (2009) On the evolution of user interaction in facebook. In: Proceeding of the 2nd ACM SIGCOMM workshop on social networks, pp 37–42
52. Wang C, Satuluri V, Parthasarathy S (2007) Local probabilistic models for link prediction. In: Proceedings of the IEEE international conference on data mining, pp 322–331
53. Wang D, Pedreschi D, Song C, Giannotti F, Barabasi AL (2011) Human mobility, social ties, and link prediction. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1100–1108
54. Wittie MP, Pejovic V, Deek L, Almeroth KC, Zhao BY (2010) Exploiting locality of interest in online social networks. In: Co-NEXT ’10 proceedings of the 6th international conference
55. Yang Y, Chawla NV, Sun Y, Han J (2012) Predicting links in multi-relational and heterogeneous networks. In: Proceedings of the 12th IEEE international conference on data mining, pp 755–764
56. Yin Z, Gupta M, Weninger T, Han J (2010) A unified framework for link recommendation using random walks. In: Proceedings of the 2010 international conference on advances in social networks analysis and mining, pp 152–159