Estimating node indirect interaction duration to enhance link prediction

  • Laxmi Amulya Gundala
  • Francesca SpezzanoEmail author
Original Article


Link prediction is the problem of inferring new relationships among nodes in a network that are likely to occur in the near future. Classical approaches mainly consider neighborhood structure similarity when linking nodes. However, we may also want to take into account if the two nodes are already indirectly interacting and if they will benefit from the link by having an active interaction over the time. For instance, it is better to link two nodes u and v if we know that these two nodes will interact in the social network even in the future, rather than suggesting \(v'\), which will never interact with u. In this paper, we deal with a variant of the link prediction problem: Given a pair of indirectly interacting nodes, predict whether or not they will form a link in the future. We propose a solution to this problem that leverages the predicted duration of their interaction and propose two supervised learning approaches to predict how long will two nodes interact in a network. Given a set of network-based predictors, the basic approach consists of learning a binary classifier to predict whether or not an observed indirect interaction will last in the future. The second and more fine-grained approach consists of estimating how long the interaction will last by modeling the problem via survival analysis or as a regression task. Once the duration is estimated, new links are predicted according to their descending order. Experimental results on Facebook Network and Wall Interaction and Wikipedia Clickstream datasets show that our more fine-grained approach performs the best and beats a link prediction model that does not consider the interaction duration and is based only on network properties.


Link prediction Persistent indirect interaction Estimating interaction duration Survival analysis 


  1. Adafre SF, de Rijke M (2005) Discovering missing links in Wikipedia. In: LinkKDD ’05, pp 90–97Google Scholar
  2. Ameri S, Fard MJ, Chinnam RB, Reddy CK (2016) Survival analysis based framework for early prediction of student dropouts. In: CIKM, pp 903–912Google Scholar
  3. Backstrom L, Leskovec J (2011) Supervised random walks: predicting and recommending links in social networks. In: WSDM, pp 635–644Google Scholar
  4. Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Comput Netw 30(1–7):107–117Google Scholar
  5. Cacheda F, Barbieri N, Blanco R (2017) Click through rate prediction for local search results. In: WSDM, pp 171–180Google Scholar
  6. Dave VS, Hasan MA, Reddy CK (2017) How fast will you get a response? Predicting interval time for reciprocal link creation. In: ICWSM, pp 508–511Google Scholar
  7. Davis J, Goadrich M (2006) The relationship between precision-recall and roc curves. In: Proceedings of the 23rd international conference on machine learning. ACM, pp 233–240Google Scholar
  8. Dhakal N, Spezzano F, Xu D (2017) Predicting friendship strength for privacy preserving: a case study on Facebook. In: ASONAM, pp 1096–1103Google Scholar
  9. Gilbert E, Karahalios K (2009) Predicting tie strength with social media. In: CHI, pp 211–220Google Scholar
  10. Goyal P, Ferrara E (2018) Graph embedding techniques, applications, and performance: a survey. Knowledge-Based Syst 151:78–94CrossRefGoogle Scholar
  11. Grover A, Leskovec J (2016) Node2vec: scalable feature learning for networks. In: KDD, pp 855–864Google Scholar
  12. Gundala LA, Spezzano F (2018) A framework for predicting links between indirectly interacting nodes. In: IEEE/ACM 2018 international conference on advances in social networks analysis and mining, ASONAM 2018, Barcelona, Spain, August 28–31, 2018, pp 544–551Google Scholar
  13. Hasan MA, Zaki MJ (2011) A survey of link prediction in social networks. In: Aggarwal C (ed) Social network data analytics. Springer, Boston, MA, pp 243–275CrossRefGoogle Scholar
  14. Jones JJ, Settle JE, Bond RM, Fariss CJ, Marlow C, Fowler JH (2013) Inferring tie strength from online directed behavior. PloS One 8(1):e52168CrossRefGoogle Scholar
  15. Kahanda I, Neville J (2009) Using transactional information to predict link strength in online social networks. In: 3rd international AAAI conference on weblogs and social media, pp 74–81Google Scholar
  16. Kamath K, Sharma A, Wang D, Yin Z (2014) Realgraph: user interaction prediction at twitter. In: User Engagement Optimization Workshop @ KDDGoogle Scholar
  17. Kumar S, Spezzano F, Subrahmanian VS, Faloutsos C (2016) Edge weight prediction in weighted signed networks. In: ICDM, pp 221–230Google Scholar
  18. Lei C, Ruan J (2012) A novel link prediction algorithm for reconstructing protein–protein interaction networks by topological similarity. Bioinformatics 29(3):355–364CrossRefGoogle Scholar
  19. Liben-Nowell D, Kleinberg J (2007) The link-prediction problem for social networks. J Assoc Inf Sci Technol 58(7):1019–1031CrossRefGoogle Scholar
  20. Li Y, Rakesh V, Reddy CK (2016) Project success prediction in crowdfunding environments. In: WSDM, pp 247–256Google Scholar
  21. Menon AK, Elkan C (2011) Link prediction via matrix factorization. In: ECML PKDD, pp 437–452Google Scholar
  22. Murtaugh PA, Burns LD, Schuster J (1999) Predictiong the retention of university students. Res High Educ 40(3):355–371CrossRefGoogle Scholar
  23. Noraset T, Bhagavatula C, Downey D (2014) Adding high-precision links to Wikipedia. In: EMNLP ’14, pp 651–656Google Scholar
  24. Paranjape A, West R, Zia L, Leskovec J (2016) Improving website hyperlink structure using server logs. In: WSDM ’16, pp 615–624Google Scholar
  25. Pavlov M, Ichise R (2007) Finding experts by link prediction in co-authorship networks. In: Proceedings of the 2nd international conference on finding experts on the web with semantics, vol 290, pp 42–55Google Scholar
  26. Pavlov M, Ichise R (2007) Finding experts by link prediction in co-authorship networks. In: Proceedings of the 2nd international conference on finding experts on the web with semantics, vol 290. FEWS’07, pp 42–55Google Scholar
  27. Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: online learning of social representations. In: KDD, pp 701–710Google Scholar
  28. Qian Y, Adali S (2014) Foundations of trust and distrust in networks: extended structural balance theory. ACM Trans Web (TWEB) 8(3):13Google Scholar
  29. Rakesh V, Lee W, Reddy CK (2016) Probabilistic group recommendation model for crowd funding domains. In: WSDM, pp 257–266Google Scholar
  30. Richardson M, Dominowska E, Ragno R (2007) Predicting clicks: estimating the click-through rate for new ads. In: WWW, pp 521–530Google Scholar
  31. Tang L, Liu H (2011) Leveraging social media networks for classification. Data Min Knowl Discov 23(3):447–478MathSciNetCrossRefGoogle Scholar
  32. Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) Line: large-scale information network embedding. In: WWW, pp 1067–1077Google Scholar
  33. Viswanath B, Mislove A, Cha M, Gummadi KP (2009) On the evolution of user interaction in Facebook. In: WOSN, pp 37–42Google Scholar
  34. Wang P, Li Y, Reddy CK (2017) Machine learning for survival analysis: a survey. CoRR arXiv:abs/1708.04649
  35. West R, Paranjape A, Leskovec J (2015) Mining missing hyperlinks from human navigation traces: a case study of Wikipedia. In: WWW ’15, pp 1242–1252Google Scholar
  36. West R, Paskov HS, Leskovec J, Potts C (2014) Exploiting social network structure for person-to-person sentiment analysis. Trans Assoc Comput Linguist 2:297–310CrossRefGoogle Scholar
  37. West R, Pineau J, Precup D (2009) Wikispeedia: an online game for inferring semantic distances between concepts. In: IJCAI’09, pp 1598–1603Google Scholar
  38. West R, Precup D, Pineau J (2009) Completing Wikipedia’s hyperlink structure through dimensionality reduction. In: CIKM ’09, pp 1097–1106Google Scholar
  39. Wilson C, Sala A, Puttaswamy KP, Zhao BY (2012) Beyond social graphs: user interactions in online social networks and their implications. ACM Trans Web (TWEB) 6(4):17Google Scholar
  40. Xiang R, Neville J, Rogati M (2010) Modeling relationship strength in online social networks. In: WWW, pp 981–990Google Scholar
  41. Zignani M, Gaito S, Rossi GP (2016) Predicting the link strength of “newborn” links. In: WWW Companion, pp 147–148Google Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Computer Science DepartmentBoise State UniversityBoiseUSA

Personalised recommendations