Structural Recommendations in Networks



The growth of various Web-enabled networks has enabled numerous models of recommendation. For example, the Web itself is a large and distributed repository of data, and a search engine such as Google can be considered a keyword-centric variation of the notion of recommendation. In fact, a major discourse in the recommendation literature is to distinguish between the notions of search and recommendations. While search technologies also recommend content to users, the results are often not personalized to the user at hand. This lack of personalization has traditionally been the case because of the historical difficulty in tracking large numbers of Web users. However, in recent years, many personalized notions of search have arisen, where the Web pages recommended to users are based on personal interests. Many search engine providers, such as Google, now provide the ability to determine personalized results. This problem is exactly equivalent to that of ranking nodes in networks with the use of personalized preferences.


Collaborative Filter Link Prediction Undirected Network User Node PageRank Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. [16]
    E. Agichtein, C. Castillo, D. Donato, A. Gionis, and G. Mishne. Finding high-quality content in social media. Web Search and Data Mining Conference, pp. 183–194, 2008.Google Scholar
  2. [22]
    C. Aggarwal. Data mining: the textbook. Springer, New York, 2015.Google Scholar
  3. [23]
    C. Aggarwal and J. Han. Frequent pattern mining. Springer, New York, 2014.Google Scholar
  4. [36]
    C. Aggarwal, Y. Xie, and P. Yu. On dynamic link inference in heterogeneous networks. SIAM Conference on Data Mining, pp. 415–426, 2012.Google Scholar
  5. [42]
    M. Al Hasan, and M. J. Zaki. A survey of link prediction in social networks. Social network data analytics, Springer, pp. 243–275, 2011.Google Scholar
  6. [56]
    A. Azran. The rendezvous algorithm: Multiclass semi-supervised learning with markov random walks. International Conference on Machine Learning, pp. 49–56, 2007.Google Scholar
  7. [77]
    S. Bhagat, G. Cormode, and S. Muthukrishnan. Node classification in social networks. Social Network Data Analytics, Springer, pp. 115–148. 2011.Google Scholar
  8. [80]
    B. Bi, Y. Tian, Y. Sismanis, A. Balmin, and J. Cho. Scalable topic-specific influence analysis on microblogs. Web Search and Data Mining Conference, pp. 513–522, 2014.Google Scholar
  9. [81]
    J. Bian, Y. Liu, D. Zhou, E. Agichtein, and H. Zha. Learning to recognize reliable users and content in social media with coupled mutual reinforcement. World Wide Web Conference, pp. 51–60, 2009.Google Scholar
  10. [104]
    S. Brin, and L. Page. The anatomy of a large-scale hypertextual web search engine. Computer Networks, 30(1–7), pp. 107–117, 1998.Google Scholar
  11. [143]
    S. Chakrabarti, B. Dom, and P. Indyk. Enhanced hypertext categorization using hyperlinks. ACM SIGMOD Conference, pp. 307–318, 1998.Google Scholar
  12. [152]
    W. Chen, Y. Wang, and S. Yang. Efficient influence maximization in social networks. ACM KDD Conference, pp. 199–208, 2009.Google Scholar
  13. [153]
    W. Chen, C. Wang, and Y. Wang. Scalable influence maximization for prevalent viral marketing in large-scale social networks. ACM KDD Conference, pp. 1029–1038, 2010.Google Scholar
  14. [154]
    W. Chen, Y. Yuan, and L. Zhang. Scalable influence maximization in social networks under the linear threshold model. IEEE International Conference on Data Mining, pp. 88–97, 2010.Google Scholar
  15. [157]
    K. Y. Chiang, C. J. Hsieh, N. Natarajan, I. S., Dhillon, and A. Tewari. Prediction and clustering in signed networks: a local to global perspective. The Journal of Machine Learning Research, 15(1), pp. 1177–1213, 2014.Google Scholar
  16. [176]
    P. Domingos and M. Richardson. Mining the network value of customers. ACM KDD Conference, pp. 57–66, 2001.Google Scholar
  17. [232]
    M. Gori and A. Pucci. Itemrank: a random-walk based scoring algorithm for recommender engines. IJCAI Conference, pp. 2766–2771, 2007.Google Scholar
  18. [233]
    A. Goyal, F. Bonchi, and L. V. S. Lakshmanan. A data-based approach to social influence maximization. VLDB Conference, pp. 73–84, 2011.Google Scholar
  19. [234]
    A. Goyal, F. Bonchi, and L. V. S. Lakshmanan. Learning influence probabilities in social networks. ACM WSDM Conference, pp. 241–250, 2011.Google Scholar
  20. [235]
    Q. Gu, J. Zhou, and C. Ding. Collaborative filtering: Weighted nonnegative matrix factorization incorporating user and item graphs. SIAM Conference on Data Mining, pp. 199–210, 2010.Google Scholar
  21. [243]
    T. H. Haveliwala. Topic-sensitive pagerank. World Wide Web Conference, pp. 517–526, 2002.Google Scholar
  22. [260]
    Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. IEEE International Conference on Data Mining, pp. 263–272, 2008.Google Scholar
  23. [278]
    G. Jeh, and J. Widom. SimRank: a measure of structural-context similarity. ACM KDD Conference, pp. 538–543, 2003.Google Scholar
  24. [297]
    D. Kempe, J. Kleinberg, and E. Tardos. Maximizing the spread of influence through a social network. ACM KDD Conference, pp. 137–146, 2003.Google Scholar
  25. [302]
    J. Kleinberg. Authoritative sources in a hyperlinked environment. Journal of the ACM (JACM), 46(5), pp. 604–632, 1999.MathSciNetCrossRefzbMATHGoogle Scholar
  26. [306]
    X. Kong, X. Shi, and P. S. Yu. Multi-Label collective classification. SIAM Conference on Data Mining, pp. 618–629, 2011.Google Scholar
  27. [324]
    J. Kunegis, S. Schmidt, A. Lommatzsch, J. Lerner, E. De Luca, and S. Albayrak. Spectral analysis of signed graphs for clustering, prediction and visualization. SIAM Conference on Data Mining, pp. 559–559, 2010.Google Scholar
  28. [325]
    J. Kunegis, E. De Luca, and S. Albayrak. The link prediction problem in bipartite networks. Computational Intelligence for Knowledge-based Systems Design, Springer, pp. 380–389, 2010.Google Scholar
  29. [326]
    J. Kunegis and A. Lommatzsch. Learning spectral graph transformations for link prediction. International Conference on Machine Learning, pp. 562–568, 2009.Google Scholar
  30. [346]
    J. Leskovec, D. Huttenlocher, and J. Kleinberg. Predicting positive and negative links in online social networks. World Wide Web Conference, pp. 641–650, 2010.Google Scholar
  31. [350]
    M. Li, B. M. Dias, I. Jarman, W. El-Deredy, and P. J. Lisboa. Grocery shopping recommendations based on basket-sensitive random walk. KDD Conference, pp. 1215–1224, 2009.Google Scholar
  32. [354]
    D. Liben-Nowell and J. Kleinberg. The link-prediction problem for social networks. Journal of the American society for information science and technology, 58(7), pp. 1019–1031, 2007.CrossRefGoogle Scholar
  33. [355]
    R. Lichtenwalter, J. Lussier, and N. Chawla. New perspectives and methods in link prediction. ACM KDD Conference, pp. 243–252, 2010.Google Scholar
  34. [369]
    L. Liu, J. Tang, J. Han, M. Jiang, and S. Yang. Mining topic-level influence in heterogeneous networks. ACM CIKM Conference, pp. 199–208, 2010.Google Scholar
  35. [375]
    B. London, and L. Getoor. Collective classification of network data. Data Classification: Algorithms and Applications, CRC Press, pp. 399–416, 2014.Google Scholar
  36. [379]
    Q. Lu, and L. Getoor. Link-based classification. ICML Conference, pp. 496–503, 2003.Google Scholar
  37. [387]
    S. Macskassy, and F. Provost. A simple relational classifier. Second Workshop on Multi-Relational Data Mining (MRDM) at ACM KDD Conference, 2003.Google Scholar
  38. [388]
    S. A. Macskassy, and F. Provost. Classification in networked data: A toolkit and a univariate case study. Joirnal of Machine Learning Research, 8, pp. 935–983, 2007.Google Scholar
  39. [432]
    A. K. Menon, and C. Elkan. Link prediction via matrix factorization. Machine Learning and Knowledge Discovery in Databases, pp. 437–452, 2011.Google Scholar
  40. [452]
    G. Nemhauser, and L. Wolsey. Integer and combinatorial optimization. Wiley, New York, 1988.Google Scholar
  41. [453]
    J. Neville, and D. Jensen. Iterative classification in relational data. AAAI Workshop on Learning Statistical Models from Relational Data, pp. 13–20, 2000.Google Scholar
  42. [465]
    L. Page, S. Brin, R. Motwani, and T. Winograd. The PageRank citation engine: Bringing order to the web. Technical Report, 1999–0120, Computer Science Department, Stanford University, 1998.Google Scholar
  43. [488]
    G. Qi, C. Aggarwal, and T. Huang. Link prediction across networks by biased cross-network sampling. IEEE ICDE Conference, pp. 793–804, 2013.Google Scholar
  44. [510]
    M. Richardson and P. Domingos. Mining knowledge-sharing sites for viral marketing. ACM KDD Conference, pp. 61–70, 2002.Google Scholar
  45. [573]
    K. Subbian, C. Aggarwal, and J. Srivasatava. Content-centric flow mining for influence analysis in social streams. CIKM Conference, pp. 841–846, 2013.Google Scholar
  46. [575]
    J. Sun and J. Tang. A survey of models and algorithms for social influence analysis. Social Network Data Analytics, Springer, pp. 177–214, 2011.Google Scholar
  47. [576]
    Y. Sun, J. Han, C. Aggarwal, and N. Chawla. When will it happen?: relationship prediction in heterogeneous information networks. ACM International Conference on Web Search and Data Mining, pp. 663–672, 2012.Google Scholar
  48. [577]
    Y. Sun, R. Barber, M. Gupta, C. Aggarwal, and J. Han. Co-author relationship prediction in heterogeneous bibliographic networks. Advances in Social Networks Analysis and Mining (ASONAM), pp. 121–128, 2011.Google Scholar
  49. [589]
    J. Tang, J. Sun, C. Wang, and Z. Yang. Social influence analysis in large-scale networks. ACM KDD Conference, pp. 807–816, 2009.Google Scholar
  50. [591]
    J. Tang, S. Chang, C. Aggarwal, and H. Liu. Negative link prediction in social media. Web Search and Data Mining Conference, 2015.Google Scholar
  51. [602]
    M.-H. Tsai, C. Aggarwal, and T. Huang. Ranking in heterogeneous social media. Web Search and Data Mining Conference, 2014.Google Scholar
  52. [639]
    L. Xiang, Q. Yuan, S. Zhao, L. Chen, X. Zhang, Q. Yang, and J. Sun. Temporal recommendation on graphs via long-and short-term preference fusion. ACM KDD Conference, pp. 723–732, 2010.Google Scholar
  53. [640]
    Z. Xiang and U. Gretzel. Role of social media in online travel information search. Tourism Management, 31(2), pp. 179–188, 2010.CrossRefGoogle Scholar
  54. [663]
    J. Zhang, M. Ackerman, and L. Adamic. Expertise networks in online communities: structure and algorithms. World Wide Web Conference, pp. 221–230, 2007.Google Scholar
  55. [674]
    D. Zhou, O. Bousquet, T. Lal, J. Weston, and B. Scholkopf. Learning with local and global consistency. Advances in Neural Information Processing Systems, 16(16), pp. 321–328, 2004.Google Scholar
  56. [675]
    D. Zhou, J. Huang, and B. Scholkopf. Learning from labeled and unlabeled data on a directed graph. ICML Conference, pp. 1036–1043, 2005.Google Scholar
  57. [678]
    X. Zhu, Z. Ghahramani, and J. Lafferty. Semi-supervised learning using gaussian fields and harmonic functions. ICML Conference, pp. 912–919, 2003.Google Scholar
  58. [700]

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.IBM T.J. Watson Research CenterYorktown HeightsUSA

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