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A Survey on Proximity Measures for Social Networks

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 7538)

Abstract

Measuring proximity in a social network is an important task, with many interesting applications, including person search and link prediction. Person search is the problem of finding, by means of keyword search, relevant people in a social network. In user-centric person search, the search query is issued by a person participating in the social network and the goal is to find people that are relevant not only to the keywords, but also to the searcher herself. Link prediction is the task of predicting new friendships (links) that are likely to be added to the network. Both of these tasks require the ability to measure proximity of nodes within a network, and are becoming increasingly important as social networks become more ubiquitous.

This chapter surveys recent work on scoring measures for determining proximity between nodes in a social network. We broadly identify various classes of measures and discuss prominent examples within each class. We also survey efficient implementations for computing or estimating the values of the proximity measures.

Keywords

  • Social Network
  • Short Path
  • Graphic Processing Unit
  • Ranking Function
  • Online Social Network

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.

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References

  1. Adamic, L., Adar, E.: How to search a social network. In: VLDB, pp. 217–225 (1987)

    Google Scholar 

  2. Avrachenkov, K., Litvak, N., Nemirovsky, D., Osipova, N.: Monte carlo methods in pagerank computation: When one iteration is sufficient. SIAM J. Numer. Anal. 45(2), 890–904 (2007)

    CrossRef  MathSciNet  MATH  Google Scholar 

  3. Bahmani, B., Chakrabarti, K., Xin, D.: Fast personalized pagerank on mapreduce. In: SIGMOD Conference, pp. 973–984 (2011)

    Google Scholar 

  4. Bahmani, B., Chowdhury, A., Goel, A.: Fast incremental and personalized pagerank. PVLDB 4(3), 173–184 (2010)

    Google Scholar 

  5. Balan, A.O., Traldi, L.: Preprocessing minpaths for sum of disjoint products. IEEE Trans. Reliability 52(3), 289–295 (2003)

    CrossRef  Google Scholar 

  6. Barabasi, A.L., Jeong, H., Neda, Z., Ravasz, E., Schubert, A., Vicsek, T.: Evolution of the social network of scientific collaborations. Physica A 311(3-4), 590–614 (2002)

    CrossRef  MathSciNet  MATH  Google Scholar 

  7. Brander, A., Sinclair, M.: A comparative study of k-shortest path algorithms. In: Proc. 11th UK Performance Engineering Workshop for Computer and Telecommunications Systems (1995)

    Google Scholar 

  8. Carmel, D., Zwerdling, N., Guy, I., Ofek-Koifman, S., Har’el, N., Ronen, I., Uziel, E., Yogev, S., Chernov, S.: Personalized social search based on the user’s social network. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009, pp. 1227–1236. ACM, New York (2009), http://doi.acm.org/10.1145/1645953.1646109

    CrossRef  Google Scholar 

  9. Cohen, S., Kimelfeld, B., Koutrika, G., Vondrák, J.: On principles of egocentric person search in social networks. In: First International Workshop on Searching and Integrating New Web Data Sources, Seattle, Washington (2011)

    Google Scholar 

  10. Davies, D., Barber, D.: Communication Networks for Computers. John Wiley, London (1973)

    Google Scholar 

  11. Dijkstra, E.W.: A note on two problems in connexion with graphs. Numerische Mathematik 1, 269–271 (1959)

    CrossRef  MathSciNet  MATH  Google Scholar 

  12. Dotson, W.P., Gobien, J.O.: A new analysis technique for probabilistic graphs. IEEE Trans. Circuits and Systems 26(10), 855–865 (1979)

    CrossRef  MathSciNet  Google Scholar 

  13. Doyle, P., Snell, J.: Random walks and electical networks. The Mathematical Association of America (1984)

    Google Scholar 

  14. Eppstein, D.: Finding the k shortest paths. SIAM J. Comput. 28(2), 652–673 (1998)

    CrossRef  MathSciNet  MATH  Google Scholar 

  15. Esfandiar, P., Bonchi, F., Gleich, D.F., Greif, C., Lakshmanan, L.V.S., On, B.-W.: Fast Katz and Commuters: Efficient Estimation of Social Relatedness in Large Networks. In: Kumar, R., Sivakumar, D. (eds.) WAW 2010. LNCS, vol. 6516, pp. 132–145. Springer, Heidelberg (2010)

    CrossRef  Google Scholar 

  16. Fishman, G.S.: A comparison of four monte carlo methods for estimating the probability of s-t connectedness. IEEE Trans. Reliability 35(2), 145–155 (1986)

    CrossRef  MathSciNet  MATH  Google Scholar 

  17. Floyd, R.W.: Algorithm 97: Shortest path. Communications of the ACM 5(6), 345 (1962)

    CrossRef  Google Scholar 

  18. Fogaras, D., Rácz, B.: Towards Scaling Fully Personalized PageRank. In: Leonardi, S. (ed.) WAW 2004. LNCS, vol. 3243, pp. 105–117. Springer, Heidelberg (2004)

    CrossRef  Google Scholar 

  19. Foster, K.C., Muth, S.Q., Potterat, J.J., Rothenberg, R.B.: A faster katz status score algorithm. Computational and Mathematical Organization Theory 7, 275–285 (2001)

    CrossRef  Google Scholar 

  20. Frank, H., Frisch, I.: Communication, Transmission and Transportation Networks. Addison Wesley, Reading (1971)

    MATH  Google Scholar 

  21. Gao, J., Jim, R., Zhou, J., Yu, J.X., Jiang, X., Wang, T.: Relational approach for shortest path discovery over large graphs. PVLDB 5(4), 358–369 (2012)

    Google Scholar 

  22. Goldberg, A., Harrelsons, C.: Computing the shortest path: A* search meets graph theory. In: SODA (2005)

    Google Scholar 

  23. Gubichev, A., Bedathur, S.J., Seufert, S., Weikum, G.: Fast and accurate estimation of shortest paths in large graphs. In: CIKM, pp. 499–508 (2010)

    Google Scholar 

  24. Guy, I., Perer, A., Daniel, T., Greenshpan, O., Turbahn, I.: Guess who?: enriching the social graph through a crowdsourcing game. In: Proceedings of the 2011 Annual Conference on Human Factors in Computing Systems, CHI 2011, pp. 1373–1382. ACM, New York (2011), http://doi.acm.org/10.1145/1978942.1979145

    Google Scholar 

  25. Hadjiconstantinou, E., Christofides, N.: An efficient implementation of an algorithm for finding k shortest simple paths. Networks 34, 88–101 (1999)

    CrossRef  MathSciNet  MATH  Google Scholar 

  26. Jeh, G., Widom, J.: Scaling personalized web search. In: Proceedings of the 12th International Conference on World Wide Web, WWW 2003, pp. 271–279. ACM, New York (2003), http://doi.acm.org/10.1145/775152.775191

    Google Scholar 

  27. Katoh, N., Ibaraki, T., Mine, H.: An efficient algorithm for k shortest simple paths. Networks 12 (1982)

    Google Scholar 

  28. Katz, G.J., Kider Jr., J.T.: All-pairs shortest-paths for large graphs on the gpu. In: Graphics Hardware, pp. 47–55 (2008)

    Google Scholar 

  29. Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)

    CrossRef  MATH  Google Scholar 

  30. Koren, Y., North, S.C., Volinsky, C.: Measuring and extracting proximity graphs in networks. TKDD 1(3) (2007)

    Google Scholar 

  31. Liben-Nowell, D., Kleinberg, J.M.: The link-prediction problem for social networks. In: CIKM (2003)

    Google Scholar 

  32. Liben-Nowell, D., Kleinberg, J.M.: The link-prediction problem for social networks. JASIST 58(7), 1019–1031 (2007)

    CrossRef  Google Scholar 

  33. Manning, C.D., Raghavan, P., Schtze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)

    CrossRef  MATH  Google Scholar 

  34. Mitzenmacher, M.: A brief history of generative models for power law and lognormal distributions. Internet Mathematics 1(2), 226–251 (2004)

    CrossRef  MathSciNet  MATH  Google Scholar 

  35. Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Tech. rep., Stanford University (1998)

    Google Scholar 

  36. Potamias, M., Bonchi, F., Castillo, C., Gionis, A.: Fast shortest path distance estimation in large networks. In: CIKM (2009)

    Google Scholar 

  37. Rapoport, A.: Spread of information through a population with socio-structural bias i: Assumption of transitivity. Bulletin of Mathematical Biophysics 15(4), 523–533 (1953)

    CrossRef  MathSciNet  Google Scholar 

  38. Sarlós, T., Benczúr, A.A., Csalogány, K., Fogaras, D., Ráz, B.: To randomize or not to randomize: space optimal summaries for hyperlink analysis. In: World Wide Web, pp. 297–306 (2006)

    Google Scholar 

  39. Song, H.H., Cho, T.W., Dave, V., Zhang, Y., Qiu, L.: Scalable proximity estimation and link prediction in online social networks. In: IMC (2009)

    Google Scholar 

  40. Terruggia, R.: A comparison of four monte carlo methods for estimating the probability of s-t connectedness. Thesis. Università degli Studi di Torino (2010)

    Google Scholar 

  41. Valiant, L.G.: The complexity of enumeration and reliability problems. SIAM J. Comput. 8(3), 410–421 (1979)

    CrossRef  MathSciNet  MATH  Google Scholar 

  42. Vieira, M.V., Fonseca, B.M., Damazio, R., Golgher, P.B., de Castro Reis, D., Ribeiro-Neto, B.A.: Efficient search ranking in social networks. In: CIKM, pp. 563–572 (2007)

    Google Scholar 

  43. Wang, C., Satuluri, V., Parthasarathy, S.: Local probabilistic models for link prediction. In: ICDM, pp. 322–331 (2007)

    Google Scholar 

  44. Xiang, R., Neville, J., Rogati, M.: Modeling relationship strength in online social networks. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, pp. 981–990. ACM, New York (2010), http://doi.acm.org/10.1145/1772690.1772790

    CrossRef  Google Scholar 

  45. Xiao, Y., Wu, W., Pei, J., Wang, W., He, Z.: Efficiently indexing shortest paths by exploiting symmetry in graphs. In: EDBT (2009)

    Google Scholar 

  46. Yen, J.Y.: Finding the k shortest loopless paths in a network. Management Science 17 (1971)

    Google Scholar 

  47. Yen, J.Y.: Another algorithm for finding the k shortest loopless network paths. In: Proc. of 41st Mtg. Operations Research Society of America 20 (1972)

    Google Scholar 

  48. Zhao, X., Salaa, A., Wilson, C., Zheng, H., Zhao, B.Y.: Orion: Shortest path estimation for large social graphs. In: Proceedings of the 3rd Workshop on Online Social Networks, WOSN (2010)

    Google Scholar 

  49. Zhao, X., Salaa, A., Zheng, H., Zhao, B.Y.: Efficient shortest paths on massive social graphs. In: Proceedings of 7th International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom (2011)

    Google Scholar 

  50. Zhou, T., Lü, L., Zhang, Y.C.: Predicting missing links via local information. The European Physical Journal B—Condensed Matter and Complex Systems 71(4), 623–630 (2009)

    CrossRef  MATH  Google Scholar 

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Cohen, S., Kimelfeld, B., Koutrika, G. (2012). A Survey on Proximity Measures for Social Networks. In: Ceri, S., Brambilla, M. (eds) Search Computing. Lecture Notes in Computer Science, vol 7538. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34213-4_13

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  • DOI: https://doi.org/10.1007/978-3-642-34213-4_13

  • Publisher Name: Springer, Berlin, Heidelberg

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