Abstract
BisoNets represent relations of information items as networks. The goal of BisoNet abstraction is to transform a large BisoNet into a smaller one which is simpler and easier to use, although some information may be lost in the abstraction process. An abstracted BisoNet can help users to see the structure of a large BisoNet, or understand connections between distant nodes, or discover hidden knowledge. In this paper we review different approaches and techniques to abstract a large BisoNet. We classify the approaches into two groups: preference-free methods and preference-dependent methods.
This chapter is a modified version of article “Review of Network Abstraction Techniques” in Workshop on Explorative Analytics of Information Networks, Sep 2009, Bled, Slovenia [1].
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Zhou, F., Mahler, S., Toivonen, H.: Review of Network Abstraction Techniques. In: Workshop on Explorative Analytics of Information Networks at ECML PKDD 2009, pp. 50–63 (2009)
Dubitzky, W., Kötter, T., Schmidt, O., Berthold, M.R.: Towards Creative Information Exploration Based on Koestler’s Concept of Bisociation. In: Berthold, M.R. (ed.) Bisociative Knowledge Discovery. LNCS (LNAI), pp. 11–32. Springer, Heidelberg (2012)
Toussaint, G.T.: The Relative Neighbourhood Graph of a Finite Planar Set. Pattern Recogn. 12(4), 261–268 (1980)
Jaromczyk, J., Toussaint, G.: Relative Neighborhood Graphs and Their Relatives. Proc. IEEE 80(9), 1502–1517 (1992)
Freeman, L.C.: Centrality in social networks: Conceptual clarification. Soc. Networks 1(3), 215–239 (1979)
Stephenson, K.Z.M.: Rethinking centrality: Methods and examples. Soc. Networks 11(1), 1–37 (1989)
Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge (1994)
Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)
Everett, M., Borgatti, S.P.: Ego network betweenness. Soc. Networks 27(1), 31–38 (2005)
Brandes, U.: A Faster Algorithm for Betweenness Centrality. J. Math. Sociol. 25(2), 163–177 (2001)
Freeman, L.C.: A Set of Measures of Centrality Based on Betweenness. Sociometry 40, 35–41 (1977)
Friedkin, N.E.: Theoretical Foundations for Centrality Measures. Am. J. Sociol. 96(6), 1478–1504 (1991)
Gert, S.: The centrality index of a graph. Psychometrika 31(4), 581–603 (1966)
Bonacich, P.: Factoring and weighting approaches to status scores and clique identification. J. Math. Sociol. 2(1), 113–120 (1972)
Lawrence, P., Sergey, B., Rajeev, M., Terry, W.: The PageRank Citation Ranking: Bringing Order to the Web. Technical report, Stanford Digital Library Technologies Project (1998)
Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. Comput. Netw. ISDN Syst. 30, 107–117 (1998)
Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J. ACM 46(5), 604–632 (1999)
Li, L., Shang, Y., Zhang, W.: Improvement of HITS-based algorithms on web documents. In: WWW 2002: Proc. 11th International Conf. on World Wide Web, pp. 527–535. ACM, New York (2002)
Haveliwala, T.H.: Topic-Sensitive PageRank. In: WWW 2002: Proc. 11th International Conf. World Wide Web, pp. 517–526. ACM, New York (2002)
Birnbaum, Z.W.: On the importance of different components in a multicomponent system. In: Multivariate Analysis - II, pp. 581–592. Academic Press, New York (1969)
Hong, J., Lie, C.: Joint reliability-importance of two edges in an undirected network. IEEE Trans. Reliab. 42, 17–23, 33 (1993)
Fjällström, P.O.: Algorithms for graph partitioning: A Survey. Linköping Electronic Atricles in Computer and Information Science. Linköping University Electronic Press, Linköping (1998)
Elsner, U.: Graph Partitioning - A Survey. Technical Report SFB393/97-27, Technische Universität Chemnitz (1997)
Pothen, A., Simon, H.D., Liou, K.P.: Partitioning Sparse Matrices with Eigenvectors of Graphs. SIAM J. Matrix Anal. Appl. 11(3), 430–452 (1990)
Hendrickson, B., Leland, R.: An improved spectral graph partitioning algorithm for mapping parallel computations. SIAM J. Sci. Comput. 16(2), 452–469 (1995)
Miller, G.L., Teng, S.H., Thurston, W., Vavasis, S.A.: Geometric Separators for Finite-Element Meshes. SIAM J. Sci. Comput. 19(2), 364–386 (1998)
Berger, M.J., Bokhari, S.H.: A Partitioning Strategy for Nonuniform Problems on Multiprocessors. IEEE Trans. Comput. 36(5), 570–580 (1987)
Karypis, G., Kumar, V.: A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs. SIAM J. Sci. Comput. 20, 359–392 (1998)
Hendrickson, B., Leland, R.: A Multi-Level Algorithm For Partitioning Graphs. In: Proc. 1995 ACM/IEEE Conf. Supercomputing (CDROM). ACM, New York (1995)
Newman, M.E.J.: Detecting community structure in networks. Eur. Phy. J. B - Condensed Matter and Complex Systems 38(2), 321–330 (2004)
Kernighan, B.W., Lin, S.: An Efficient Heuristic Procedure for Partitioning Graphs. Bell Sys. Tech. J. 49(1), 291–307 (1970)
Fiduccia, C.M., Mattheyses, R.M.: A Linear-Time Heuristic for Improving Network Partitions. In: DAC 1982: P. 19th Conf. Des. Autom., pp. 175–181. ACM, New York (1982)
Diekmann, R., Monien, B., Preis, R.: Using Helpful Sets to Improve Graph Bisections. In: Interconnection Networks and Mapping and Scheduling Parallel Computations, pp. 57–73. American Mathematical Society, USA (1995)
Scott, J.: Social Network Analysis: A Handbook. SAGE Publications, UK (2000)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004)
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. USA 99(12), 7821–7826 (2002)
Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., Parisi, D.: Defining and identifying communities in networks. Proc. Natl. Acad. Sci. USA 101, 2658–2663 (2004)
Wu, F., Huberman, B.: Finding Communities in Linear Time: A Physics Approach. Eur. Phys. J. B - Condensed Matter and Complex Systems 38, 331–338 (2004)
Dehaspe, L., Toivonen, H., King, R.D.: Finding Frequent Substructures in Chemical Compounds. In: Agrawal, R., Stolorz, P., Piatetsky-Shapiro, G. (eds.) 4th International Conf. Knowl. Disc. Data Min., USA, pp. 30–36. AAAI Press (1998)
Holder, L.B., Cook, D.J., Djoko, S.: Substructure Discovery in the SUBDUE System. In: Proc. AAAI Workshop Knowl. Disc. Databases, pp. 169–180. AAAI, Menlo Park (1994)
Cook, D.J., Holder, L.B.: Substructure Discovery Using Minimum Description Length and Background Knowledge. J. Artif. Intell. Res. 1, 231–255 (1994)
Yoshida, K., Motoda, H.: CLIP: Concept Learning from Inference Patterns. Artif. Intell. 75(1), 63–92 (1995)
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) Proc. 20th International Conf. Very Large Data Bases, VLDB 1994, pp. 487–499. Morgan Kaufmann, San Francisco (1994)
Inokuchi, A., Washio, T., Motoda, H.: An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 13–23. Springer, Heidelberg (2000)
Kuramochi, M., Karypis, G.: Frequent Subgraph Discovery. In: Proc. 2001 IEEE International Conf. Data Min., ICDM 2001, pp. 313–320. IEEE Computer Society, Washington, DC (2001)
Kuramochi, M., Karypis, G.: An Efficient Algorithm for Discovering Frequent Subgraphs. IEEE Trans. on Knowl. and Data Eng. 16(9), 1038–1051 (2004)
Yan, X., Han, J.: gSpan: Graph-Based Substructure Pattern Mining. In: Proceedings of the 2002 IEEE International Conf. Data Min., pp. 721–724. IEEE Computer Society, Washington, DC (2002)
Yan, X., Han, J.: CloseGraph: Mining Closed Frequent Graph Patterns. In: KDD 2003: Proc. 9th ACM SIGKDD International Conf. Knowl. Disc. Data Min., pp. 286–295. ACM, New York (2003)
Huan, J., Wang, W., Prins, J., Yang, J.: SPIN: Mining Maximal Frequent Subgraphs from Graph Databases. In: KDD 2004: Proc. 10th ACM SIGKDD International Conf. Knowl. Disc Data Min., pp. 581–586. ACM, New York (2004)
Bringmann, B., Nijssen, S.: What Is Frequent in a Single Graph? In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 858–863. Springer, Heidelberg (2008)
Fiedler, M., Borgelt, C.: Subgraph Support in a Single Large Graph. In: ICDMW 2007: Proc. 7th IEEE International Conf. Data Min. Workshops, pp. 399–404. IEEE Computer Society, Washington, DC (2007)
Fiedler, M., Borgelt, C.: Support Computation for Mining Frequent Subgraphs in a Single Graph. In: Proc. 5th Int. Workshop on Mining and Learning with Graphs, MLG 2007, Florence, Italy, pp. 25–30 (2007)
Kuramochi, M., Karypis, G.: Finding Frequent Patterns in a Large Sparse Graph. Data Min. Knowl. Disc. 11(3), 243–271 (2005)
Grötschel, M., Monma, C.L., Stoer, M.: Design of survivable networks. In: Handbooks in Operations Research and Management Science, vol. 7, pp. 617–672 (1995)
Ramakrishnan, C., Milnor, W.H., Perry, M., Sheth, A.P.: Discovering Informative Connection Subgraphs in Multi-relational Graphs. SIGKDD Explor. Newsl. 7(2), 56–63 (2005)
Faloutsos, C., McCurley, K.S., Tomkins, A.: Fast Discovery of Connection Subgraphs. In: KDD 2004: Proc. 10th ACM SIGKDD International Conf. Knowl. Disc. Data Min., pp. 118–127. ACM, New York (2004)
Tong, H., Faloutsos, C.: Center-Piece Subgraphs: Problem Definition and Fast Solutions. In: KDD 2006: Proc. 12th ACM SIGKDD International Conf. Knowl. Disc. Data Min., pp. 404–413. ACM, New York (2006)
Sevon, P., Eronen, L., Hintsanen, P., Kulovesi, K., Toivonen, H.: Link Discovery in Graphs Derived from Biological Databases. In: Leser, U., Naumann, F., Eckman, B. (eds.) DILS 2006. LNCS (LNBI), vol. 4075, pp. 35–49. Springer, Heidelberg (2006)
Hintsanen, P., Toivonen, H.: Finding reliable subgraphs from large probabilistic graphs. Data Min. Knowl. Discov. 17, 3–23 (2008)
Raedt, L.D., Kimmig, A., Toivonen, H.: ProbLog: A Probabilistic Prolog and its Application in Link Discovery. In: Proc. 20th International Joint Conf. Artif. Intel., pp. 2468–2473. AAAI Press, Menlo Park (2007)
Raedt, L., Kersting, K., Kimmig, A., Revoredo, K., Toivonen, H.: Compressing probabilistic Prolog programs. Mach. Learn. 70(2-3), 151–168 (2008)
Lin, S., Chalupsky, H.: Unsupervised Link Discovery in Multi-relational Data via Rarity Analysis. In: ICDM 2003: Proc. 3rd IEEE International Conf. Data Min., p. 171. IEEE Computer Society, Washington, DC (2003)
White, S., Smyth, P.: Algorithms for Estimating Relative Importance in Networks. In: KDD 2003: Proc. 9th ACM SIGKDD International Conf. Knowl. Disc. Data Min., pp. 266–275. ACM, New York (2003)
Jeh, G., Widom, J.: Scaling Personalized Web Search. In: WWW 2003: Proc. 12th International Conf. World Wide Web, pp. 271–279. ACM, New York (2003)
Forgaras, D., Rácz, B., Csalogány, K., Sarlós, T.: Towards Scaling Fully Personalized PageRank: Algorithms, Lower Bounds and Experiments. Internet Mathematics 2(3), 335–358 (2005)
Ullmann, J.R.: An Algorithm for Subgraph Isomorphism. J. ACM 23(1), 31–42 (1976)
Cordella, L.P., Foggia, P., Sansone, C., Vento, M.: A (Sub)Graph Isomorphism Algorithm for Matching Large Graphs. IEEE Trans. Pattern Anal. 26(10), 1367–1372 (2004)
Shasha, D., Wang, J.T.L., Giugno, R.: Algorithmics and Applications of Tree and Graph Searching. In: PODS 2002: Proc. 21st ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 39–52. ACM, New York (2002)
Yan, X., Yu, P.S., Han, J.: Graph Indexing: A Frequent Structure-based Approach. In: SIGMOD 2004: Proc. 2004 ACM SIGMOD International Conf. Management of Data, pp. 335–346. ACM, New York (2004)
He, H., Singh, A.K.: Closure-Tree: An Index Structure for Graph Queries. In: ICDE 2006: Proc. 22nd International Conf. Data Eng., p. 38. IEEE Computer Society, Los Alamitos (2006)
Tian, Y., Mceachin, R.C., Santos, C., States, D.J., Patel, J.M.: SAGA: a subgraph matching tool for biological graphs. Bioinformatics 23(2), 232–239 (2007)
Bunke, H., Shearer, K.: A graph distance metric based on the maximal common subgraph. Pattern Recogn. Lett. 19(3-4), 255–259 (1998)
Fernández, M.-L., Valiente, G.: A graph distance metric combining maximum common subgraph and minimum common supergraph. Pattern Recogn. Lett. 22(6-7), 753–758 (2001)
Yan, X., Yu, P.S., Han, J.: Substructure Similarity Search in Graph Databases. In: SIGMOD 2005: Proc. 2005 ACM SIGMOD International Conf. Management of Data, pp. 766–777. ACM, New York (2005)
Yan, X., Zhu, F., Han, J., Yu, P.S.: Searching Substructures with Superimposed Distance. In: ICDE 2006: Proc. 22nd International Conf. Data Eng. IEEE Computer Society, Washington, DC (2006)
Tian, Y., Patel, J.M.: TALE: A Tool for Approximate Large Graph Matching. In: Proc. 2008 IEEE 24th International Conf. Data Eng., pp. 963–972. IEEE Computer Society, Los Alamitos (2008)
Williams, D., Huan, J., Wang, W.: Graph Database Indexing Using Structured Graph Decomposition. In: Proc. 2007 IEEE 23rd International Conf. Data Eng, pp. 976–985. IEEE Computer Society Press, Los Alamitos (2007)
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Zhou, F., Mahler, S., Toivonen, H. (2012). Review of BisoNet Abstraction Techniques. In: Berthold, M.R. (eds) Bisociative Knowledge Discovery. Lecture Notes in Computer Science(), vol 7250. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31830-6_12
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