Analyzing knowledge communities using foreground and background clusters

ACM Transactions on Knowledge Discovery from Data

Volumn 4, Issue 2, 2010, Pages

  Kandylas, Vasileios ^a   Upham, S Phineas ^b   Ungar, Lyle H ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

Citation analysis; Clustering; Community evolution; Knowledge communities; Text mining

Indexed keywords

CITATION ANALYSIS; CLUSTER DOCUMENTS; CLUSTERING METHODS; COMMUNITY EVOLUTION; DIFFUSE BACKGROUND; KNOWLEDGE COMMUNITIES; PREDICTIVE MODELS; SCIENTIFIC KNOWLEDGE; TEXT MINING;

BIOLOGY;

TECHNICAL WRITING;

EID: 77953175973     PISSN: 15564681     EISSN: 1556472X     Source Type: Journal    
DOI: 10.1145/1754428.1754430     Document Type: Article

References (73)

1. ALLISON, P. AND WATERMAN, R. 2002. Fixed-Effects Negative Binomial Regression Models. Sociol. Method. 32, 1, 247-265.
2. BANERJEE, A.,DHILLON, I.,GHOSH, J.,MERUGU, S., AND MODHA, D. S. 2004. A generalized maximum entropy approach to Bregman co-clustering andmatrix approximation. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'04). ACM, New York, NY, 509-514.
3. BLEI, D., NG, A., AND JORDAN, M. 2003. Latent Dirichlet allocation. J. Mach. Learn. Resear. 3, 993-1022.
4. BLEI, D. M. AND LAFFERTY, J. D. 2006. Dynamic topic models. In Proceedings of the 23rd International Conference on Machine Learning (ICML'06). ACM, New York, NY, 113-120.
5. BONACICH, P. 1978. Using Boolean algebra to analyze overlapping memberships. Sociol. Method. 9, 101-115.
6. BORGATTI, S. AND EVERETT, M. 1997. Network analysis of 2-mode data. Soc. Netw. 19, 3, 243-269.
7. BRAAM, R. R.,MOED, H. F., AND VAN RAAN, A. F. J. 1991. Mapping of science by combined co-citation and word analysis. ii: Dynamical aspects. J. Amer. Soci. Inform. Sci. 42, 4, 252-266.
8. CRANE, D. 1972. Invisible Colleges: Diffusion of Knowledge in Scientific Communities. University of Chicago Press, Chicago.
9. DASGUPTA, S. AND SCHULMAN, L. J. 2000. A two-round variant of em for gaussian mixtures. In Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence (UAI'00). Morgan Kaufmann Publishers Inc., San Francisco, CA, 152-159.
10. DEMPSTER, A., LAIRD, N., AND RUBIN, D. 1977. Maximum likelihood from incomplete data via the EM algorithm. J. Royal Statist. Soc. 39, 1-38.
11. DHILLON, I. AND MODHA, D. 2001. Concept decompositions for large sparse text data using clustering. Mach. Learn. 42, 1 (Jan), 143-175
12. DHILLON, I. S. 2001. Co-clustering documents and words using bipartite spectral graph partitioning. In Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'01). ACM Press, New York, NY, 269-274.
13. DHILLON, I. S. AND GUAN, Y. 2003. Information theoretic clustering of sparse co-occurrence data. In Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM'03). IEEE Computer Society, Los Alamitos, CA, 517.
14. DHILLON, I. S.,GUAN, Y., AND KULIS, B. 2004. Kernel k-means: spectral clustering and normalized cuts. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'04). ACM, New York, NY, 551-556.
15. DHILLON, I. S., MALLELA, S., AND MODHA, D. S. 2003. Information-theoretic co-clustering. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'03). ACM Press, New York, NY, 89-98.
16. DOREIAN, P. 1979. On the delineation of small group structure. In Classifying Social Data, H. C. Hudson, Ed. Jossey-Bass, San Francisco, CA, 215-230.
17. ESTER, M., KRIEGEL, H., SANDER, J., AND XU, X. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining. AAAI Press, Portland, OR, 226-231.
18. EVERETT, M. AND BORGATTI, S. 1993. An extension of regular colouring of graphs to digraphs, networks and hypergraphs. Soc. Netw. 15, 23, 7-254.
19. FERN, X. Z. AND BRODLEY, C. E. 2003. Random projection for high dimensional data clustering: A cluster ensemble approach. In Proceedings of the 20th International Conference of Machine Learning. AAAI Press, Washington, DC, 186-193.
20. FLAKE, G. W., LAWRENCE, S., AND GILES, C. L. 2000. Efficient identification of Web communities. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'00). ACM, New York, NY, 150-160.
21. FREEMAN, L. 2003. Finding social groups: A meta-analysis of the southern women data. In Dynamic Social Network Modeling and Analysis: Workshop Summary and Papers. National Academy Press, Washington, DC, 39-97.
22. FRENKEN, K.,HÖLZL, W., AND VOR, F. 2005. The citation impact of research collaborations: the case of European biotechnology and appliedmicrobiology (1988-2002). J. Engin. Tech.Manag. 22, 1-2, 9-30.
23. GETOOR, L., FRIEDMAN, N., KOLLER, D., AND TASKAR, B. 2001. Learning probabilistic models of relational structure. In Proceedings of the 18th International Conference on Machine Learning (ICML'01). Morgan Kaufmann Publishers Inc., San Francisco, CA, 170-177.
24. GIBSON, D.,KLEINBERG, J., AND RAGHAVAN, P. 1998. InferringWeb Communities from Link Topology. ACM, New York, NY.
25. GILES, C. L., BOLLACKER, K., AND LAWRENCE, S. 1998. CiteSeer: An automatic citation indexing system. In Proceedings of the 3rd ACM Conference on Digital Libraries. I. Witten, R. Akscyn, and F. M. Shipman III, Eds. ACM Press, 89-98.
26. GREENE, W. H. 1994. Accounting for excess zeros and sample selection in Poisson and negative binomial regression models. Working Papers 94-110 Leonard N. Stern School of Business, Department of Economics, New York University.
27. GRIFFITH, B., SMALL, H., STONEHILL, J., AND DEY, S. 1974. The Structure of Scientific Literatures II: Toward a Macro-and Microstructure for Science. Sci. Stud. 4, 4, 339-365.
28. GRIFFITHS, T. AND STEYVERS, M. 2004. Finding scientific topics. Proc. Nat. Acad. Sci. 101, 5228-5235.
29. GUHA, S., MEYERSON, A., MISHRA, N., MOTWANI, R., AND O'CALLAGHAN, L. 2003. Clustering Data Streams: Theory and Practice. IEEE Trans. Knowl. Data Engin. 15, 3, 515-528.
30. GUHA, S., RASTOGI, R., AND SHIM, K. 2001. Cure: an efficient clustering algorithm for large databases. Inform. Syst. 26, 1, 35-58.
31. HAGE, J. 1999. Organizational Innovation And Organizational Change. Ann. Rev. Sociol. 25, 1, 597-622.
32. HAGE, J. AND MEEUS, M. 2006. Innovation, Science, and Institutional Change. Oxford University Press, Oxford, UK.
33. HANNAN,M. AND FREEMAN, J. 1977. The Population Ecology of Organizations. Amer. J. Sociol. 82, 5, 929.
34. HAUSMAN, J., HALL, B., AND GRILICHES, Z. 1984. Econometric Models for Count Data with an Application to the Patents-R & D Relationship. Econometrica 52, 4, 909-938.
35. HE, Y. AND HUI, S. C. 2002. Mining a web citation database for author co-citation analysis. Inf. Process. Manage. 38, 4, 491-508. (Pubitemid 34232112)
36. HOPCROFT, J.,KHAN, O.,KULIS, B., AND SELMAN, B. 2003. Natural communities in large linked networks. In Proceedings of the 9th ACMSIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'03). ACM Press, New York, NY, 541-546.
37. HUANG, Q., DOM, B., STEELE, D., ASHLEY, J., AND NIBLACK, W. 1995. Foreground/background segmentation of color images by integration ofmultiple cues. In Proceedings of the IEEE Intenational Conference on Image Processing 1, 246-249.
38. JAIN, A. K., MURTY, M. N., AND FLYNN, P. J. 1999. Data clustering: a review. ACM Comput. Surv. 31, 3, 264-323.
39. JANSSENS, F., GLÄNZEL, W., AND MOOR, B. D. 2007. Dynamic hybrid clustering of bioinformatics by incorporating text mining and citation analysis. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'07). ACM Press, New York, NY, 360-369.
40. KANDYLAS, V., UPHAM, S. P., AND UNGAR, L. H. 2007. Finding cohesive clusters for analyzing knowledge communities. In Proceedings of IEEE International Conference on Data Mining. IEEE Computer Society, Los Alamitos, CA, 203-212.
41. KEARNS,M. J.,MANSOUR, Y., ANDNG, A. Y. 1997. An information-theoretic analysis of hard and soft assignment methods for clustering. In Procedings of the Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann, 282-293.
42. KOSTOFF, R., DEL RIO, J., HUMENIK, J., GARCIA, E., AND RAMIREZ, A. 2001. Citation mining: Integrating text mining and bibliometrics for research user profiling. J. Amer. Soci. Inform. Sci. Techn. 52, 13, 1148-1156.
43. LARSEN, B. AND AONE, C. 1999. Fast and effective text mining using linear-time document clustering. In Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'99). ACM Press, 16-22.
44. LEE, J., VICENTE, K., CASSANO, A., AND SHEARER, A. 2003. Can scientific impact be judged prospectively? A bibliometric test of Simonton's model of creative productivity. Scientometrics 56, 2, 223-232.
45. LESKOVEC, J.,KLEINBERG, J., AND FALOUTSOS, C. 2005. Graphs over time: densification laws, shrinking diameters and possible explanations. In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'05). ACM, New York, NY, 177-187.
46. LEY, M. 2002. The DBLP computer science bibliography: Evolution, research issues, perspectives. In Proceedings of the 9th International Symposium on String Processing and Information Retrieval (SPIRE'02). Springer-Verlag, Berlin, Germany, 1-10.
47. MARCH, J. 1991. Exploration and Exploitation in Organizational Learning. Organiz. Sci. 2, 1, 71-87.
48. MATSUMURA, N., OHSAWA, Y., AND ISHIZUKA, M. 2001. Discovery of emerging topics between communities on WWW. In Proceedings of the 1st Asia-Pecific Conference on Web Intelligence. Lecture Notes in Computer Science. Vol.2198, 473-482.
49. MCCULLAGH, P. AND NELDER, J. 1989. Generalized Linear Models. Chapman & Hall/CRC, Boca Raton, FL.
50. MCGANN, A. 2002. The Advantages of Ideological Cohesion a Model of Constituency Representation and Electoral Competition in Multi-Party Democracies. J. Theoret. Pol. 14, 1, 37-70.
51. MCGOVERN, A., FRIEDLAND, L., HAY, M., GALLAGHER, B., FAST, A., NEVILLE, J., AND JENSEN, D. 2003. Exploiting relational structure to understand publication patterns in high-energy physics. SIGKDD Explor. Newsl. 5, 2, 165-172.
52. MOODY, J. 2001. Peer influence groups: identifying dense clusters in large networks. Soc. Netw. 23, 4, 261-283.
53. NIGAM, K., MCCALLUM, A., THRUN, S., AND MITCHELL, T. M. 2000. Text classification from labeled and unlabeled documents using EM. Mach. Learn. 39, 2/3, 103-134.
54. PANTEL, P. AND LIN, D. 2002. Document clustering with committees. In Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'02). ACM Press, New York, NY, 199-206.
55. PERLICH, C.,PROVOST, F. J., ANDMACSKASSY, S. A. 2003. Predicting citation rates for physics papers: constructing features for an ordered probit model. SIGKDD Explor. 5, 154.
56. PFEFFER, J. 1993. Barriers to the Advance of Organizational Science: Paradigm Development as a Dependent Variable. Acad. Manag. Rev. 18, 4, 599-620.
57. POPESCUL, A., FLAKE, G., LAWRENCE, S., UNGAR, L., AND GILES, C. 2000. Clustering and identifying temporal trends in document databases. In Proceedings of the IEEE Conferece on Advances in Digital Libraries. IEEE Computer Society, Los Alamitos, CA, 173-182.
58. SAVAKIS, A. 1998. Adaptive document image thresholding using foreground and background clustering. In Proceedings of the International Conference on Image Processing (ICIP'98). IEEE Computer Society, Chicago, IL.
59. SHI, J. AND MALIK, J. 1997. Normalized cuts and image segmentation. In Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR'97). IEEE Computer Society, Washington, DC, 731.
60. SLONIM, N., FRIEDMAN, N., AND TISHBY, N. 2001. Agglomerative multivariate information bottleneck. Advanc. Neur. Inform. Process. Syst.
61. SMALL, H. 2003. Paradigms, citations, and maps of science: A personal history. J. Amer. Soci. Inform. Sci. Tech. 54, 5, 394-399.
62. SMALL, H. AND CRANE, D. 1979. Specialties and disciplines in science and social science: An examination of their structure using citation indexes. Scientometrics 1, 5, 445-461.
63. SMALL, H. AND SWEENEY, E. 1985a. Clustering the science citation index using co-citations i: a comparison of methods. Scientometrics 7, 391-409.
64. SMALL, H. AND SWEENEY, E. 1985b. Clustering the science citation index using co-citations ii: Mapping science. Scientometrics 8, 321-340.
65. STEINBACH, M.,KARYPIS, G., AND KUMAR, V. 2000. A comparison of document clustering techniques. In Proceedings of the KDD Workshop on Text Mining 34, 35.
66. STREHL, A. AND GHOSH, J. 2002. Cluster ensembles-a knowledge reuse framework for combining multiple partitions. J. Mach. Learn. Resear. 3, 583-617.
67. SULLIVAN, D.,WHITE, D. H., AND BARBONI, E. J. 1977. Co-citation analyses of science: An evaluation. Soc. Stud. Sci. 7, 2, 223-240.
68. SUN, J., FALOUTSOS, C., PAPADIMITRIOU, S., AND YU, P. S. 2007. GraphScope: parameter-free mining of large time-evolving graphs. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'07). ACM Press, New York, NY, 687-696. (Pubitemid 350229254)
69. TANTIPATHANANANDH, C., BERGER-WOLF, T., AND KEMPE, D. 2007. A framework for community identification in dynamic social networks. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, New York, NY, 717-726.
70. UPHAM, S. P. 2006. Communities of innovation. Ph.D. thesis, University of Pennsylvania.
71. WANG, P. 2003. A bivariate zero-inflated negative binomial regression model for count data with excess zeros. Econ. Lett. 78, 3, 373-378.
72. WANG, X. AND MCCALLUM, A. 2006. Topics over time: a non-Markov continuous-time model of topical trends. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press New York, NY, 424-433.
73. ZHANG, T., RAMAKRISHNAN, R., AND LIVNY, M. 1996. BIRCH: an efficient data clustering method for very large databases. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM Press New York, NY, 103-114.