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Cohesive Subgraph Search Over Large Heterogeneous Information Networks pp 57–60Cite as

Related Work on CSMs and Solutions

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Abstract

In the literature, the topic of CSS on graphs has received tremendous research attention and it has been extensively studied in the past several decades, and most of existing research works focus on conventional homogeneous networks. However, the models and solutions of these works are highly related to the these of CSS over large HINs. In this chapter, we thoroughly review the five groups of works on CSS on homogeneous networks, which are core-, truss-, clique-, connectivity-, and density-based CSMs and solutions. In addition, we also review the works on HIN clustering and compare it with the earlier version of this book.

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References

  1. Bahmani, B., Kumar, R., & Vassilvitskii, S. (2012). Densest subgraph in streaming and mapreduce. PVLDB, 5(5), 454–465.

    Google Scholar 

  2. Barber, M. J. (2007). Modularity and community detection in bipartite networks. Physical Review E, 76(6), 066102.

    Article  MathSciNet  Google Scholar 

  3. Batagelj, V., & Zaversnik, M. (2003). An o (m) algorithm for cores decomposition of networks. arXiv preprint cs/0310049.

    Google Scholar 

  4. Beckett, S. J. (2016). Improved community detection in weighted bipartite networks. Royal Society Open Science, 3(1), 140536.

    Article  MathSciNet  Google Scholar 

  5. Berlowitz, D., Cohen, S., & Kimelfeld, B. (2015). Efficient enumeration of maximal k-plexes. In SIGMOD (pp. 431–444). ACM.

    Google Scholar 

  6. Bi, F., Chang, L., Lin, X., & Zhang, W. (2018). An optimal and progressive approach to online search of top-k influential communities. PVLDB, 11(9), 1056–1068.

    Google Scholar 

  7. Cazals, F., & Karande, C. (2008). A note on the problem of reporting maximal cliques. Theoretical Computer Science, 407(1–3), 564–568.

    Article  MathSciNet  MATH  Google Scholar 

  8. Chang, L., Lin, X., Qin, L., Yu, J. X., & Zhang, W. (2015). Index-based optimal algorithms for computing Steiner components with maximum connectivity. In SIGMOD (pp. 459–474). ACM.

    Google Scholar 

  9. Chang, L., Yu, J. X., Qin, L., Lin, X., Liu, C., & Liang, W. (2013). Efficiently computing k-edge connected components via graph decomposition. In SIGMOD (pp. 205–216). ACM.

    Google Scholar 

  10. Charikar, M. (2000). Greedy approximation algorithms for finding dense components in a graph. In International Workshop on Approximation Algorithms for Combinatorial Optimization (pp. 84–95). Springer.

    Google Scholar 

  11. Chen, L., Gao, Y., Zhang, Y., Jensen, C. S., & Zheng, B. (2019). Efficient and incremental clustering algorithms on star-schema heterogeneous graphs. In ICDE (pp. 256–267). IEEE.

    Google Scholar 

  12. Chen, L., Liu, C., Zhou, R., Li, J., Yang, X., & Wang, B. (2018). Maximum co-located community search in large scale social networks. PVLDB, 11(10), 1233–1246.

    Google Scholar 

  13. Chen, L., Liu, C., Zhou, R., Xu, J., & Li, J. (2021). Efficient exact algorithms for maximum balanced biclique search in bipartite graphs (pp. 248–260).

    Google Scholar 

  14. Chen, P.-L., Chou, C.-K., & Chen, M.-S. (2014). Distributed algorithms for k-truss decomposition. In Big data (pp. 471–480). IEEE.

    Google Scholar 

  15. Chen, S., Wei, R., Popova, D., & Thomo, A. (2016). Efficient computation of importance based communities in web-scale networks using a single machine. In CIKM (pp. 1553–1562).

    Google Scholar 

  16. Chen, Y., Fang, Y., Cheng, R., Li, Y., Chen, X., & Zhang, J. (2018). Exploring communities in large profiled graphs. IEEE Transactions on Knowledge and Data Engineering, 31(8), 1624–1629.

    Article  Google Scholar 

  17. Cheng, J., Ke, Y., Chu, S., & Özsu, M. T. (2011). Efficient core decomposition in massive networks. In ICDE (pp. 51–62). IEEE.

    Google Scholar 

  18. Cheng, J., Ke, Y., Fu, A. W.-C., Yu, J. X., & Zhu, L. (2011). Finding maximal cliques in massive networks. TODS, 36(4), 1–34.

    Article  Google Scholar 

  19. Cohen, J. (2008). Trusses: Cohesive subgraphs for social network analysis. National security Agency Technical Report, 16, 3–1.

    Google Scholar 

  20. Danisch, M., Balalau, O., & Sozio, M. (2018). Listing k-cliques in sparse real-world graphs. In WWW (pp. 589–598).

    Google Scholar 

  21. Danisch, M., Chan, T.-H. H., & Sozio, M. (2017). Large scale density-friendly graph decomposition via convex programming. In WWW (pp. 233–242).

    Google Scholar 

  22. Ding, D., Li, H., Huang, Z., & Mamoulis, N. (2017). Efficient fault-tolerant group recommendation using alpha-beta-core. In CIKM (pp. 2047–2050).

    Google Scholar 

  23. Dormann, C. F., & Strauss, R. (2014). A method for detecting modules in quantitative bipartite networks. Methods in Ecology and Evolution, 5(1), 90–98.

    Article  Google Scholar 

  24. Fang, Y., Cheng, R., Chen, Y., Luo, S., & Hu, J. (2017). Effective and efficient attributed community search. The VLDB Journal, 26(6), 803–828.

    Article  Google Scholar 

  25. Fang, Y., Cheng, R., Li, X., Luo, S., & Hu, J. (2017). Effective community search over large spatial graphs. PVLDB, 10(6), 709–720.

    Google Scholar 

  26. Fang, Y., Cheng, R., Luo, S., & Hu, J. (2016). Effective community search for large attributed graphs. PVLDB, 9(12), 1233–1244.

    Google Scholar 

  27. Fang, Y., Cheng, R., Luo, S., Hu, J., & Huang, K. (2017). C-explorer: Browsing communities in large graphs. PVLDB, 10(12), 1885–1888.

    Google Scholar 

  28. Fang, Y., Huang, X., Qin, L., Zhang, Y., Zhang, W., Cheng, R., & Lin, X. (2020). A survey of community search over big graphs. The VLDB Journal, 29(1), 353–392.

    Article  Google Scholar 

  29. Fang, Y., Wang, K., Lin, X., & Zhang, W. (2021). Cohesive subgraph search over big heterogeneous information networks: Applications, challenges, and solutions. ACM SIGMOD (pp. 2829–2838).

    Google Scholar 

  30. Fang, Y., Wang, Z., Cheng, R., Li, X., Luo, S., Hu, J., & Chen, X. (2019). On spatial-aware community search. TKDE, 31(4), 783–798.

    Google Scholar 

  31. Fang, Y., Wang, Z., Cheng, R., Wang, H., & Hu, J. (2019). Effective and efficient community search over large directed graphs. TKDE, 31(11), 2093–2107.

    Google Scholar 

  32. Fang, Y., Yu, K., Cheng, R., Lakshmanan, L. V., & Lin, X. (2019). Efficient algorithms for densest subgraph discovery. PVLDB, 12(11), 1719–1732.

    Google Scholar 

  33. Gallo, G., Grigoriadis, M. D., & Tarjan, R. E. (1989). A fast parametric maximum flow algorithm and applications. SIAM Journal on Computing, 18(1), 30–55.

    Article  MathSciNet  MATH  Google Scholar 

  34. Giatsidis, C., Thilikos, D. M., & Vazirgiannis, M. (2013). D-cores: Measuring collaboration of directed graphs based on degeneracy. Knowledge and Information Systems, 35(2), 311–343.

    Article  Google Scholar 

  35. Gibbons, A. (1985). Algorithmic graph theory. Cambridge University Press.

    MATH  Google Scholar 

  36. Goldberg, A. V. (1984). Finding a maximum density subgraph. University of California Berkeley, CA.

    Google Scholar 

  37. Guimerà, R., Sales-Pardo, M., & Amaral, L. A. N. (2007). Module identification in bipartite and directed networks. Physical Review E, 76(3), 036102.

    Article  Google Scholar 

  38. Hu, J., Wu, X., Cheng, R., Luo, S., & Fang, Y. (2016). Querying minimal Steiner maximum-connected subgraphs in large graphs. In CIKM (pp. 1241–1250).

    Google Scholar 

  39. Hu, J., Wu, X., Cheng, R., Luo, S., & Fang, Y. (2017). On minimal Steiner maximum-connected subgraph queries. TKDE, 29(11), 2455–2469.

    Google Scholar 

  40. Huang, X., Cheng, H., Qin, L., Tian, W., & Yu, J. X. (2014). Querying k-truss community in large and dynamic graphs. In SIGMOD (pp. 1311–1322). ACM.

    Google Scholar 

  41. Huang, X., & Lakshmanan, L. V. (2017). Attribute-driven community search. PVLDB, 10(9), 949–960.

    Google Scholar 

  42. Huang, X., Lakshmanan, L. V., & Xu, J. (2017). Community search over big graphs: Models, algorithms, and opportunities. In ICDE (pp. 1451–1454). IEEE.

    Google Scholar 

  43. Huang, X., Lakshmanan, L. V., Yu, J. X., & Cheng, H. (2015). Approximate closest community search in networks. PVLDB, 9(4).

    Google Scholar 

  44. Khaouid, W., Barsky, M., Srinivasan, V., & Thomo, A. (2015). K-core decomposition of large networks on a single pc. PVLDB, 9(1), 13–23.

    Google Scholar 

  45. Larremore, D. B., Clauset, A., & Jacobs, A. Z. (2014). Efficiently inferring community structure in bipartite networks. Physical Review E, 90(1), 012805.

    Article  Google Scholar 

  46. Li, R.-H., Gao, S., Qin, L., Wang, G., Yang, W., & Yu, J. X. (2020). Ordering heuristics for k-clique listing. PVLDB, 13(12), 2536–2548.

    Google Scholar 

  47. Li, R.-H., Qin, L., Ye, F., Yu, J. X., Xiao, X., Xiao, N., & Zheng, Z. (2018). Skyline community search in multi-valued networks. In SIGMOD (pp. 457–472).

    Google Scholar 

  48. Li, R.-H., Qin, L., Yu, J. X., & Mao, R. (2015). Influential community search in large networks. PVLDB, 8(5), 509–520.

    Google Scholar 

  49. Li, R.-H., Yu, J. X., & Mao, R. (2013). Efficient core maintenance in large dynamic graphs. TKDE, 26(10), 2453–2465.

    Google Scholar 

  50. Liu, Q., Zhao, M., Huang, X., Xu, J., & Gao, Y. (2020). Truss-based community search over large directed graphs. In SIGMOD (pp. 2183–2197).

    Google Scholar 

  51. Liu, Q., Zhu, Y., Zhao, M., Huang, X., Xu, J., and Gao, Y. (2020). Vac: Vertex-centric attributed community search. In ICDE (pp. 937–948). IEEE.

    Google Scholar 

  52. Liu, X., & Murata, T. (2010). Community detection in large-scale bipartite networks. Transactions of the Japanese Society for Artificial Intelligence, 25(1), 16–24.

    Article  Google Scholar 

  53. Luo, L., Fang, Y., Cao, X., Zhang, X., & Zhang, W. (2021). Detecting communities from heterogeneous graphs: A context path-based graph neural network model. In CIKM (pp. 1170–1180).

    Google Scholar 

  54. Ma, C., Fang, Y., Cheng, R., Lakshmanan, L. V., Zhang, W., & Lin, X. (2020). Efficient algorithms for densest subgraph discovery on large directed graphs. In SIGMOD (pp. 1051–1066). ACM.

    Google Scholar 

  55. Ma, C., Fang, Y., Cheng, R., Lakshmanan, L. V., Zhang, W., & Lin, X. (2021). Efficient directed densest subgraph discovery. ACM SIGMOD Record, 50(1), 33–40.

    Article  Google Scholar 

  56. Ma, C., Fang, Y., Cheng, R., Lakshmanan, L. V., Zhang, W., & Lin, X. (2021). On directed densest subgraph discovery. ACM Transactions on Database Systems (TODS), 46(4), 1–45.

    Article  MathSciNet  Google Scholar 

  57. Mitzenmacher, M., Pachocki, J., Peng, R., Tsourakakis, C., & Xu, S. C. (2015). Scalable large near-clique detection in large-scale networks via sampling. In SIGKDD (pp. 815–824). ACM.

    Google Scholar 

  58. Montresor, A., De Pellegrini, F., & Miorandi, D. (2012). Distributed k-core decomposition. IEEE TPDS, 24(2), 288–300.

    Google Scholar 

  59. Qin, L., Li, R.-H., Chang, L., & Zhang, C. (2015). Locally densest subgraph discovery. In KDD (pp. 965–974). ACM.

    Google Scholar 

  60. Saito, K., Yamada, T., & Kazama, K. (2008). Extracting communities from complex networks by the k-dense method. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 91(11), 3304–3311.

    Article  Google Scholar 

  61. Sarıyüce, A. E., Gedik, B., Jacques-Silva, G., Wu, K.-L., & Çatalyürek, Ü. V. (2016). Incremental k-core decomposition: Algorithms and evaluation. VLDBJ, 25(3), 425–447.

    Article  Google Scholar 

  62. Schmidt, M. C., Samatova, N. F., Thomas, K., & Park, B.-H. (2009). A scalable, parallel algorithm for maximal clique enumeration. Journal of Parallel and Distributed Computing, 69(4), 417–428.

    Article  Google Scholar 

  63. Seidman, S. B. (1983). Network structure and minimum degree. Social Networks, 5(3), 269–287.

    Article  MathSciNet  Google Scholar 

  64. Shi, C., Li, Y., Zhang, J., Sun, Y., & Philip, S. Y. (2016). A survey of heterogeneous information network analysis. TKDE, 29(1), 17–37.

    Google Scholar 

  65. Shi, C., Wang, R., Li, Y., Yu, P. S., & Wu, B. (2014). Ranking-based clustering on general heterogeneous information networks by network projection. In CIKM (pp. 699–708). ACM.

    Google Scholar 

  66. Sun, Y., Aggarwal, C. C., & Han, J. (2012). Relation strength-aware clustering of heterogeneous information networks with incomplete attributes. PVLDB, 5(5), 394–405.

    Google Scholar 

  67. Sun, Y., Han, J., Zhao, P., Yin, Z., Cheng, H., & Wu, T. (2009). Rankclus: Integrating clustering with ranking for heterogeneous information network analysis. In EDBT (pp. 565–576). ACM.

    Google Scholar 

  68. Sun, Y., Norick, B., Han, J., Yan, X., Yu, P. S., & Yu, X. (2012). Integrating meta-path selection with user-guided object clustering in heterogeneous information networks. In SIGKDD (pp. 1348–1356). ACM.

    Google Scholar 

  69. Sun, Y., Yu, Y., & Han, J. (2009). Ranking-based clustering of heterogeneous information networks with star network schema. In SIGKDD (pp. 797–806). ACM.

    Google Scholar 

  70. Tatti, N., & Gionis, A. (2015). Density-friendly graph decomposition. In WWW (pp. 1089–1099).

    Google Scholar 

  71. Tomita, E., Tanaka, A., & Takahashi, H. (2006). The worst-case time complexity for generating all maximal cliques and computational experiments. Theoretical Computer Science, 363(1), 28–42.

    Article  MathSciNet  MATH  Google Scholar 

  72. Tsourakakis, C. (2015). The k-clique densest subgraph problem. In WWW (pp. 1122–1132).

    Google Scholar 

  73. Verma, A., & Butenko, S. (2013). Network clustering via clique relaxations: A community based. Graph Partitioning and Graph Clustering, 588, 129.

    Article  MathSciNet  MATH  Google Scholar 

  74. Wang, J., & Cheng, J. (2012). Truss decomposition in massive networks. PVLDB, 5(9), 812–823.

    Google Scholar 

  75. Wang, K., Cao, X., Lin, X., Zhang, W., & Qin, L. (2018). Efficient computing of radius-bounded k-cores. In ICDE (pp. 233–244). IEEE.

    Google Scholar 

  76. Wang, Z., Yuan, Y., Zhou, X., & Qin, H. (2020). Effective and efficient community search in directed graphs across heterogeneous social networks. In Australasian Database Conference (ADC) (pp. 161–172).

    Google Scholar 

  77. Wen, D., Qin, L., Zhang, Y., Lin, X., & Yu, J. X. (2016). I/O efficient core graph decomposition at web scale. In ICDE (pp. 133–144). IEEE.

    Google Scholar 

  78. Yan, X., Zhou, X. J., & Han, J. (2005). Mining closed relational graphs with connectivity constraints. In SIGKDD (pp. 324–333). ACM.

    Google Scholar 

  79. Zeng, Z., Wang, J., Zhou, L., & Karypis, G. (2007). Out-of-core coherent closed quasi-clique mining from large dense graph databases. TODS, 32(2), 13–es.

    Google Scholar 

  80. Zhang, F., Zhang, Y., Qin, L., Zhang, W., & Lin, X. (2017). When engagement meets similarity: Efficient (k, r)-core computation on social networks. PVLDB, 10(10), 998–1009.

    Google Scholar 

  81. Zhang, Y., & Parthasarathy, S. (2012). Extracting analyzing and visualizing triangle k-core motifs within networks. In ICDE (pp. 1049–1060). IEEE.

    Google Scholar 

  82. Zhang, Y., & Yu, J. X. (2019). Unboundedness and efficiency of truss maintenance in evolving graphs. In SIGMOD (pp. 1024–1041). ACM.

    Google Scholar 

  83. Zhang, Y., Yu, J. X., Zhang, Y., & Qin, L. (2017). A fast order-based approach for core maintenance. In ICDE (pp. 337–348). IEEE.

    Google Scholar 

  84. Zhang, Z., Huang, X., Xu, J., Choi, B., & Shang, Z. (2019). Keyword-centric community search. In ICDE (pp. 422–433). IEEE.

    Google Scholar 

  85. Zhou, R., Liu, C., Yu, J. X., Liang, W., Chen, B., & Li, J. (2012). Finding maximal k-edge-connected subgraphs from a large graph. In EDBT (pp. 480–491).

    Google Scholar 

  86. Zhou, R., Liu, C., Yu, J. X., Liang, W., & Zhang, Y. (2014). Efficient truss maintenance in evolving networks. arXiv preprint arXiv:1402.2807.

    Google Scholar 

  87. Zhou, Y., & Liu, L. (2013). Social influence based clustering of heterogeneous information networks. In KDD (pp. 338–346). ACM.

    Google Scholar 

  88. Zhou, Z., & Amini, A. A. (2019). Analysis of spectral clustering algorithms for community detection: The general bipartite setting. Journal of Machine Learning Research, 20, 47–1.

    MathSciNet  MATH  Google Scholar 

  89. Zhou, Z., & Amini, A. A. (2020). Optimal bipartite network clustering. Journal of Machine Learning Research, 21(40), 1–68.

    MathSciNet  MATH  Google Scholar 

  90. Zhu, Y., He, J., Ye, J., Qin, L., Huang, X., & Yu, J. X. (2020). When structure meets keywords: Cohesive attributed community search. In CIKM (pp. 1913–1922).

    Google Scholar 

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Authors and Affiliations

  1. School of Data Science, The Chinese University of Hong Kong, Shenzhen, Shenzhen, Guangdong, China

    Yixiang Fang

  2. Antai College of Economics & Management, Shanghai Jiao Tong University, Shanghai, China

    Kai Wang & Xuemin Lin

  3. Computer Science and Engineering, The University of New South Wales, Sydney, NSW, Australia

    Wenjie Zhang

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  1. Yixiang Fang
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  2. Kai Wang
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  4. Wenjie Zhang
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Fang, Y., Wang, K., Lin, X., Zhang, W. (2022). Related Work on CSMs and Solutions. In: Cohesive Subgraph Search Over Large Heterogeneous Information Networks. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-030-97568-5_6

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