Paper Information | SBC BOOKS

#	Name
1	Vinicius Dias(viniciusdias@ufla.br)
2	Samuel Ferraz(samuel.ferraz@ufms.br)

#	Reference
1	Agrawal, M., Zitnik, M., and Leskovec, J. (2018). Large-scale analysis of disease pathways in the human interactome. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 23:111–122.
2	Aquino, S. B. F. (2023). Strategies for efficient subgraph enumeration on GPUs. Phd thesis, Federal University of Minas Gerais. Available at http://hdl.handle.net/1843/62443.
3	Benson, A. R., Gleich, D. F., and Leskovec, J. (2016). Higher-order organization of complex networks. Science.
4	Bindschaedler, L., Malicevic, J., Lepers, B., Goel, A., and Zwaenepoel, W. (2021). Tesseract: Distributed, General Graph Pattern Mining on Evolving Graphs, page 458–473. Association for Computing Machinery, New York, NY, USA.
5	Canonical forms for frequent graph mining. In Decker, R. and Lenz, H. J., editors, Advances in Data Analysis, pages 337–349, Berlin, Heidelberg. Springer Berlin Heidelberg.
6	Bringmann, B. and Nijssen, S. (2008). What is frequent in a single graph? In Proceedings of the 12th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD’08, pages 858–863, Berlin, Heidelberg. Springer-Verlag.
7	Buehrer, G. and Chellapilla, K. (2008). A scalable pattern mining approach to web graph compression with communities. In Proceedings of the 2008 International Conference on Web Search and Data Mining, WSDM ’08, page 95–106, New York, NY, USA. Association for Computing Machinery.
8	Chen, X. and Arvind (2022). Efficient and scalable graph pattern mining on GPUs. In 16th USENIX Symposium on Operating Systems Design and Implementation (OSDI 22), pages 857–877, Carlsbad, CA. USENIX Association.
9	Chen, X., Dathathri, R., Gill, G., and Pingali, K. (2020). Pangolin: An efficient and flexible graph mining system on cpu and gpu. Proc. VLDB Endow., 13(8):1190–1205.
10	Corporation, N. (2024). NVIDIA Website. https://www.nvidia.com/. [Online; accessed 5-August-2024].
11	Dias, V., Teixeira, C. H. C., Guedes, D., Meira Jr., W., and Parthasarathy, S. (2019). Fractal: A general-purpose graph pattern mining system. In Proceedings of the 2019 International Conference on Management of Data (SIGMOD).
12	dos Santos Dias, V. V. (2023). Graph pattern mining: consolidating models, systems, and abstractions. Phd thesis, Federal University of Minas Gerais. Available at http://hdl.handle.net/1843/51806.
13	Dourisboure, Y., Geraci, F., and Pellegrini, M. (2009). Extraction and classification of dense implicit communities in the web graph. ACM Trans. Web, 3(2).
14	Elbassuoni, S. and Blanco, R. (2011). Keyword search over rdf graphs. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM ’11, pages 237–242, New York, NY, USA. ACM.
15	Elseidy, M., Abdelhamid, E., Skiadopoulos, S., and Kalnis, P. (2014). Grami: Frequent subgraph and pattern mining in a single large graph. Proc. VLDB Endow., 7(7):517–528.
16	Big graphs: Challenges and opportunities. Proc. VLDB Endow., 15(12):3782–3797.
17	Ferraz, S., Dias, V., Teixeira, C. H., Parthasarathy, S., Teodoro, G., and Meira, W. (2024). Dumato: An efficient warp-centric subgraph enumeration system for gpu. Journal of Parallel and Distributed Computing, 191:104903.
18	Ferraz, S., Dias, V., Teixeira, C. H., Teodoro, G., and Meira, W. (2022). Efficient strategies for graph pattern mining algorithms on gpus. In 2022 IEEE 34th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), pages 110–119.
19	Hoffman, F. and Krasle, D. (2015). Fraud detection using network analysis. Patent No. EP2884418A1, Filed September 1st., 2014, Issued June 17th., 2015.
20	Hong, S., Kim, S. K., Oguntebi, T., and Olukotun, K. (2011). Accelerating cuda graph algorithms at maximum warp. In Proceedings of the 16th ACM Symposium on Principles and Practice of Parallel Programming, PPoPP ’11, page 267–276, New York, NY, USA. Association for Computing Machinery.
21	Hooi, B., Shin, K., Lamba, H., and Faloutsos, C. (2020). Telltail: Fast scoring and detection of dense subgraphs. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04):4150–4157.
22	Huan, J., Wang, W., and Prins, J. (2003). Efficient mining of frequent subgraphs in the presence of isomorphism. In Proceedings of the Third IEEE International Conference on Data Mining, ICDM ’03, pages 549–, Washington, DC, USA. IEEE Computer Society.
23	Jamshidi, K., Mahadasa, R., and Vora, K. (2020). Peregrine: A pattern-aware graph mining system. In Proceedings of the Fifteenth European Conference on Computer Systems, EuroSys ’20, New York, NY, USA. Association for Computing Machinery.
24	Jin, R., Xiang, Y., Ruan, N., and Fuhry, D. (2009). 3-hop: a highcompression indexing scheme for reachability query. In Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data, SIGMOD ’09, page 813–826, New York, NY, USA. Association for Computing Machinery.
25	Junttila, T. and Kaski, P. (2007). Engineering an efficient canonical labeling tool for large and sparse graphs. In Proceedings of the Meeting on Algorithm Engineering & Expermiments, pages 135–149, Philadelphia, PA, USA. Society for Industrial and Applied Mathematics.
26	Kipf, T. N. and Welling, M. (2017). Semi-supervised classification with graph convolutional networks.
27	Kriege, N. and Mutzel, P. (2012). Subgraph matching kernels for attributed graphs. In Proceedings of the 29th International Coference on International Conference on Machine Learning, ICML’12, page 291–298, Madison, WI, USA. Omnipress.
28	Kuramochi, M. and Karypis, G. (2005). Finding frequent patterns in a large sparse graph*. Data Min. Knowl. Discov., 11(3):243–271.
29	Leon-Suematsu, Y. I., Inui, K., Kurohashi, S., and Kidawara, Y. (2011). Web Spam Detection by Exploring Densely Connected Subgraphs. In 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, volume 1, pages 124–129.
30	Mawhirter, D., Reinehr, S., Holmes, C., Liu, T., and Wu, B. (2021). Graphzero: A high-performance subgraph matching system. SIGOPS Oper. Syst. Rev., 55(1):21–37.
31	Mawhirter, D. and Wu, B. (2019). Automine: Harmonizing high-level abstraction and high performance for graph mining. In Proceedings of the 27th ACM Symposium on Operating Systems Principles, SOSP ’19, pages 509–523, New York, NY, USA. ACM.
32	Mouli, S. C., Ribeiro, B., and Neville, J. (2018). Subgraph pattern neural networks for high-order graph evolution prediction.
33	Prˇzulj, N., Corneil, D. G., and Jurisica, I. (2004). Modeling interactome: scale-free or geometric? Bioinformatics, 20(18):3508–3515.
34	Qin, H., Li, R.-H., Wang, G., Qin, L., Cheng, Y., and Yuan, Y. (2019). Mining periodic cliques in temporal networks. In 2019 IEEE 35th International Conference on Data Engineering (ICDE), pages 1130–1141.
35	Ribeiro, P., Paredes, P., Silva, M. E. P., Aparicio, D., and Silva, F. (2021). A survey on subgraph counting: Concepts, algorithms, and applications to network motifs and graphlets. ACM Comput. Surv., 54(2).
36	Sun, X., Cheng, H., Li, J., Liu, B., and Guan, J. (2023). All in one: Multi-task prompting for graph neural networks. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD ’23, page 2120–2131, New York, NY, USA. Association for Computing Machinery.
37	Teixeira, C. H. C., Fonseca, A. J., Serafini, M., Siganos, G., Zaki, M. J., and Aboulnaga, A. (2015). Arabesque: a system for distributed graph mining. In Proceedings of the 25th Symposium on Operating Systems Principles, SOSP ’15. ACM.
38	Ugander, J., Backstrom, L., and Kleinberg, J. (2013). Subgraph frequencies: mapping the empirical and extremal geography of large graph collections. In WWW.
39	Wang, K., Zuo, Z., Thorpe, J., Nguyen, T. Q., and Xu, G. H. (2018). Rstream: Marrying relational algebra with streaming for efficient graph mining on a single machine. In Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation, OSDI’18, pages 763–782, Berkeley, CA, USA. USENIX Association.
40	Yan, X. and Han, J. (2002). gspan: Graph-based substructure pattern mining. In Proceedings of the 2002 IEEE International Conference on Data Mining, ICDM ’02, pages 721–, Washington, DC, USA. IEEE Computer Society.
41	Yang, Y., Yan, D., Wu, H., Cheng, J., Zhou, S., and Lui, J. C. (2016). Diversified temporal subgraph pattern mining. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’16, page 1965–1974, New York, NY, USA. Association for Computing Machinery.
42	Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J., McCauley, M., Franklin, M. J., Shenker, S., and Stoica, I. (2012). Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. In NSDI.
43	Zhao, H., Zhou, Y., Song, Y., and Lee, D. L. (2019). Motif enhanced recommendation over heterogeneous information network. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM’19, pages 2189–2192, New York, NY, USA. ACM.

1

Agrawal, M., Zitnik, M., and Leskovec, J. (2018). Large-scale analysis of disease pathways in the human interactome. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 23:111–122.

2

Aquino, S. B. F. (2023). Strategies for efficient subgraph enumeration on GPUs. Phd thesis, Federal University of Minas Gerais. Available at http://hdl.handle.net/1843/62443.

3

Benson, A. R., Gleich, D. F., and Leskovec, J. (2016). Higher-order organization of complex networks. Science.

4

Bindschaedler, L., Malicevic, J., Lepers, B., Goel, A., and Zwaenepoel, W. (2021). Tesseract: Distributed, General Graph Pattern Mining on Evolving Graphs, page 458–473. Association for Computing Machinery, New York, NY, USA.

5

Canonical forms for frequent graph mining. In Decker, R. and Lenz, H. J., editors, Advances in Data Analysis, pages 337–349, Berlin, Heidelberg. Springer Berlin Heidelberg.

6

Bringmann, B. and Nijssen, S. (2008). What is frequent in a single graph? In Proceedings of the 12th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD’08, pages 858–863, Berlin, Heidelberg. Springer-Verlag.

7

Buehrer, G. and Chellapilla, K. (2008). A scalable pattern mining approach to web graph compression with communities. In Proceedings of the 2008 International Conference on Web Search and Data Mining, WSDM ’08, page 95–106, New York, NY, USA. Association for Computing Machinery.

8

Chen, X. and Arvind (2022). Efficient and scalable graph pattern mining on GPUs. In 16th USENIX Symposium on Operating Systems Design and Implementation (OSDI 22), pages 857–877, Carlsbad, CA. USENIX Association.

9

Chen, X., Dathathri, R., Gill, G., and Pingali, K. (2020). Pangolin: An efficient and flexible graph mining system on cpu and gpu. Proc. VLDB Endow., 13(8):1190–1205.

10

Corporation, N. (2024). NVIDIA Website. https://www.nvidia.com/. [Online; accessed 5-August-2024].

11

Dias, V., Teixeira, C. H. C., Guedes, D., Meira Jr., W., and Parthasarathy, S. (2019). Fractal: A general-purpose graph pattern mining system. In Proceedings of the 2019 International Conference on Management of Data (SIGMOD).

12

dos Santos Dias, V. V. (2023). Graph pattern mining: consolidating models, systems, and abstractions. Phd thesis, Federal University of Minas Gerais. Available at http://hdl.handle.net/1843/51806.

13

Dourisboure, Y., Geraci, F., and Pellegrini, M. (2009). Extraction and classification of dense implicit communities in the web graph. ACM Trans. Web, 3(2).

14

Elbassuoni, S. and Blanco, R. (2011). Keyword search over rdf graphs. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM ’11, pages 237–242, New York, NY, USA. ACM.

15

Elseidy, M., Abdelhamid, E., Skiadopoulos, S., and Kalnis, P. (2014). Grami: Frequent subgraph and pattern mining in a single large graph. Proc. VLDB Endow., 7(7):517–528.

16

Big graphs: Challenges and opportunities. Proc. VLDB Endow., 15(12):3782–3797.

17

Ferraz, S., Dias, V., Teixeira, C. H., Parthasarathy, S., Teodoro, G., and Meira, W. (2024). Dumato: An efficient warp-centric subgraph enumeration system for gpu. Journal of Parallel and Distributed Computing, 191:104903.

18

Ferraz, S., Dias, V., Teixeira, C. H., Teodoro, G., and Meira, W. (2022). Efficient strategies for graph pattern mining algorithms on gpus. In 2022 IEEE 34th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), pages 110–119.

19

Hoffman, F. and Krasle, D. (2015). Fraud detection using network analysis. Patent No. EP2884418A1, Filed September 1st., 2014, Issued June 17th., 2015.

20

Hong, S., Kim, S. K., Oguntebi, T., and Olukotun, K. (2011). Accelerating cuda graph algorithms at maximum warp. In Proceedings of the 16th ACM Symposium on Principles and Practice of Parallel Programming, PPoPP ’11, page 267–276, New York, NY, USA. Association for Computing Machinery.

21

Hooi, B., Shin, K., Lamba, H., and Faloutsos, C. (2020). Telltail: Fast scoring and detection of dense subgraphs. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04):4150–4157.

22

Huan, J., Wang, W., and Prins, J. (2003). Efficient mining of frequent subgraphs in the presence of isomorphism. In Proceedings of the Third IEEE International Conference on Data Mining, ICDM ’03, pages 549–, Washington, DC, USA. IEEE Computer Society.

23

Jamshidi, K., Mahadasa, R., and Vora, K. (2020). Peregrine: A pattern-aware graph mining system. In Proceedings of the Fifteenth European Conference on Computer Systems, EuroSys ’20, New York, NY, USA. Association for Computing Machinery.

24

Jin, R., Xiang, Y., Ruan, N., and Fuhry, D. (2009). 3-hop: a highcompression indexing scheme for reachability query. In Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data, SIGMOD ’09, page 813–826, New York, NY, USA. Association for Computing Machinery.

25

Junttila, T. and Kaski, P. (2007). Engineering an efficient canonical labeling tool for large and sparse graphs. In Proceedings of the Meeting on Algorithm Engineering & Expermiments, pages 135–149, Philadelphia, PA, USA. Society for Industrial and Applied Mathematics.

26

Kipf, T. N. and Welling, M. (2017). Semi-supervised classification with graph convolutional networks.

27

Kriege, N. and Mutzel, P. (2012). Subgraph matching kernels for attributed graphs. In Proceedings of the 29th International Coference on International Conference on Machine Learning, ICML’12, page 291–298, Madison, WI, USA. Omnipress.

28

Kuramochi, M. and Karypis, G. (2005). Finding frequent patterns in a large sparse graph*. Data Min. Knowl. Discov., 11(3):243–271.

29

Leon-Suematsu, Y. I., Inui, K., Kurohashi, S., and Kidawara, Y. (2011). Web Spam Detection by Exploring Densely Connected Subgraphs. In 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, volume 1, pages 124–129.

30

Mawhirter, D., Reinehr, S., Holmes, C., Liu, T., and Wu, B. (2021). Graphzero: A high-performance subgraph matching system. SIGOPS Oper. Syst. Rev., 55(1):21–37.

31

Mawhirter, D. and Wu, B. (2019). Automine: Harmonizing high-level abstraction and high performance for graph mining. In Proceedings of the 27th ACM Symposium on Operating Systems Principles, SOSP ’19, pages 509–523, New York, NY, USA. ACM.

32

Mouli, S. C., Ribeiro, B., and Neville, J. (2018). Subgraph pattern neural networks for high-order graph evolution prediction.

33

Prˇzulj, N., Corneil, D. G., and Jurisica, I. (2004). Modeling interactome: scale-free or geometric? Bioinformatics, 20(18):3508–3515.

34

Qin, H., Li, R.-H., Wang, G., Qin, L., Cheng, Y., and Yuan, Y. (2019). Mining periodic cliques in temporal networks. In 2019 IEEE 35th International Conference on Data Engineering (ICDE), pages 1130–1141.

35

Ribeiro, P., Paredes, P., Silva, M. E. P., Aparicio, D., and Silva, F. (2021). A survey on subgraph counting: Concepts, algorithms, and applications to network motifs and graphlets. ACM Comput. Surv., 54(2).

36

Sun, X., Cheng, H., Li, J., Liu, B., and Guan, J. (2023). All in one: Multi-task prompting for graph neural networks. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD ’23, page 2120–2131, New York, NY, USA. Association for Computing Machinery.

37

Teixeira, C. H. C., Fonseca, A. J., Serafini, M., Siganos, G., Zaki, M. J., and Aboulnaga, A. (2015). Arabesque: a system for distributed graph mining. In Proceedings of the 25th Symposium on Operating Systems Principles, SOSP ’15. ACM.

38

Ugander, J., Backstrom, L., and Kleinberg, J. (2013). Subgraph frequencies: mapping the empirical and extremal geography of large graph collections. In WWW.

39

Wang, K., Zuo, Z., Thorpe, J., Nguyen, T. Q., and Xu, G. H. (2018). Rstream: Marrying relational algebra with streaming for efficient graph mining on a single machine. In Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation, OSDI’18, pages 763–782, Berkeley, CA, USA. USENIX Association.

40

Yan, X. and Han, J. (2002). gspan: Graph-based substructure pattern mining. In Proceedings of the 2002 IEEE International Conference on Data Mining, ICDM ’02, pages 721–, Washington, DC, USA. IEEE Computer Society.

41

Yang, Y., Yan, D., Wu, H., Cheng, J., Zhou, S., and Lui, J. C. (2016). Diversified temporal subgraph pattern mining. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’16, page 1965–1974, New York, NY, USA. Association for Computing Machinery.

42

Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J., McCauley, M., Franklin, M. J., Shenker, S., and Stoica, I. (2012). Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. In NSDI.

43

Zhao, H., Zhou, Y., Song, Y., and Lee, D. L. (2019). Motif enhanced recommendation over heterogeneous information network. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM’19, pages 2189–2192, New York, NY, USA. ACM.

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