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Efficient and Scalable Subgraph Search from Big Graphs in Cloud

Funding: 2016: $105,000
2017: $115,000
2018: $115,000

Project Member(s): Qin, L.

Funding or Partner Organisation: Australian Research Council (ARC Discovery Projects)

Start year: 2016

Summary: Subgraph search is in high demand for many applications that deal with big graphs, such as social network marketing, crime detection, and sales pattern discovery. However, there are many challenges currently when the search needs to be conducted in cloud environments. This project aims to develop efficient and scalable query processing algorithms to search subgraphs from a big graph stored in a cluster of machines in the cloud. The expected outcomes will provide major technological breakthroughs to benefit science, society, and the economy nationally by laying theoretical foundations for subgraph search in the cloud, and establish a unified graph search engine that integrates various query semantics for use in many applications.

Publications:

Li, W, Qiao, M, Qin, L, Zhang, Y, Chang, L & Lin, X 2019, 'Eccentricities on small-world networks', VLDB Journal, vol. 28, no. 5, pp. 765-792.
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Lyu, B, Qin, L, Lin, X, Chang, L & Yu, JX 2019, 'Supergraph Search in Graph Databases via Hierarchical Feature-Tree', IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 2, pp. 385-400.
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Wang, X, Qin, L, Lin, X, Zhang, Y & Chang, L 2019, 'Leveraging set relations in exact and dynamic set similarity join', VLDB Journal, vol. 28, pp. 267-292.
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Wen, D, Qin, L, Zhang, Y, Lin, X & Yu, JX 2019, 'I/O Efficient Core Graph Decomposition: Application to Degeneracy Ordering', IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 1, pp. 75-90.
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Liu, W, Hanchen, W, Ying, Z, Wei, W & Lu, Q 2019, 'I-LSH: I/O Efficient c-Approximate Nearest Neighbor Search in High-Dimensional Space', IEEE 35th International Conference on Data Engineering, Macao.
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Qin, H, Li, RH, Wang, G, Qin, L, Cheng, Y & Yuan, Y 2019, 'Mining periodic cliques in temporal networks', Proceedings - International Conference on Data Engineering, International Conference on Data Engineering, IEEE, Macao, Macao, pp. 1130-1141.
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Wen, D, Qin, L, Zhang, Y, Chang, L & Chen, L 2019, 'Enumerating k-Vertex Connected Components in Large Graphs.', 2019 IEEE 35th International Conference on Data Engineering (ICDE), International Conference on Data Engineering, IEEE, Macao, Macao, pp. 52-63.
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Yang, B, Wen, D, Qin, L, Zhang, Y, Chang, L & Li, R-H 2019, 'Index-based Optimal Algorithm for Computing K-Cores in Large Uncertain Graphs', 2019 IEEE 35nd International Conference on Data Engineering (ICDE), 2019 IEEE 35nd International Conference on Data Engineering (ICDE), IEEE, Macau SAR, China, pp. 64-75.
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Chang, L & Qin, L 2018, Cohesive Subgraph Computation over Large Sparse Graphs, Springer International Publishing.
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Li, W, Qiao, M, Qin, L, Zhang, Y, Chang, L & Lin, X 2018, 'Exacting Eccentricity for Small-World Networks', 2018 IEEE 34th International Conference on Data Engineering (ICDE), International Conference on Data Engineering, IEEE, Paris, France, pp. 785-796.
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Zhang, F, Zhang, Y, Qin, L, Zhang, W & Lin, X 2017, 'When Engagement Meets Similarity: Efficient (k, r)-Core Computation on Social Networks.', Proc. VLDB Endow., vol. 10, pp. 998-1009.

Zhu, Y, Zhang, H, Qin, L & Cheng, H 2017, 'Efficient MapReduce algorithms for triangle listing in billion-scale graphs', Distributed and Parallel Databases, vol. 35, no. 2, pp. 149-176.
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Zhang, H, Zhu, Y, Qin, L, Cheng, H & Yu, JX 2017, 'Efficient local clustering coefficient estimation in massive graphs', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Database Systems for Advanced Applications, China, pp. 371-386.
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Zhu, Y, Li, Y, Liu, J, Qin, L & Yu, JX 2017, 'GMAlign: A new network aligner for revealing large conserved functional components', Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017, IEEE International Conference on Bioinformatics and Biomedicine, IEEE, Kansas City, MO, USA, pp. 120-127.
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Zhang, S, Qin, L, Zheng, Y & Cheng, H 2016, 'Effective and Efficient: Large-Scale Dynamic City Express', IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 12, pp. 3203-3217.
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Wen, D, Qin, L, Zhang, Y, Lin, X & Yu, JX 2016, 'I/O Efficient Core Graph Decomposition at Web Scale.', CoRR.

Huang, X, Cheng, H, Li, R-H, Qin, L & Yu, JX 2015, 'Top-K structural diversity search in large networks.', The VLDB Journal, vol. 24, pp. 319-343.
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Li, Z, Qin, L, Cheng, H, Zhang, X & Zhou, X 2015, 'TRIP: An Interactive Retrieving-Inferring Data Imputation Approach.', IEEE Transactions on Knowledge and Data Engineering, vol. 27, pp. 2550-2563.
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Lai, L, Qin, L, Lin, X & Chang, L 2015, 'Scalable Subgraph Enumeration in MapReduce.', Proceedings of the VLDB Endowment, International Conference on Very Large Databases, VLDB Endowment, Kohala Coast, Hawaii, pp. 974-985.
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Li, RH, Yu, JX, Qin, L, Mao, R & Jin, T 2015, 'On random walk based graph sampling', Proceedings - International Conference on Data Engineering, IEEE International Conference on Data Engineering, IEEE, Seoul, South Korea, pp. 927-938.
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Qin, L, Li, RH, Chang, L & Zhang, C 2015, 'Locally densest subgraph discovery', Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM International Conference on Knowledge Discovery and Data Mining, ACM, Sydney, Australia, pp. 965-974.
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FOR Codes: Database Management, Pattern Recognition and Data Mining, Information Processing Services (incl. Data Entry and Capture), Electronic Information Storage and Retrieval Services, Expanding Knowledge in the Information and Computing Sciences