Abstract
\(k-\)core is one type of cohesive subgraphs such that every vertex has at least k degree within the graph. It is widely used in many graph mining tasks, including but not limited to community detection, graph visualization and clique finding. Frequently decomposing a dynamic graph to get its \(k-\)cores brings expensive cost since \(k-\)cores evolve as the dynamic graph changes. To address this problem, previous studies proposed several maintenance solutions to update \(k-\)cores based on a single inserted (removed) edge. Unlike previous studies, we maintain affected \(k-\)cores from the sparsest to the densest, so the cost of our method is determined by the largest core number of a graph. Experimental results show that our approach can significantly outperform the previous algorithms up to 3 order of magnitude for real graphs tested.
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References
Alvarez-Hamelin, J.I., Dall’Asta, L., Barrat, A., Vespignani, A.: Large scale networks fingerprinting and visualization using the k-core decomposition. In: Advances in Neural Information Processing Systems, pp. 41–50 (2006)
Alvarezhamelin, J.I., Dall’Asta, L., Barrat, A., Vespignani, A.: K-core decomposition of internet graphs: hierarchies, self-similarity and measurement biases. Netw. Heterogen. Media 3(2), 371–393 (2017)
Bonchi, F., Gullo, F., Kaltenbrunner, A., Volkovich, Y.: Core decomposition of uncertain graphs. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1316–1325. ACM (2014)
Cheng, J., Ke, Y., Chu, S., Özsu, M.T.: Efficient core decomposition in massive networks. In: 2011 IEEE 27th International Conference on Data Engineering (ICDE), pp. 51–62. IEEE (2011)
Cui, W., Xiao, Y., Wang, H., Wei, W.: Local search of communities in large graphs. In: ACM SIGMOD International Conference on Management of Data, pp. 991–1002. ACM (2014)
Li, R.H., Yu, J.X., Mao, R.: Efficient core maintenance in large dynamic graphs. IEEE Trans. Knowl. Data Eng. 26(10), 2453–2465 (2014)
Lu, C., Yu, J.X., Wei, H., Zhang, Y.: Finding the maximum clique in massive graphs. Proc. VLDB Endowment 10(11), 1538–1549 (2017)
Montresor, A., De Pellegrini, F., Miorandi, D.: Distributed k-core decomposition. IEEE Trans. Parallel Distrib. Syst. 24(2), 288–300 (2013)
Sarıyüce, A.E., Gedik, B., Jacques-Silva, G., Wu, K.L., Çatalürek, Ü.V.: Incremental k-core decomposition: algorithms and evaluation. VLDB J. 25(3), 425–447 (2016)
Seidman, S.B.: Network structure and minimum degree. Soc. Netw. 5(3), 269–287 (1983)
Wu, H., Cheng, J., Lu, Y., Ke, Y., Huang, Y., Yan, D., Wu, H.: Core decomposition in large temporal graphs. In: 2015 IEEE International Conference on Big Data (Big Data), pp. 649–658. IEEE (2015)
Acknowledgment
This work was supported by the National Natural Science Foundation of China under Grant U1911201, Guangdong Special Support Program under Grant 2017T X04X148, the Fundamental Research Funds for the Central Universities under Grant 19LGZD37, 19LGYJS57.
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Bai, W., Zhang, Y., Liu, X., Chen, M., Wu, D. (2020). Efficient Core Maintenance of Dynamic Graphs. In: Nah, Y., Cui, B., Lee, SW., Yu, J.X., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020. Lecture Notes in Computer Science(), vol 12113. Springer, Cham. https://doi.org/10.1007/978-3-030-59416-9_42
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DOI: https://doi.org/10.1007/978-3-030-59416-9_42
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