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Big Data Mining and Analytics  2018, Vol. 01 Issue (03): 245-256    DOI: 10.26599/BDMA.2018.9020023
A Multi-granularity Decomposition Mechanism of Complex Tasks Based on Density Peaks
Ziling Pang, Guoyin Wang*, Jie Yang
Ziling Pang, Guoyin Wang, and Jie Yang are with the Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Post and Telecommunication, Chongqing 400060, China. E-mail:;
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There are many algorithms for solving complex problems in supervised manner. However, unsupervised tasks are more common in real scenarios. Inspired by the idea of granular computing and the characteristics of human cognitive process, this paper proposes a complex tasks decomposition mechanism based on Density Peaks Clustering (DPC) to address complex tasks with an unsupervised process, which simulates the multi-granular observation and analysis of human being. Firstly, the DPC algorithm is modified to nullify its essential defects such as the difficulty of locating correct clustering centers and classifying them accurately. Then, the improved DPC algorithm is used to construct the initial decomposition solving space with multi-granularity theory. We also define subtask centers set and the granulation rules to guide the multi-granularity decomposing procedure. These rules are further used to decompose the solving space from coarse granules to the optimal fine granules with a convergent and automated process. Furthermore, comprehensive experiments are presented to verify the applicability and veracity of our proposed method in community-detection tasks with several benchmark complex social networks. The results show that our method outperforms other four state-of-the-art approaches.

Key wordsmulti-granularity      task decomposition      density peaks      complex network     
Received: 08 September 2017      Published: 13 January 2020
Corresponding Authors: Guoyin Wang   
Cite this article:

Ziling Pang, Guoyin Wang, Jie Yang. A Multi-granularity Decomposition Mechanism of Complex Tasks Based on Density Peaks. Big Data Mining and Analytics, 2018, 01(03): 245-256.

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Fig. 1 (a) Dataset DS which contains the latitude and longitude of 13 cities; (b) Leading tree of DS; and (c) Clustering result of DS.
Table 1 The intermediate results of DS.
Fig. 2 The real social structure of Dolphin network.
Table 2 The intermediate results of DPC in Dolphin network.
Fig. 3 The global task leading tree of Dolphin network.
Table 3 The redundant centers CT of Dolphin network.
iSimilarity (i)
Table 4 Popping process for the first member of final center task set.
Fig. 4 (a) Global leading tree structure of Dolphin network; (b) Progress of the first granulation, where node 14 is one of the community center, so the tree splits two sub trees, and they are constituted as a fine granularity task solving layer; (c) Granulating process triggered by center node 52, and they form a fine granular solving layer with three subtask sets.
iSimilarity (i)
Table 5 Popping process for the second member of final center task set.
iSimilarity (i)
Table 6 Popping process for the third member of final center task set.
Fig. 5 The structure of Dolphin network by MrGDM.
ENBC[28]By utilizing personal views and redefined community structure, this algorithm presents a notion of common interest in the relationship of social network. The method proposes two key measures reachability and isolability, where the reachability evaluates the ability of each node to reach out members of community and the isolability accounts for the ability of any community to isolate itself from the rest of the network.
Local-T[29]By considering the number of internal and external triads, the main idea of this method is the T metric, which computes the relative quality of a community. The authors propose an intuitive statistical method based on the T metric, which can identify outlier and hub nodes within each discovered community.
CDERS[30]The authors utilize an expanding ring search starting from the individual of interest and treat it as the seed node, and then according to their definition of a community, they iteratively contain the nodes at increasing number of hops from the seed user. If there is no further nodes can be appended, the iterative process is terminated. Furthermore, the social communities are organized by the list of added nodes.
LICOD[31]This method adopts a leader-follower approach, whereby the leader nodes create social communities in which local communities can be calculated. The nodes whose degree is higher percentage compared to their neighbors would be selected as leader nodes. And then the leaders with a certain percent of common neighbors are considered a community. By computing the shortest distance of every node to the leader and considering the decision of neighbors, each node can be added to advisable community.
Table 7 Compared methods and their description.
DatasetMethodNMIF-ScoreModularity (rank)C
MrGDM1(1)1(1)0.371 (2)2
MrGDM0.889 (1)0.970 (1)0.379 (2)2
MrGDM0.890 (3)0.829 (2)0.555 (1)12
MrGDM0.970 (3)1 (1)0.857 (4)15
MrGDM0.959 (3)0.957 (3)0.837 (3)31
Table 8 Experimental applications of the compared methods for the selected datasets.
ENBC[28]O?(n2)Accurate communities, but still bound by cost
Local-T[29]O?(n3)Identifies outliers, but for local communities
CDERS[30]O?(n2)Accurate communities in small network
LICOD[[31]O?(n3)Smaller and very inaccurate communities
SCAN[38]O?(m)Identifies outliers, but inaccurate communities
LeadF[39]O?(nm)Smaller communities in dense network
MrGDMO?(n2)Accurate communities, but for dense network
Table 9 Summary of complexity of community detection algorithms.
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