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A network method to identify the dynamic changes of the data flow with spatio-temporal feature

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Abstract

Mining on the spatio-temporal data based on network method, is advantage to explore the dynamic changes of mobile communication system from a new perspective. The mobile communication system can be understood as a structure composed of interdependent base stations. The interaction between base stations can be evaluated by the similarity of base station data flow. The constructed network can reveal the interaction structure of human mobile communication activities. Three correlation networks are constructed. In Pearson correlation network, the link represents that the dynamic change of data flow has linear similarity. In Spearman correlation network and Mutual correlation network, that represents monotonic similarity and nonlinear similarity. The networks have large average clustering coefficient. It indicates that the structure of the base station system is stable. In base station system, the nonlinear similarity is stronger than the linear similarity between the time series of data flow. Furthermore, there are greater number nodes with the “bridge” role in the Pearson correlation network. The base stations corresponding to these nodes play an important role in information transmission. Constructing links by the similarity of base station data flow, the potential connectivity between base stations can be found, and the information of remote interaction in mobile communication system can be obtained. The geographical distribution can present the spatial variation of data flow time series correlation.

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Acknowledgments

We appreciate editor and reviewers for their positive and constructive comments and suggestions. This work was supported by the Natural Science Foundation of Inner Mongolia [grant number 2018LH01012]; and partly supported by the National Natural Science Foundation of China [grant numbers 11861049].

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Authors and Affiliations

  1. Science College, Inner Mongolia University of Technology, Hohhot, 010051, China

    Li-Na Wang & Gui-Min Tan

  2. Inner Mongolian Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Hohhot, 010051, China

    Li-Na Wang

  3. Inner Mongolian Branch, China Unicom, Hohhot, 010050, China

    Chen-Rui Zang

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  1. Li-Na Wang
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Correspondence to Li-Na Wang.

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Appendix

Appendix

The raw data of Eq. (8) is as follows. The spatio-temporal sequence matrix D consists of three column vectors: α1, α2, α3. Data in three columns are time series of data flow at three base stations. The length of each time series is 144.

α1 = (4.2254, 6.9536, 0.1331, 0.1070, 0.1617, 0.1622, 0.5675, 9.7144, 7.4732, 47.8609, 19.0116, 191.3641, 5.4373, 15.9058, 14.1898, 4.7601, 4.1999, 6.1638, 5.9149, 7.1773, 8.1560, 14.6302, 2.3700, 71.2440, 3.4324, 0.2110, 13.8395, 0.6696, 8.8676, 1.0834, 7.5824, 10.0579, 2.3766, 17.5327, 13.3228, 26.4965, 6.5343, 19.4150, 4.3120, 10.0341, 9.7108, 10.3062, 13.0456, 27.2355, 37.9095, 12.9162, 26.3166, 25.0704, 0.1313, 0.0531, 0.7245, 0.0371, 0.1500, 0.1850, 6.1619, 17.1917, 26.6532, 12.4903, 7.1840, 5.6126, 9.5235, 13.7192, 21.5468, 16.8919, 7.3776, 30.8557, 6.9697, 18.1500, 25.7069, 4.4798, 45.2360, 5.8823, 3.1715, 0.0790, 2.7104, 2.7899, 0.5192, 0.1058, 0.3142, 5.1533, 8.1087, 5.5130, 25.8272, 13.9438, 5.1281, 15.7147, 3.0741, 7.7981, 2.4721, 15.1025, 9.2804, 4.8258, 5.6095, 1.3536, 5.1127, 2.2506, 0.1021, 0.0626, 0.0281, 0.9588, 0.0964, 0.1636, 3.4632, 9.0962, 3.4512, 3.6361, 7.6806, 12.5249, 8.6885, 0.8406, 7.0643, 5.3448, 3.0835, 10.0879, 2.7440, 22.8063, 4.5731, 5.1853, 5.4473, 3.3129, 16.2445, 0.0430, 0.2003, 0.0690, 0.1251, 0.1643, 4.3230, 0.8983, 4.7684, 8.5058, 10.3970, 10.8331, 17.6634, 5.0980, 8.5983, 6.7927, 195.5040, 25.9908, 16.9744, 68.6794, 15.2118, 13.7123, 2.3288, 17.4739)T.

α2 = (9.6017, 0.9453, 6.7698, 0.2454, 0.4047, 0.0367, 0.3699, 7.4782, 23.5142, 12.5177, 6.7492, 3.3458, 363.2937, 20.1205, 6.4980, 7.3425, 9.1083, 10.4926, 15.4130, 12.9427, 6.6487, 3.6176, 4.2620, 9.0877, 1.1027, 0.2513, 1.8489, 0.0872, 0.0814, 0.0934, 0.3445, 1.8966, 6.7795, 3.4730, 3.3304, 9.6402, 8.5818, 12.3951, 2.6853, 14.8838, 9.0342, 5.3071, 16.9290, 4.4918, 4.0576, 36.8136, 9.0119, 1.7865, 71.5539, 143.5127, 90.9715, 4.6031, 0.9156, 0.2180, 0.0823, 1.5460, 2.9026, 5.9645, 3.5715, 10.7770, 5.9761, 28.7818, 17.2549, 18.2050, 11.4658, 6.7675, 4.9894, 6.7317, 3.9084, 20.3362, 9.6532, 13.2448, 14.1717, 0.6698, 0.0780, 0.9675, 0.0533, 0.5611, 0.9510, 0.8979, 1.1224, 7.4890, 4.7928, 4.4116, 6.3022, 9.6669, 4.8543, 43.9041, 8.2797, 9.2219, 13.6541, 6.7986, 10.3338, 8.3197, 6.8527, 12.0417, 42.0184, 1.6449, 0.5611, 0.3873, 0.2633, 0.1250, 0.1518, 0.5115, 8.6398, 25.4342, 24.3789, 11.3780, 15.6235, 27.3627, 7.6459, 12.0722, 14.6253, 11.1348, 12.7810, 11.2493, 4.6949, 7.4506, 6.1625, 93.5560, 4.4494, 2.5873, 1.4874, 0.3666, 0.5021, 0.7570, 0.3488, 1.8010, 0.9802, 9.6263, 1.6587, 8.2196, 6.7500, 59.7954, 7.6097, 4.7948, 3.6051, 31.7453, 18.5544, 12.7098, 6.6940, 12.7846, 26.7312, 37.5261)T.

α3 = (14.7685, 3.6363, 0.0527, 0.1009, 0.0774, 0.1440, 0.0640, 0.5845, 2.0513, 30.5791, 7.9186, 16.4403, 6.2237, 13.3395, 3.5927, 8.0461, 19.8523, 11.7937, 21.8353, 11.4439, 2.8482, 2.3336, 4.0990, 6.2345, 0.0626, 0.2762, 0.0327, 0.0930, 0.0493, 0.0307, 0.1528, 3.4544, 7.9903, 4.9159, 32.6839, 8.5307, 24.6034, 9.8889, 5.6112, 20.6144, 11.3688, 12.1781, 31.1372, 14.7879, 1.0073, 2.7825, 0.9285, 0.8109, 0.0412, 0.1562, 0.0209, 0.1953, 0.0192, 0.5896, 0.3303, 1.1377, 6.5087, 10.8039, 15.9944, 12.2080, 13.6918, 2.9069, 6.1732, 12.9291, 10.3150, 55.0751, 66.8895, 18.4058, 4.1239, 7.0076, 12.4804, 6.4458, 1.1070, 1.3128, 0.1642, 0.0433, 0.0277, 0.0366, 0.2406, 0.6828, 3.9944, 4.7312, 57.0462, 17.6360, 12.8582, 7.7218, 20.1681, 10.9176, 21.5793, 46.9833, 23.2448, 18.8866, 5.1837, 24.3867, 7.0820, 0.9336, 0.1391, 0.1931, 0.1684, 0.2833, 0.4699, 0.0847, 0.5281, 0.5745, 16.7977, 3.0713, 13.2144, 40.2816, 8.7383, 11.7499, 17.2615, 7.8832, 37.3397, 34.6834, 20.2066, 10.4823, 14.4809, 5.7378, 3.1332, 1.6836, 0.2498, 0.6798, 0.0389, 0.0722, 0.0276, 0.1606, 1.1171, 6.0699, 9.4755, 13.9211, 15.7776, 9.6734, 13.2413, 38.2821, 70.6513, 166.0539, 290.5563, 141.8450, 13.9289, 10.8143, 0.8087, 5.9432, 0.8713, 0.9620)T.

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Wang, LN., Tan, GM. & Zang, CR. A network method to identify the dynamic changes of the data flow with spatio-temporal feature. Appl Intell 52, 5584–5593 (2022). https://doi.org/10.1007/s10489-021-02591-0

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