【发布时间】：2021-01-01 11:23:15
【问题描述】：
这是我的交易数据：
data:
id from to date amount
<int> <fctr> <fctr> <date> <dbl>
19521 6644 6934 2005-01-01 700.0
19524 6753 8456 2005-01-01 600.0
19523 9242 9333 2005-01-01 1000.0
… … … … …
1056317 7819 7454 2010-12-31 60.2
1056318 6164 7497 2010-12-31 107.5
1056319 7533 7492 2010-12-31 164.1
之前我问this question，我需要在指定的时间间隔内形成一个网络，然后对网络进行网络测量。
现在，我需要更进一步。
我想做的如下：
- 例如让我们考虑
row 1。在row 1中，交易日期为"2005-01-01"，所以我们需要提取date_trx位于"2004-07-05"-"2005-01-01"（即"2005-01-01"之前的最后6个月）的数据，然后在此形成网络提取数据以计算网络的度量，例如网络中帐户的度中心性、中介中心性。最后，新列degree_centrality和betweenness_centrality的第一行将分别包含账户6644的度中心性和账户6644的中介中心性。 - 同样，在
row 2中，交易日期又是"2005-01-01"，所以我们需要提取date_trx位于"2004-07-05"-"2005-01-01"的数据（这是日期"2005-01-01"之前的最后6个月）和然后在这个提取的数据上形成网络，计算网络的度量，例如网络中账户的度中心性、介数中心性。最后，新列degree_centrality和betweenness_centrality的第二行将分别包含账户6753的度中心性和账户6753的中介中心性。 - 所有数据行都是这样的。
PS：
- 让我们考虑
row 1、row 2和row 3。由于日期相同，因此将由日期在"2004-07-05"-"2005-01-01"范围内的数据创建的网络对于所有三行将完全相同。但是，由于degree centr.或betweenness centr.等网络度量将根据from列中的帐户添加到数据中，因此6644、6753和9242的这些网络度量很可能会有所不同彼此。- 此外，当我们提取日期范围之间的数据以形成网络时，会考虑日期间隔的边界。例如，我数据中的第一个日期是
"2005-01-01"（实际上在该日期进行了 4 笔交易）。但是，假设在第一个日期"2005-01-01"上只有一个观察（交易）并且在此日期之前没有任何观察，现在我们要创建一个介于"2004-07-05"-"2005-01-01"之间的数据网络。因为正如我所说，边界也很重要，所以我们可以在这个指定的日期范围内进行的唯一观察来形成网络是日期为"2005-01-01"的观察。因此，由于一笔交易由两个账户组成，因此只有这两个账户（节点）会出现在网络中。因此，归根结底，一次观察就足以构建网络并在其上计算网络指标。
数据样本的dput()输出：
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14183, 14183, 14183, 14183, 14183, 14188, 14188, 14196, 14196,
14213, 14213, 14213, 14213, 14218, 14226, 14226, 14231, 14232,
14243, 14244, 14244, 14244, 14249, 14249, 14254, 14256, 14257,
14257, 14258, 14259, 14259, 14263, 14263, 14264, 14266, 14267,
14274, 14275, 14275, 14275, 14275, 14280, 14285, 14286, 14286,
14288, 14291, 14299, 14302, 14303, 14303, 14308, 14312, 14313,
14316, 14316, 14317, 14334, 14334, 14334, 14339, 14342, 14347,
14364, 14364, 14364, 14369, 14377, 14377, 14377, 14391, 14395,
14395, 14395, 14395, 14395, 14395, 14400, 14402, 14403, 14406,
14408, 14425, 14425, 14425, 14425, 14425, 14430, 14433, 14436,
14437, 14438, 14440, 14443, 14445, 14456, 14456, 14456, 14456,
14458, 14461, 14461, 14463, 14463, 14464, 14466, 14469, 14484,
14487, 14487, 14487, 14487, 14487, 14487, 14492, 14492, 14498,
14500, 14500, 14508, 14513, 14514, 14517, 14517, 14517, 14517,
14517, 14522, 14522, 14527, 14530, 14537, 14540, 14548, 14548,
14548, 14553, 14556, 14560, 14561, 14563, 14578, 14578, 14578,
14578, 14583, 14584, 14585, 14586, 14591, 14607, 14609, 14609,
14609, 14609, 14609, 14609, 14611, 14612, 14614, 14614, 14615,
14616, 14616, 14618, 14620, 14622, 14622, 14624, 14626, 14628,
14629, 14631, 14633, 14637, 14640, 14640, 14640, 14640, 14645,
14646, 14646, 14648, 14652, 14653, 14668, 14668, 14668, 14668,
14670, 14673, 14673, 14676, 14679, 14680, 14681, 14681, 14685,
14686, 14699, 14699, 14699, 14699, 14703, 14704, 14707, 14711,
14712, 14712, 14727, 14729, 14729, 14729, 14734, 14737, 14738,
14742, 14760, 14760, 14760, 14760, 14760, 14760, 14760, 14763,
14765, 14769, 14773, 14773, 14773, 14774, 14777, 14784, 14790,
14790, 14790, 14790, 14795, 14803, 14821, 14821, 14821, 14821,
14824, 14826, 14826, 14828, 14829, 14829, 14830, 14831, 14834,
14840, 14850, 14852, 14852, 14852, 14852, 14852, 14852, 14856,
14856, 14857, 14865, 14882, 14882, 14882, 14882, 14882, 14882,
14887, 14889, 14889, 14890, 14895, 14910, 14913, 14913, 14913,
14918, 14918, 14919, 14922, 14926, 14927, 14943, 14943, 14943,
14946, 14948, 14949, 14951, 14954, 14956, 14967, 14974, 14974
), class = "Date"), amount = c(24.4, 7618, 21971, 5245, 2921,
8000, 169.2, 71.5, 14.6, 4214, 14.6, 13920, 14.6, 24640, 1600,
261.1, 16400, 3500, 2700, 19882, 182, 14.6, 16927, 25653, 3059,
2880, 9658, 4500, 12480, 14.6, 1000, 3679, 34430, 12600, 14.6,
19.2, 4900, 826, 3679, 2100, 38000, 79, 11400, 21495, 3679, 200,
14.6, 100.6, 3679, 5300, 108.9, 3679, 2696, 7500, 171.6, 14.6,
99.2, 2452, 3679, 3218, 700, 69.7, 14.6, 91.5, 2452, 3679, 2900,
17572, 14.6, 14.6, 90.5, 2452, 49752, 3679, 1900, 14.6, 870,
85.2, 2452, 3679, 1600, 540, 14.6, 14.6, 79, 210, 2452, 28400,
720, 180, 420, 44289, 489, 3679, 840, 2900, 150, 870, 420, 14.6,
152.9, 64.1, 41464, 2452, 205, 3679, 2166, 100, 14.6, 14.6, 59.7,
6588, 2452, 3679, 65, 200, 14.6, 65.7, 2452, 3679, 15792, 2000,
2200, 99.7, 14.6, 140.3, 67.6, 2452, 5600, 873, 3679, 660, 36258,
1900, 2002, 263, 14.6, 63.3, 2452, 14607, 3679, 1400, 7680, 360,
14.6, 14.6, 65.5, 2452, 344, 3679, 990, 1000, 19652, 1080, 14.6,
72.8, 22991, 2452, 1140, 9226, 3679, 2400, 1410, 18300, 14.6,
14.6, 75.7, 2452, 3679, 1700, 14.6, 14.6, 65.4, 78.8, 2452, 6846,
270, 3679, 6339, 3000, 14.6, 14.6, 358.3, 102.3, 14.6, 73.2,
3869, 2452, 3679, 2800, 14.6, 14.6, 72.4, 228.2, 2452, 3679,
3700, 180, 6900, 350, 14.6, 14.6, 59.2, 23131, 2452, 2880, 1200,
630, 3679, 2400, 1680, 17100, 720, 390, 2520, 450, 1920, 840,
137.9, 14.6, 14.6, 55.4, 2452, 5800, 1700, 24977, 3679, 570,
630, 11700, 14.6, 58.3, 2452, 1347, 1455, 4600, 3679, 3300, 14.6,
14.6, 51.9, 2452, 2038, 3679, 14.6, 49.6, 14.6, 2452, 9252, 3294,
3679, 1170, 70.5, 70.1, 91.3, 14.6, 14.6, 54.1, 2452, 46181,
15586, 74529, 3679, 189.5, 318.1, 14.6, 55.2, 14.6, 2452, 6446,
1500, 6300, 3679, 8600, 300, 6500, 2600, 185.2, 14.6, 64.1, 11200,
3041, 2452, 2895, 14037, 3103, 1200, 3679, 870, 14.6, 57.9, 93,
109.1, 67.1, 14.6, 2452, 390, 13200, 7746, 3679, 2300, 2960,
18292, 14.6, 136.9, 7500, 14.6, 65, 2452, 2974, 331, 3679, 1300,
14600, 14.6, 14.6, 72.4, 2452, 1100, 10315, 3679, 39864, 94.1,
101.2, 14.6, 72.4, 2452, 1469, 27132, 4700, 3679, 1000, 101.8,
58.4, 76, 14.6, 14.6, 61.6, 38200, 570, 2400, 2452, 600, 400,
100, 2412, 4485, 1320, 3679, 2500, 600, 810, 1800, 2400, 330,
480, 14.6, 114, 14.6, 55.9, 2452, 500, 4356, 7045, 11889, 3679,
146.1, 14.6, 288.5, 56.3, 1300, 3, 2452, 7156, 24784, 24423,
3679, 822, 810, 20800, 2.2, 1800, 14.6, 63, 1050, 2452, 15976,
4755, 11100, 3679, 180, 14.6, 166.6, 59.9, 2452, 5411, 13405,
3679, 183.9, 14.6, 35.4, 10.8, 14.6, 14.6, 66.7, 1800, 2452,
2277, 511, 800, 3679, 65, 5040, 450, 14.6, 14.6, 93.5, 71.5,
2452, 3679, 14.6, 14.6, 14.6, 75.4, 270, 14, 2452, 4622, 145,
24324, 1523, 372, 3679, 1400, 3100, 14.6, 14.6, 14.6, 77.9, 14.6,
77.1, 14989, 6500, 2452, 3679, 247.3, 14.6, 14.6, 14.6, 190.6,
58.7, 2452, 1398, 4268, 3597, 3679, 1800, 14.6, 14.6, 63.1, 2452,
12400, 5106, 4633, 3679, 510, 14.6, 204.7, 67.1, 4500, 2452,
5800, 1275, 6460, 3679, 6406, 78.7, 52.6)), row.names = c(NA,
-478L), class = "data.frame")
编辑：
根据@pseudospin 的解决方案，代码如下：
library(data.table)
setDT(dt)
measure_graph <- function(dt) {
network <- graph_from_data_frame(dt[,.(from, to)], directed=TRUE)
accounts <- dt[date == max(date), from]
list(
account = accounts,
degree = degree(network, mode = "all")[accounts],
in_degree = degree(network, mode = "in")[accounts],
out_degree = degree(network, mode = "out")[accounts]
)
}
dt[, measure_graph(dt[(date >= end_date - 180) & (date <= end_date)]), .(end_date = date)]
当使用上面提供的数据应用时，会返回此输出：
end_date account degree in_degree out_degree
<date> <fctr> <dbl> <dbl> <dbl>
2005-05-31 5370 1 0 1
2005-08-05 5370 2 0 2
2005-09-12 5370 3 0 3
2005-10-05 8605 NA NA NA
2005-11-12 5370 5 1 4
2005-11-26 6390 NA NA NA
2005-11-30 5370 6 2 4
2006-01-31 5370 7 2 5
2006-03-31 8934 NA NA NA
2006-04-11 5370 6 2 4
...
这里，为什么帐户8605,6390,8934 没有degree=1, in_degree=0 和out_degree=1 而有NA？此外，有些值是正确的，但有些不是。例如，考虑帐户 5370 的日期 "2010-12-11"。
按此日期和帐户过滤输出：
end_date account degree in_degree out_degree
<date> <fctr> <dbl> <dbl> <dbl>
2010-12-11 5370 1 0 1
但是，当我在日期 "2010-12-11" 和帐户 5370 之前的最后 6 个月期间（即 "2010-06-14"-"2010-12-11"）过滤上面提供的示例数据时，由
data[(date <="2010-12-11" & date>=as.Date(as.numeric(as.Date("2010-12-11"))-180, origin="1970-01-01")) & (from==5370 | to==5370)]
它给出：
id from to date amount
<int> <fctr> <fctr> <date> <dbl>
890395 5939 5370 2010-06-14 65.0
891820 6592 5370 2010-06-17 5040.0
894176 5370 6352 2010-06-24 450.0
901068 5765 5370 2010-06-30 14.6
902416 5370 6251 2010-06-30 14.6
904059 5370 5722 2010-06-30 93.5
904624 5898 5370 2010-06-30 71.5
...
1032335 6700 5370 2010-11-30 67.1
1033712 5370 5827 2010-12-03 4500.0
1035096 5370 5852 2010-12-05 2452.0
1036537 7078 5370 2010-12-06 5800.0
1038682 7349 5370 2010-12-08 1275.0
1042439 5370 6710 2010-12-11 6460.0
(63 rows in total)
因此，根据这个，实际上5370 应该有degree=63，如果我们只过滤to==5370，我们会看到它应该有in_degree=37，同样如果我们只过滤from==5370，我们'会看到它应该有out_degree=26，对于指定的日期范围"2010-06-14"-"2010-12-11"，而代码分别计算为1,0和1。
为什么代码对某些人给出正确的结果，而对另一些人给出错误的结果？
【问题讨论】：
-
我复制了您的代码版本，它似乎可以工作 - 我没有看到您看到的 NA。这可能与数据表的名称有关 - 您使用的是
data，我在函数外部和内部都使用了dt。尝试为它们设置不同的名称，并从您的环境中删除任何旧名称。此外，问题中的示例数据在 2008 年停止，因此我无法检查问题的其余部分。 -
我刚刚注意到，
from和to列在形成网络时应该是字符格式，而不是因子。这样，所有值都可以正确计算。我认为当这两列是因素时，会发生错误，因为这些因素列的级别包含的帐户数量超过了构成网络的帐户数量。将这两列转换为字符即可解决问题。
标签： r data.table igraph social-networking network-analysis