如何使用 igraph 提取特定节点的邻居？
How to extract Neighbors of specific nodes using igraph?
我有患有他们疾病的人类边缘列表的 .csv 文件。
Person Diseases Age gender location
Person1 Asthma 25 Female Location1
Person2 Pneumonia 35 Male Location2
Person3 Typhoid 40 Male Location3
Person4 TB 36 Male Location4
Person5 Asthma 25 Female Location5
Person6 Pneumonia 50 Male Location21
Person7 Typhoid 24 Female Location22
Person8 TB 30 Female Location23
Person9 Pneumonia 40 Male Location24
Person10 Pneumonia 30 Male Location25
Person11 Pneumonia 45 Female Location26
Person12 Pneumonia 25 Male Location3
Person13 Pneumonia 45 Male Location4
Person14 Pneumonia 30 Male Location5
Person15 Pneumonia 65 Male Location15
Person16 Pneumonia 40 Female Location16
Person17 TB 55 Male Location17
Person18 TB 21 Male Location18
Person19 TB 35 Female Location11
Person20 TB 32 Male Location12
Person21 TB 42 Male Location13
Person22 TB 55 Female Location14
Person23 TB 41 Male Location21
Person24 TB 22 Female Location22
Person25 Typhoid 31 Female Location23
Person26 Typhoid 25 Female Location24
Person27 Typhoid 67 Male Location25
Person28 Typhoid 20 Female Location26
Person29 Typhoid 36 Male Location19
Person30 Typhoid 65 Female Location20
Person31 Asthma 33 Female Location6
Person32 Asthma 21 Male Location7
Person33 Asthma 20 Female Location8
Person34 Asthma 55 Male Location9
Person35 Asthma 40 Female Location10
Person36 Asthma 47 Male Location11
Person37 Asthma 35 Male Location12
Person38 Typhoid 30 Male Location13
Person39 Typhoid 39 Female Location23
Person40 Typhoid 21 Male Location24
Person41 Asthma 40 Female Location25
Person42 Asthma 19 Male Location26
Person43 Asthma 29 Male Location1
Person44 TB 49 Female Location2
Person45 TB 24 Female Location3
Person46 TB 68 Female Location4
Person47 Typhoid 38 Female Location5
Person48 Typhoid 59 Female Location6
Person49 Typhoid 64 Male Location7
Person50 Typhoid 31 Female Location8
Person51 Typhoid 64 Female Location20
Person52 Typhoid 32 Female Location21
Person53 Asthma 30 Male Location22
Person54 Asthma 36 Female Location23
Person55 Asthma 40 Female Location24
Person56 TB 20 Female Location25
Person57 TB 26 Female Location26
Person58 TB 42 Female Location1
Person59 Typhoid 24 Female Location2
Person60 Typhoid 48 Female Location3
Person61 Typhoid 62 Male Location4
Person62 Typhoid 42 Female Location5
Person63 Typhoid 27 Female Location6
Person64 Typhoid 36 Female Location7
Person65 Asthma 67 Male Location8
Person66 Asthma 23 Male Location9
Person67 TB 21 Male Location10
Person68 TB 45 Female Location11
Person1 Arthritis 25 Female Location1
Person2 heart attack 35 Male Location2
Person3 diabetes 40 Female Location3
Person4 heart attack 36 Male Location4
Person5 Arthritis 25 Male Location5
Person6 heart attack 50 Female Location21
Person7 heart attack 24 Male Location22
Person8 diabetes 30 Female Location23
Person9 heart attack 40 Male Location24
Person10 Arthritis 30 Male Location25
Person11 heart attack 45 Female Location26
Person12 Arthritis 25 Male Location3
Person13 heart attack 45 Male Location4
Person14 diabetes 30 Female Location5
Person15 heart attack 65 Female Location15
Person16 Arthritis 40 Female Location16
Person17 heart attack 55 Female Location17
Person18 heart attack 21 Female Location18
Person19 Arthritis 35 Male Location11
Person20 heart attack 32 Female Location12
Person21 heart attack 42 Male Location13
Person22 diabetes 55 Female Location14
Person23 Arthritis 41 Female Location21
Person24 heart attack 22 Female Location22
Person25 diabetes 31 Female Location23
Person26 heart attack 25 Female Location24
Person27 Arthritis 67 Female Location25
Person28 heart attack 20 Male Location26
Person29 heart attack 36 Female Location19
Person30 Arthritis 65 Female Location20
Person31 heart attack 25 Female Location6
Person32 heart attack 24 Male Location7
我试过这行代码来绘制人类及其疾病的二分网络，我已经通过这段代码 "location" 显示了人的属性
V(g)$location <- d_el$location
V(g)$color=V(g)$location
V(g)$Person<- d_el$Person
现在我想提取TB节点的所有邻居节点我已经使用了这行代码
V(g)$location[which(V(g)$Person %in% neighbors(g, "TB"))]
[1] 20 22 14 16 17 20 8 15 16 17 18 19 11 13 24 25 20 21 20 22 14
上面的输出显示了什么？
这应该可行（您的位置是因数，因此显示为整数，请将其转换为字符）：
V(g)$location <- as.character(d_el$location)
V(g)$Person <- as.character(d_el$Person)
V(g)$location[which(V(g)$Person %in% names(neighbors(g, "TB")))]
# [1] "Location4" "Location23" "Location17" "Location18" "Location11" "Location12" "Location13" "Location14" "Location21" "Location22" "Location2" "Location3" "Location4" "Location25"
# [15] "Location26" "Location1" "Location10" "Location11" "Location4"
我有患有他们疾病的人类边缘列表的 .csv 文件。
Person Diseases Age gender location
Person1 Asthma 25 Female Location1
Person2 Pneumonia 35 Male Location2
Person3 Typhoid 40 Male Location3
Person4 TB 36 Male Location4
Person5 Asthma 25 Female Location5
Person6 Pneumonia 50 Male Location21
Person7 Typhoid 24 Female Location22
Person8 TB 30 Female Location23
Person9 Pneumonia 40 Male Location24
Person10 Pneumonia 30 Male Location25
Person11 Pneumonia 45 Female Location26
Person12 Pneumonia 25 Male Location3
Person13 Pneumonia 45 Male Location4
Person14 Pneumonia 30 Male Location5
Person15 Pneumonia 65 Male Location15
Person16 Pneumonia 40 Female Location16
Person17 TB 55 Male Location17
Person18 TB 21 Male Location18
Person19 TB 35 Female Location11
Person20 TB 32 Male Location12
Person21 TB 42 Male Location13
Person22 TB 55 Female Location14
Person23 TB 41 Male Location21
Person24 TB 22 Female Location22
Person25 Typhoid 31 Female Location23
Person26 Typhoid 25 Female Location24
Person27 Typhoid 67 Male Location25
Person28 Typhoid 20 Female Location26
Person29 Typhoid 36 Male Location19
Person30 Typhoid 65 Female Location20
Person31 Asthma 33 Female Location6
Person32 Asthma 21 Male Location7
Person33 Asthma 20 Female Location8
Person34 Asthma 55 Male Location9
Person35 Asthma 40 Female Location10
Person36 Asthma 47 Male Location11
Person37 Asthma 35 Male Location12
Person38 Typhoid 30 Male Location13
Person39 Typhoid 39 Female Location23
Person40 Typhoid 21 Male Location24
Person41 Asthma 40 Female Location25
Person42 Asthma 19 Male Location26
Person43 Asthma 29 Male Location1
Person44 TB 49 Female Location2
Person45 TB 24 Female Location3
Person46 TB 68 Female Location4
Person47 Typhoid 38 Female Location5
Person48 Typhoid 59 Female Location6
Person49 Typhoid 64 Male Location7
Person50 Typhoid 31 Female Location8
Person51 Typhoid 64 Female Location20
Person52 Typhoid 32 Female Location21
Person53 Asthma 30 Male Location22
Person54 Asthma 36 Female Location23
Person55 Asthma 40 Female Location24
Person56 TB 20 Female Location25
Person57 TB 26 Female Location26
Person58 TB 42 Female Location1
Person59 Typhoid 24 Female Location2
Person60 Typhoid 48 Female Location3
Person61 Typhoid 62 Male Location4
Person62 Typhoid 42 Female Location5
Person63 Typhoid 27 Female Location6
Person64 Typhoid 36 Female Location7
Person65 Asthma 67 Male Location8
Person66 Asthma 23 Male Location9
Person67 TB 21 Male Location10
Person68 TB 45 Female Location11
Person1 Arthritis 25 Female Location1
Person2 heart attack 35 Male Location2
Person3 diabetes 40 Female Location3
Person4 heart attack 36 Male Location4
Person5 Arthritis 25 Male Location5
Person6 heart attack 50 Female Location21
Person7 heart attack 24 Male Location22
Person8 diabetes 30 Female Location23
Person9 heart attack 40 Male Location24
Person10 Arthritis 30 Male Location25
Person11 heart attack 45 Female Location26
Person12 Arthritis 25 Male Location3
Person13 heart attack 45 Male Location4
Person14 diabetes 30 Female Location5
Person15 heart attack 65 Female Location15
Person16 Arthritis 40 Female Location16
Person17 heart attack 55 Female Location17
Person18 heart attack 21 Female Location18
Person19 Arthritis 35 Male Location11
Person20 heart attack 32 Female Location12
Person21 heart attack 42 Male Location13
Person22 diabetes 55 Female Location14
Person23 Arthritis 41 Female Location21
Person24 heart attack 22 Female Location22
Person25 diabetes 31 Female Location23
Person26 heart attack 25 Female Location24
Person27 Arthritis 67 Female Location25
Person28 heart attack 20 Male Location26
Person29 heart attack 36 Female Location19
Person30 Arthritis 65 Female Location20
Person31 heart attack 25 Female Location6
Person32 heart attack 24 Male Location7
我试过这行代码来绘制人类及其疾病的二分网络，我已经通过这段代码 "location" 显示了人的属性
V(g)$location <- d_el$location
V(g)$color=V(g)$location
V(g)$Person<- d_el$Person
现在我想提取TB节点的所有邻居节点我已经使用了这行代码
V(g)$location[which(V(g)$Person %in% neighbors(g, "TB"))]
[1] 20 22 14 16 17 20 8 15 16 17 18 19 11 13 24 25 20 21 20 22 14
上面的输出显示了什么？
这应该可行（您的位置是因数，因此显示为整数，请将其转换为字符）：
V(g)$location <- as.character(d_el$location)
V(g)$Person <- as.character(d_el$Person)
V(g)$location[which(V(g)$Person %in% names(neighbors(g, "TB")))]
# [1] "Location4" "Location23" "Location17" "Location18" "Location11" "Location12" "Location13" "Location14" "Location21" "Location22" "Location2" "Location3" "Location4" "Location25"
# [15] "Location26" "Location1" "Location10" "Location11" "Location4"