最近在開發一套自己的單細胞分析方法，所以copy paste事業有所停頓。
實例：
R eNetIt v0.1-1
data(ralu.site)
# Saturated spatial graph
sat.graph <- knn.graph(ralu.site, row.names=ralu.site@data[,"SiteName"])
head(sat.graph@data)
# Distanced constrained spatial graph
dist.graph <- knn.graph(ralu.site, row.names=ralu.site@data[,"SiteName"], max.dist = 5000)
par(mfrow=c(1,2))
plot(sat.graph, col="grey")
points(ralu.site, col="red", pch=20, cex=1.5)
box()
title("Saturated graph")
plot(dist.graph, col="grey")
points(ralu.site, col="red", pch=20, cex=1.5)
box()
title("Distance constrained graph")
一下來自wiki
The nearest neighbor graph (NNG) for a set of n objects P in a metric space (e.g., for a set of points in the plane with Euclidean distance) is a directed graph with P being its vertex set and with a directed edge from p to q whenever q is a nearest neighbor of p (i.e., the distance from p to q is no larger than from p to any other object from P).[1]
NNG圖，在多維空間里我有很多個點，如上例，在17維空間里，我有31個點，一個常見的距離度量就是歐氏距離，NNG是有方向的，因為q是p的鄰居並不代表p是q的鄰居！
In many discussions, the directions of the edges are ignored and the NNG is defined as an ordinary (undirected) graph. However, the nearest neighbor relation is not a symmetric one, i.e., p from the definition is not necessarily a nearest neighbor for q.
In some discussions, in order to make the nearest neighbor for each object unique, the set P is indexed and in the case of a tie the object with, e.g., the largest index is taken for the nearest neighbor.[2]
The k-nearest neighbor graph (k-NNG) is a graph in which two vertices p and q are connected by an edge. if the distance between p and q is among the k-th smallest distances from p to other objects from P. The NNG is a special case of the k-NNG, namely, it is the 1-NNG. k-NNGs obey a separator theorem: they can be partitioned into two subgraphs of at most n(d + 1)/(d + 2) vertices each by the removal of O(k1/dn1 − 1/d) points.[3]
在k-NNG里，就不是最近鄰了，而是考慮k-th，就是把k-th內的點都當做鄰居。
Another special case is the (n − 1)-NNG. This graph is called the farthest neighbor graph (FNG).
如果k=n-1，那么就是FNG圖。
In theoretical discussions of algorithms a kind of general position is often assumed, namely, the nearest (k-nearest) neighbor is unique for each object. In implementations of the algorithms it is necessary to bear in mind that this is not always the case.
NNGs for points in the plane as well as in multidimensional spaces find applications, e.g., in data compression, motion planning, and facilities location. In statistical analysis, the nearest-neighbor chain algorithm based on following paths in this graph can be used to find hierarchical clusterings quickly. Nearest neighbor graphs are also a subject of computational geometry.