Welcome to Anagrammer Crossword Genius! Keep reading below to see if coseness is an answer to any crossword puzzle or word game (Scrabble, Words With Friends etc). Scroll down to see all the info we have compiled on coseness.
coseness
Searching in Crosswords ...
The answer COSENESS has 0 possible clue(s) in existing crosswords.
Searching in Word Games ...
The word COSENESS is NOT valid in any word game. (Sorry, you cannot play COSENESS in Scrabble, Words With Friends etc)
There are 8 letters in COSENESS ( C3E1N1O1S1 )
To search all scrabble anagrams of COSENESS, to go: COSENESS?
Rearrange the letters in COSENESS and see some winning combinations
Scrabble results that can be created with an extra letter added to COSENESS
4 letters out of COSENESS
Searching in Dictionaries ...
Definitions of coseness in various dictionaries:
No definitions found
Word Research / Anagrams and more ...
Keep reading for additional results and analysis below.
| Coseness might refer to |
|---|
|
In a connected graph, Closeness centrality (or closeness) of a node is a measure of centrality in a network, calculated as the reciprocal of the sum of the length of the shortest paths between the node and all other nodes in the graph. Thus, the more central a node is, the closer it is to all other nodes. * Closeness was defined by Bavelas (1950) as the reciprocal of the farness, that is:* * * * C * ( * x * ) * = * * * 1 * * * ∑ * * y * * * d * ( * y * , * x * ) * * * * . * * * {\displaystyle C(x)={\frac {1}{\sum _{y}d(y,x)}}.} * where * * * * d * ( * y * , * x * ) * * * {\displaystyle d(y,x)} * is the distance between vertices * * * * x * * * {\displaystyle x} * and * * * * y * * * {\displaystyle y} * . When speaking of closeness centrality, people usually refer to its normalized form which represents the average length of the shortest paths instead of their sum. It is generally given by the previous formula multiplied by * * * * N * − * 1 * * * {\displaystyle N-1} * , where * * * * N * * * {\displaystyle N} * is the number of nodes in the graph. For large graphs this difference becomes inconsequential so the * * * * − * 1 * * * {\displaystyle -1} * is dropped resulting in: * * * * * C * ( * x * ) * = * * * N * * * ∑ * * y * * * d * ( * y * , * x * ) * * * * . * * * {\displaystyle C(x)={\frac {N}{\sum _{y}d(y,x)}}.} * This adjustment allows comparisons between nodes of graphs of different sizes. * Taking distances from or to all other nodes is irrelevant in undirected graphs, whereas it can produce totally different results in directed graphs (e.g. a website can have a high closeness centrality from outgoing link, but low closeness centrality from incoming links). |