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  2. Clustering coefficient - Wikipedia

    en.wikipedia.org/wiki/Clustering_coefficient

    In graph theory, a clustering coefficient is a measure of the degree to which nodes in a graph tend to cluster together. Evidence suggests that in most real-world networks, and in particular social networks, nodes tend to create tightly knit groups characterised by a relatively high density of ties; this likelihood tends to be greater than the average probability of a tie randomly established ...

  3. Surjective function - Wikipedia

    en.wikipedia.org/wiki/Surjective_function

    In mathematics, a surjective function (also known as surjection, or onto function / ˈɒn.tuː /) is a function f such that, for every element y of the function's codomain, there exists at least one element x in the function's domain such that f(x) = y. In other words, for a function f : X → Y, the codomain Y is the image of the function's ...

  4. Graph theory - Wikipedia

    en.wikipedia.org/wiki/Graph_theory

    In mathematics, graph theory is the study of graphs, which are mathematical structures used to model pairwise relations between objects. A graph in this context is made up of vertices (also called nodes or points) which are connected by edges (also called arcs, links or lines).

  5. Average path length - Wikipedia

    en.wikipedia.org/wiki/Average_path_length

    Average path length. Average path length, or average shortest path length is a concept in network topology that is defined as the average number of steps along the shortest paths for all possible pairs of network nodes. It is a measure of the efficiency of information or mass transport on a network.

  6. Laplacian matrix - Wikipedia

    en.wikipedia.org/wiki/Laplacian_matrix

    Laplacian matrix. In the mathematical field of graph theory, the Laplacian matrix, also called the graph Laplacian, admittance matrix, Kirchhoff matrix or discrete Laplacian, is a matrix representation of a graph. Named after Pierre-Simon Laplace, the graph Laplacian matrix can be viewed as a matrix form of the negative discrete Laplace ...

  7. Betweenness centrality - Wikipedia

    en.wikipedia.org/wiki/Betweenness_centrality

    Betweenness centrality. An undirected graph colored based on the betweenness centrality of each vertex from least (red) to greatest (blue). In graph theory, betweenness centrality is a measure of centrality in a graph based on shortest paths. For every pair of vertices in a connected graph, there exists at least one shortest path between the ...

  8. Bellman–Ford algorithm - Wikipedia

    en.wikipedia.org/wiki/Bellman–Ford_algorithm

    Bellman–Ford algorithm. The Bellman–Ford algorithm is an algorithm that computes shortest paths from a single source vertex to all of the other vertices in a weighted digraph. [1] It is slower than Dijkstra's algorithm for the same problem, but more versatile, as it is capable of handling graphs in which some of the edge weights are ...

  9. Probability density function - Wikipedia

    en.wikipedia.org/wiki/Probability_density_function

    In probability theory, a probability density function (PDF), density function, or density of an absolutely continuous random variable, is a function whose value at any given sample (or point) in the sample space (the set of possible values taken by the random variable) can be interpreted as providing a relative likelihood that the value of the ...