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The nodes are drugs or targets, and an edge between two nodes exists if their similarity is above a user-defined threshold.
In network theory, an adjacency matrix commonly describes the network topology by containing the information of whether a link between two nodes exists or not.
An edge between two nodes exists if the distance between corresponding points is within a threshold r.
The adjacency matrix only gives information about whether a connection between two nodes exists in the network; in particular, it contains only topological information about the network and is not related to the colloquial meaning of adjacent as spatially nearby.
Inverse geometric model: similar to the geometric model, except that nodes are connected if the distance between their corresponding points is larger than a radius R: ‖ x i − x j ‖≥ R. ER model: there are m0 nodes, and each possible edge between two nodes exists uniformly at random with a probability P. See Figure 4 for a schematic representation of the seed graph models.
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A link between any two nodes exists if they lie within the transmission radius of each other.
Given two nodes separated by a distance S, Pmhc(S) is the probability of having multi-hop connectivity (mhc) or, in other words, the probability that one chain of inter-connected vehicles between the two nodes exists.
The constructed network took proteins as its nodes, and the edge between any two nodes existed if and only if the score of the interaction between the corresponding proteins was greater than zero.
Second, we consider that a single-layer link between two nodes i and j exists if the link appears only in one layer in the multilayer social network.
Where a link exists between two nodes i and j, the nodes are said to be adjacent and the link is therefore represented by the unordered pair of nodes (i,j).
The nodes of this graph are the elements of A, and an edge exists between two nodes x and y if (x,y) ∈ R.
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