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The map is structured as a graph of nodes where each node defines a local region feature map.
Informally, a mission dependency model is a graph of nodes where every edge is associated with a probability value.
The predicted interactome can be represented as a graph of nodes, corresponding to the proteins, and connecting edges, corresponding to the interactions.
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It generates a graph of n nodes and m edges by randomly connecting two nodes by an edge and repeat this procedure until the graph contains m edges.
In a so-called semantic network, conceptual entities such as objects, actions, or events are represented as a graph of linked nodes (Figure 4).
Motzek et al. [11] extend a model by Jakobson [20] and model mission dependencies as a graph of mission nodes (MN) as shown in Fig. 1.
To create a graph of n nodes, one begins with a seed graph of size m0.
Yeast SL network is a graph of 2383 nodes and 12707 links.
In order to partition a graph of N nodes into k clusters, we utilize the first k eigenvectors y 1, …, y k.
A protein family phylogeny is represented as a tree, i.e., as an acyclic graph of nodes connected by branches (links), where each node represents a diversification event.
A graph consists of nodes and edges: the nodes represent 'microstates' and the edges represent transitions between microstates.
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