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evolmod

# evolmod - Inferring How Networks Evolved 858L Inferring...

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Inferring How Networks Evolved 858L

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Inferring network mechanisms: The Drosophila melanogaster protein interaction network Manuel Middendorf, Etay Ziv, and Chris H. Wiggins
RDS - Random Static Erdos-Renyi (1960): Create n vertices Between every pair of vertices {u,v}, add an edge with probability p . ShowGraphArray[Partition[Table[RandomGraph[15, p], {p, 0.1, 0.9, 0.1}], 3]] Expected degree is ( n -1) p

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RDG - Random Growing G i-1 u i Parameters: n = number of nodes; M = expected # of edges. At time i , a node is added. Then M/n edges are added uniformly at random. Added edges might not involve u i . Nodes added earlier have more chances to be adjacent to edges.
LPA – Linear Preferential Attachment u 1 u 2 u 3 u 4 u i-2 u i-1 u i Histogram: degree( u j ) + a , normalized to be a probability distribution: Parameters: n = number of nodes; M = number of edges; a = smoothing parameter. At time i , a node u i is added. M/ n edges are added between u i and the existing nodes drawn randomly according to the histogram.

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AGV – Aging Vertices A V i-1 u i For each potential edge: With probability μ , add the edge. With probability (1- μ ) , add a random edge between u i and any node in V i-1 , chosen according to the existing degrees.
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evolmod - Inferring How Networks Evolved 858L Inferring...

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