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arxiv: 1607.03850 · v1 · pith:JQPEOCFWnew · submitted 2016-07-13 · ⚛️ physics.soc-ph · cond-mat.stat-mech

Structural Transitions in Dense Networks

classification ⚛️ physics.soc-ph cond-mat.stat-mech
keywords fracdensecliquesneighborsnetworknetworksnodenodes
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We introduce an evolving network model in which a new node attaches to a randomly selected target node and also to each of its neighbors with probability $p$. The resulting network is sparse for $p<\frac{1}{2}$ and dense (average degree increasing with number of nodes $N$) for $p\geq \frac{1}{2}$. In the dense regime, individual networks realizations built by this copying mechanism are disparate and not self-averaging. Further, there is an infinite sequence of structural anomalies at $p=\frac{2}{3}$, $\frac{3}{4}$, $\frac{4}{5}$, etc., where the dependences on $N$ of the number of triangles (3-cliques), 4-cliques, undergo phase transitions. When linking to second neighbors of the target can occur, the probability that the resulting graph is complete---where all nodes are connected---is non-zero as $N\to\infty$.

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