Growing networks with superjoiners
Ameerah Jabr-Hamdan, Jie Sun, Daniel ben‐Avraham
Abstract
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Ameerah Jabr-Hamdan, Jie Sun, Daniel ben‐Avraham
Abstract
Open-access reader
We study the Krapivsky-Redner (KR) network growth model, but where new nodes can connect to any number of existing nodes, m, picked from a power-law distribution p(m)∼m^{-α}. Each of the m new connections is still carried out as in the KR model with probability redirection r (corresponding to degree exponent γ_{KR}=1+1/r in the original KR model). The possibility to connect to any number of nodes resembles a more realistic type of growth in several settings, such as social networks, routers networks, and networks of citations. Here we focus on the in-, out-, and total-degree distributions and on the potential tension between the degree exponent α, characterizing new connections (outgoing links), and the degree exponent γ_{KR}(r) dictated by the redirection mechanism.
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We study the Krapivsky-Redner (KR) network growth model, but where new nodes can connect to any number of existing nodes, m, picked from a power-law distribution p(m)∼m^{-α}. Each of the m new connections is still carried out as in the KR model with probability redirection r (corresponding to degree exponent γ_{KR}=1+1/r in the original KR model). The possibility to connect to any number of nodes resembles a more realistic type of growth in several settings, such as social networks, routers networks, and networks of citations. Here we focus on the in-, out-, and total-degree distributions and on the potential tension between the degree exponent α, characterizing new connections (outgoing links), and the degree exponent γ_{KR}(r) dictated by the redirection mechanism.
Key concepts: Computer science