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Joint estimation of preferential attachment and node fitness in growing complex networks

机译:日益复杂的网络中对优先连接和节点适应度的联合估计

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Complex network growth across diverse fields of science is hypothesized to be driven in the main by a combination of preferential attachment and node fitness processes. For measuring the respective influences of these processes, previous approaches make strong and untested assumptions on the functional forms of either the preferential attachment function or fitness function or both. We introduce a Bayesian statistical method called PAFit to estimate preferential attachment and node fitness without imposing such functional constraints that works by maximizing a log-likelihood function with suitably added regularization terms. We use PAFit to investigate the interplay between preferential attachment and node fitness processes in a Facebook wall-post network. While we uncover evidence for both preferential attachment and node fitness, thus validating the hypothesis that these processes together drive complex network evolution, we also find that node fitness plays the bigger role in determining the degree of a node. This is the first validation of its kind on real-world network data. But surprisingly the rate of preferential attachment is found to deviate from the conventional log-linear form when node fitness is taken into account. The proposed method is implemented in the R package PAFit.
机译:假设通过优先依附和节点适应性过程的组合,主要驱动跨科学各个领域的复杂网络增长。为了测量这些过程的各自影响,先前的方法对优先依附函数或适应性函数或两者的功能形式做出了未经检验的强有力的假设。我们引入了一种称为PAFit的贝叶斯统计方法,以估计优先连接和节点适应性,而不会施加通过适当添加正则项使对数似然函数最大化而起作用的功能约束。我们使用PAFit来研究Facebook墙帖网络中优先依恋和节点适应度过程之间的相互作用。虽然我们发现了优先连接和节点适应性的证据,从而验证了这些过程共同驱动复杂网络演进的假设,但我们还发现,节点适应性在确定节点的程度方面起着更大的作用。这是对真实网络数据的首次此类验证。但是令人惊讶的是,当考虑节点适应性时,发现优先连接的速率与传统的对数线性形式不同。所提出的方法在R包PAFit中实现。

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