Prediction Of Arrival Of Nodes In A Scale Free Network

TL;DR

This paper introduces a novel method to partially predict the order of node arrivals in scale-free networks generated by the Barabasi-Albert model, outperforming existing centrality-based approaches with over 80% accuracy.

Contribution

The paper proposes a new strategy for predicting node arrival order in scale-free networks, improving accuracy over traditional centrality measures.

Findings

Method achieves above 80% accuracy in bin-based node arrival prediction.

Outperforms existing centrality measure approaches.

Applicable to networks generated by the Barabasi-Albert model.

Abstract

Most of the networks observed in real life obey power-law degree distribution. It is hypothesized that the emergence of such a degree distribution is due to preferential attachment of the nodes. Barabasi-Albert model is a generative procedure that uses preferential attachment based on degree and one can use this model to generate networks with power-law degree distribution. In this model, the network is assumed to grow one node every time step. After the evolution of such a network, it is impossible for one to predict the exact order of node arrivals. We present in this article, a novel strategy to partially predict the order of node arrivals in such an evolved network. We show that our proposed method outperforms other centrality measure based approaches. We bin the nodes and predict the order of node arrivals between the bins with an accuracy of above 80%.