Completely random measures for modeling power laws in sparse graphs

TL;DR

This paper introduces a new framework using completely random measures to model power-law behaviors in sparse graphs, addressing the need for scalable models that reflect real-world network properties.

Contribution

It proposes a novel graph generative model based on completely random measures that considers instantiating existing atoms as data grows, extending prior work by Caron and Fox.

Findings

Model exhibits desirable asymptotic power-law behavior

Framework captures both existing members and new nodes in networks

Simulations support the model's scalability and power-law properties

Abstract

Network data appear in a number of applications, such as online social networks and biological networks, and there is growing interest in both developing models for networks as well as studying the properties of such data. Since individual network datasets continue to grow in size, it is necessary to develop models that accurately represent the real-life scaling properties of networks. One behavior of interest is having a power law in the degree distribution. However, other types of power laws that have been observed empirically and considered for applications such as clustering and feature allocation models have not been studied as frequently in models for graph data. In this paper, we enumerate desirable asymptotic behavior that may be of interest for modeling graph data, including sparsity and several types of power laws. We outline a general framework for graph generative models using completely random measures; by contrast to the pioneering work of Caron and Fox (2015), we consider instantiating more of the existing atoms of the random measure as the dataset size increases rather than adding new atoms to the measure. We see that these two models can be complementary; they respectively yield interpretations as (1) time passing among existing members of a network and (2) new individuals joining a network. We detail a particular instance of this framework and show simulated results that suggest this model exhibits some desirable asymptotic power-law behavior.