Geometric Graph Properties of the Spatial Preferred Attachment model

TL;DR

This paper analyzes the geometric properties of the SPA model, showing how to infer metric distances from graph structure and revealing a power-law distribution of edge lengths.

Contribution

It provides a theoretical framework linking common neighbors to metric distances and characterizes the edge length distribution in SPA graphs.

Findings

Metric distance can be inferred from common neighbors.

Edge length distribution has three regimes.

Tail of distribution follows a power law.

Abstract

The spatial preferred attachment (SPA) model is a model for networked information spaces such as domains of the World Wide Web, citation graphs, and on-line social networks. It uses a metric space to model the hidden attributes of the vertices. Thus, vertices are elements of a metric space, and link formation depends on the metric distance between vertices. We show, through theoretical analysis and simulation, that for graphs formed according to the SPA model it is possible to infer the metric distance between vertices from the link structure of the graph. Precisely, the estimate is based on the number of common neighbours of a pair of vertices, a measure known as {\sl co-citation}. To be able to calculate this estimate, we derive a precise relation between the number of common neighbours and metric distance. We also analyze the distribution of {\sl edge lengths}, where the length of an edge is the metric distance between its end points. We show that this distribution has three different regimes, and that the tail of this distribution follows a power law.