Network Essence: PageRank Completion and Centrality-Conforming Markov Chains

TL;DR

This paper explores the algebraic properties of personalized PageRank matrices, proposing a Markovian network completion approach to better understand the underlying essence of complex network data.

Contribution

It introduces a novel perspective on network analysis by emphasizing the algebraic properties of PageRank matrices and proposing a Markovian completion framework for network understanding.

Findings

Personalized PageRank matrices exhibit specific algebraic properties.

Markovian closure offers a new way to complete and analyze network data.

The approach motivates systematic development of network theory.

Abstract

Ji\v{r}\'i Matou\v{s}ek (1963-2015) had many breakthrough contributions in mathematics and algorithm design. His milestone results are not only profound but also elegant. By going beyond the original objects --- such as Euclidean spaces or linear programs --- Jirka found the essence of the challenging mathematical/algorithmic problems as well as beautiful solutions that were natural to him, but were surprising discoveries to the field. In this short exploration article, I will first share with readers my initial encounter with Jirka and discuss one of his fundamental geometric results from the early 1990s. In the age of social and information networks, I will then turn the discussion from geometric structures to network structures, attempting to take a humble step towards the holy grail of network science, that is to understand the network essence that underlies the observed sparse-and-multifaceted network data. I will discuss a simple result which summarizes some basic algebraic properties of personalized PageRank matrices. Unlike the traditional transitive closure of binary relations, the personalized PageRank matrices take "accumulated Markovian closure" of network data. Some of these algebraic properties are known in various contexts. But I hope featuring them together in a broader context will help to illustrate the desirable properties of this Markovian completion of networks, and motivate systematic developments of a network theory for understanding vast and ubiquitous multifaceted network data.