Influence, originality and similarity in directed acyclic graphs

TL;DR

This paper presents a novel framework for analyzing directed acyclic graphs using random walks, enabling the evaluation of node influence, discovery of seminal nodes, and a new similarity metric tested in recommendation tasks.

Contribution

It introduces a new framework based on random walks for network analysis in DAGs, including influence measurement and a similarity metric validated in recommendation scenarios.

Findings

The influence measure effectively identifies influential nodes.

The similarity metric performs comparably to classical metrics.

The framework is applicable to citation networks and recommendation systems.

Abstract

We introduce a framework for network analysis based on random walks on directed acyclic graphs where the probability of passing through a given node is the key ingredient. We illustrate its use in evaluating the mutual influence of nodes and discovering seminal papers in a citation network. We further introduce a new similarity metric and test it in a simple personalized recommendation process. This metric's performance is comparable to that of classical similarity metrics, thus further supporting the validity of our framework.