Comparing Personalized Relevance Algorithms for Directed Graphs

TL;DR

This paper introduces an interactive web platform for comparing relevance algorithms on directed graphs, featuring a new algorithm called Cyclerank that leverages cyclic paths for improved relevance scoring.

Contribution

The paper presents a novel algorithm Cyclerank and an interactive platform for comparing multiple relevance algorithms on directed graphs.

Findings

Cyclerank addresses limitations of existing algorithms.

The platform supports diverse datasets and easy algorithm integration.

Users can explore hidden relationships in graph data.

Abstract

We present an interactive Web platform that, given a directed graph, allows identifying the most relevant nodes related to a given query node. Besides well-established algorithms such as PageRank and Personalized PageRank, the demo includes Cyclerank, a novel algorithm that addresses some of their limitations by leveraging cyclic paths to compute personalized relevance scores. Our demo design enables two use cases: (a) algorithm comparison, comparing the results obtained with different algorithms, and (b) dataset comparison, for exploring and gaining insights into a dataset and comparing it with others. We provide 50 pre-loaded datasets from Wikipedia, Twitter, and Amazon and seven algorithms. Users can upload new datasets, and new algorithms can be easily added. By showcasing efficient algorithms to compute relevance scores in directed graphs, our tool helps to uncover hidden relationships within the data, which makes of it a valuable addition to the repertoire of graph analysis algorithms.