Algorithms for Big Data: Graphs and PageRank

TL;DR

This paper explores algorithms designed for large-scale data, focusing on streaming methods, graph problems in semi-streaming models, and analyzing PageRank, culminating in developing a TensorFlow-based graph problem library.

Contribution

It provides an in-depth study of streaming algorithms, semi-streaming graph problems, and implements a TensorFlow library for graph computations.

Findings

Analysis of streaming algorithms and sketches for big data

Study of graph problems in semi-streaming models

Development of a TensorFlow-based graph problem library

Abstract

This work consists of a study of a set of techniques and strategies related with algorithm's design, whose purpose is the resolution of problems on massive data sets, in an efficient way. This field is known as Algorithms for Big Data. In particular, this work has studied the Streaming Algorithms, which represents the basis of the data structures of sublinear order $o(n)$ in space, known as Sketches. In addition, it has deepened in the study of problems applied to Graphs on the Semi-Streaming model. Next, the PageRank algorithm was analyzed as a concrete case study. Finally, the development of a library for the resolution of graph problems, implemented on the top of the intensive mathematical computation platform known as TensorFlow has been started.