Graph processing: a problem with no clear victor

Graph processing: a problem with no clear victor

Graph processing: a problem with no clear victor. Do you know the most popular graph processing solution? No? Don’t worry. There is no such thing yet.

We all depend on the Internet to search for potential solutions to technical problems. For example, for big data problems after five minutes in Google you will find out that Spark may help you. Even if you have no idea about what is Spark you will come across this name. Something similar occurs to TensorFlow when searching for deep learning solutions, Kubernetes for cloud, Docker for containers… It seems that there is always one platform/framework/library for every buzzword in computer science. However, try to look for a graph processing solution. You will find out that there is no clear victor. And I find this quite surprising.

In 2015, I and my colleagues at Inria published an article proposing a middleware that could inspire developers to offer a generic framework to implement distributed graph processing solutions. We had a strong feeling that there was not a consistent proposal to accelerate the development of massive graph processing solutions. And this is surprising if we consider that The Graph500 benchmark has some computing-intensive problems using graphs. The explosion of social networks after the born of Facebook and Twitter captured the attention of the research community and put on the table new problems in terms of computation and scalability. Additionally, there is a vast number of problems that use graphs as the underlying data structure to be used. Graphs are used for fraud detection, game theory, and a vast number of data-related problems.

algorithms technology computer-science programming data-science

Bootstrap 5 Complete Course with Examples

Bootstrap 5 Tutorial - Bootstrap 5 Crash Course for Beginners

Nest.JS Tutorial for Beginners

Hello Vue 3: A First Look at Vue 3 and the Composition API

Building a simple Applications with Vue 3

Deno Crash Course: Explore Deno and Create a full REST API with Deno

How to Build a Real-time Chat App with Deno and WebSockets

Convert HTML to Markdown Online

HTML entity encoder decoder Online

Data Science Course in Dallas

Become a data analysis expert using the R programming language in this [data science](https://360digitmg.com/usa/data-science-using-python-and-r-programming-in-dallas "data science") certification training in Dallas, TX. You will master data...

50 Data Science Jobs That Opened Just Last Week

Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.

Applications Of Data Science On 3D Imagery Data

The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.

Program a Quantum Computer Today

Your options on how to start with working with today’s quantum computers. Quantum computing is one of the most rapidly advancing technologies.

77 Programming Language Q&A (P4)

Check the bottom of the page for links to the other questions and answers I’ve come up with to make you a great Computer Scientist (when it comes to Programming Languages).