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

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