# Construction of Directed 2K Graphs

## TL;DR

This paper introduces a method for constructing synthetic directed graphs that accurately replicate real-world degree correlations, using a novel directed 2K framework and an efficient algorithm.

## Contribution

It defines the directed 2K construction problem, provides realizability conditions, and develops an algorithm that outperforms existing methods in generating realistic synthetic graphs.

## Key findings

- The algorithm successfully creates synthetic graphs matching real-world degree correlations.
- It outperforms state-of-the-art approaches in realism and efficiency.
- Application to Twitter data demonstrates practical effectiveness.

## Abstract

We study the problem of constructing synthetic graphs that resemble real-world directed graphs in terms of their degree correlations. We define the problem of directed 2K construction (D2K) that takes as input the directed degree sequence (DDS) and a joint degree and attribute matrix (JDAM) so as to capture degree correlation specifically in directed graphs. We provide necessary and sufficient conditions to decide whether a target D2K is realizable, and we design an efficient algorithm that creates realizations with that target D2K. We evaluate our algorithm in creating synthetic graphs that target real-world directed graphs (such as Twitter) and we show that it brings significant benefits compared to state-of-the-art approaches.