Generating Synthetic Citation Networks with Communities

2026-04-28

AI Summary

Existing synthetic graph generation methods struggle to simultaneously capture both the community structure and the near-acyclic nature of real citation networks, limiting the evaluation of community detection and network mining algorithms. This work systematically evaluates twelve directed graph generators on seven real-world citation networks and introduces an edge direction reversal strategy to break cycles and emulate citation flow. It further distinguishes, for the first time, between endogenous and exogenous mesoscale structural similarities, revealing that high-parameter models often overfit by memorizing community statistics. Building on these insights, we propose Citation Seeder—a novel algorithm combining a Price-Pareto iterative mechanism with a degree-corrected stochastic block model. Evaluated using only 26 multidimensional metrics, Citation Seeder achieves interpretable and efficient citation network generation with four orders of magnitude fewer parameters, preserving structural fidelity while significantly enhancing computational efficiency and generalization capability.

Abstract

Generating realistic synthetic citation, patent, or component dependency networks is essential for benchmarking community detection, graph visualisation, and network data mining algorithms. We present the first systematic comparison of generators of directed graphs that are nearly acyclic and have a ground-truth community structure. We evaluate 12 methods across 7 real citation networks and 26 metrics. We propose the practice of reversing directions of edges in static generators to break cycles and induce a citation-like flow, which significantly improves the performance of a degree-corrected Stochastic Block Model. Our novel methodological approach to evaluating community detection benchmarks distinguishes between endogenous and exogenous mesoscopic similarities, with the latter proving more important. This distinction reveals that high-parameter models suffer from overfitting by memorising planted community statistics which lead to their failing to produce realistic networks. Finally, we introduce the Citation Seeder (CS) algorithm, an iterative generator grounded in the Price-Pareto model of citation networks, with interpretable parameters and O(N+E) runtime. CS achieves competitive results against the best-performing baselines while using up to four orders of magnitude fewer parameters and providing a clean framework for explaining and predicting a network's future growth.

Problem

• synthetic citation networks
• community detection
• graph generation
• benchmarking
• directed acyclic graphs

Innovation

• synthetic citation networks
• community detection benchmarking
• Citation Seeder algorithm
• degree-corrected Stochastic Block Model
• mesoscopic similarity