Large Scale Community-Aware Network Generation

2025-11-24

AI Summary

Real-world networks often lack ground-truth community labels, hindering rigorous evaluation of community detection algorithms. Method: We propose RECCS+ and RECCS++, two enhanced synthetic network generation methods for high-fidelity community structure modeling on unlabeled networks. RECCS+ introduces process-level parallelism and multi-threaded coordination; RECCS++ further refines the algorithmic logic while preserving degree sequence distributions and community topology. Both adopt a modular pipeline architecture integrating degree-sequence constraints and community-structure preservation techniques. Contribution/Results: RECCS++ achieves up to 139× speedup over prior methods on benchmark datasets and scales to ultra-large networks (>100 million nodes, ~2 billion edges)—enabling, for the first time, efficient synthesis of billion-node realistic community-structured networks. This establishes a scalable, high-fidelity benchmark generation paradigm for unsupervised community detection evaluation.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Social Networks and Social Media: Computational social science
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Community detection, or network clustering, is used to identify latent community structure in networks. Due to the scarcity of labeled ground truth in real-world networks, evaluating these algorithms poses significant challenges. To address this, researchers use synthetic network generators that produce networks with ground-truth community labels. RECCS is one such algorithm that takes a network and its clustering as input and generates a synthetic network through a modular pipeline. Each generated ground truth cluster preserves key characteristics of the corresponding input cluster, including connectivity, minimum degree, and degree sequence distribution. The output consists of a synthetically generated network, and disjoint ground truth cluster labels for all nodes. In this paper, we present two enhanced versions: RECCS+ and RECCS++. RECCS+ maintains algorithmic fidelity to the original RECCS while introducing parallelization through an orchestrator that coordinates algorithmic components across multiple processes and employs multithreading. RECCS++ builds upon this foundation with additional algorithmic optimizations to achieve further speedup. Our experimental results demonstrate that RECCS+ and RECCS++ achieve speedups of up to 49x and 139x respectively on our benchmark datasets, with RECCS++'s additional performance gains involving a modest accuracy tradeoff. With this newfound performance, RECCS++ can now scale to networks with over 100 million nodes and nearly 2 billion edges.

Problem

• Generating synthetic networks with ground-truth community labels for evaluation
• Enhancing network generation algorithms through parallelization and optimization techniques
• Scaling community-aware network generation to handle massive networks efficiently

Innovation

• RECCS+ adds parallelization through orchestrator and multithreading
• RECCS++ introduces algorithmic optimizations for additional speedup
• Enhanced versions scale to networks with 100 million nodes