[Paper] Enhanced Pruning for Distributed Closeness Centrality under Multi-Packet Messaging

Source: arXiv - 2512.11512v1

Overview

The paper presents a new twist on distributed closeness centrality computation that dramatically cuts down the chatter between nodes. By bundling information into multi‑packet messages, the authors turn a communication‑heavy pruning algorithm into a leaner, faster solution—making large‑scale network analysis feasible on real‑world distributed systems.

Key Contributions

• Multi‑packet messaging layer that aggregates several pruning updates into a single transmission.
• Enhanced pruning algorithm that retains the original approximation guarantees while slashing the total number of messages exchanged.
• Theoretical analysis showing bounded error and memory overhead despite the batching.
• Extensive empirical evaluation on synthetic and real‑world graphs (up to millions of nodes) demonstrating up to 70 % reduction in message count and up to 45 % speed‑up in wall‑clock time.
• Open‑source reference implementation compatible with common distributed graph processing frameworks (e.g., Apache Giraph, GraphX).

Methodology

1. Baseline Pruning Recap – The classic distributed pruning technique iteratively removes nodes whose distance estimates cannot affect the final closeness scores, sending a small “prune” packet each round.
2. Batching Strategy – Instead of sending a prune packet per node, each processor collects all pending prune updates for a local batch window (configurable size or time‑out) and packs them into a single larger message.
3. Multi‑packet Protocol – The protocol defines a header that lists the number of embedded updates and their target IDs, followed by a compact payload. Receivers unpack the batch, apply updates locally, and optionally generate a new batch for the next round.
4. Memory Management – Nodes keep a short‑term buffer for pending updates; the authors show that the buffer size grows sub‑linearly with graph size, keeping memory usage practical.
5. Correctness Guarantees – By proving that batching does not reorder or drop updates, the authors demonstrate that the approximation error remains identical to the original pruning method.

Results & Findings

The experiments span random geometric graphs, scale‑free networks, and a real social‑media graph (≈2 M edges). Across the board, the multi‑packet version kept the closeness estimates within the original error bounds while delivering the communication and speed gains highlighted above.

Practical Implications

• Scalable Network Monitoring – Systems that need to continuously assess node importance (e.g., IoT mesh health, CDN node selection) can now run closeness centrality locally without flooding the control plane.
• Edge‑Centric Analytics – Edge devices with limited bandwidth (e.g., autonomous drones, sensor clusters) can compute centrality collaboratively, preserving battery life and reducing latency.
• Graph‑Processing Frameworks – The batching layer can be dropped into existing Pregel‑style APIs, giving developers a plug‑and‑play performance boost for any algorithm that relies on iterative pruning or message‑heavy propagation.
• Cost Savings – Fewer network packets translate directly into lower cloud‑networking bills for large‑scale analytics pipelines (e.g., social‑graph insights, fraud detection networks).

Limitations & Future Work

• Memory Trade‑off – The buffer required for batching grows with batch size; on extremely memory‑constrained nodes the approach may need adaptive windowing.
• Batch Size Tuning – Selecting optimal batch thresholds is currently heuristic; an auto‑tuning mechanism could make the method more robust across heterogeneous deployments.
• Fault Tolerance – The paper assumes reliable delivery; handling packet loss or node crashes in the multi‑packet regime remains an open challenge.
• Extension to Other Centralities – Future research could explore whether similar batching ideas benefit betweenness, eigenvector, or community‑detection algorithms in distributed settings.

Bottom line: By turning a flood of tiny messages into a handful of well‑packed packets, this work makes decentralized closeness centrality practical for the massive, bandwidth‑sensitive networks that modern developers and operators grapple with today.

Authors

• Patrick D. Manya
• Eugene M. Mbuyi
• Gothy T. Ngoie
• Jordan F. Masakuna

Paper Information

• arXiv ID: 2512.11512v1
• Categories: cs.DC, cs.SI
• Published: December 12, 2025
• PDF: Download PDF