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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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Communication-Optimal Distributed Dynamic Graph Clustering.

Chun Jiang Zhu1, Tan Zhu1, Kam-Yiu Lam2

  • 1Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA.

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PubMed
Summary
This summary is machine-generated.

We developed communication-efficient algorithms for clustering dynamic graphs distributively. Our methods achieve near-optimal communication costs and high-quality clustering, comparable to centralized approaches.

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Area of Science:

  • Graph theory
  • Distributed systems
  • Machine learning

Background:

  • Clustering large-scale dynamic graphs presents challenges due to distributed data and frequent updates.
  • Existing centralized methods are inefficient for distributed graph data.

Purpose of the Study:

  • To propose communication-efficient algorithms for distributed graph clustering.
  • To analyze the performance of these algorithms in message passing and blackboard models.
  • To ensure high clustering quality comparable to centralized methods.

Main Methods:

  • Developed two novel algorithms for distributed graph clustering.
  • Analyzed communication costs in message passing and blackboard models.
  • Evaluated algorithms on synthetic and real-life datasets.

Main Results:

  • Algorithms achieve near-optimal communication costs: Õ(ns) and Õ(n + s).
  • Clustering quality is comparable to standard centralized algorithms.
  • Experimental results confirm communication efficiency and quality.

Conclusions:

  • The proposed algorithms offer an efficient solution for distributed graph clustering.
  • These methods are suitable for dynamic graphs in various applications like citation networks and web graphs.