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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
Published on: October 19, 2021
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Higher-order organization of complex networks.
Austin R Benson1, David F Gleich2, Jure Leskovec3
1Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305, USA.
Science (New York, N.Y.)
|July 9, 2016
Summary
We developed a new framework to cluster complex networks based on higher-order connectivity patterns. This method reveals hidden organizational structures in networks, like in neuronal and transportation systems.
Area of Science:
- Network science
- Complex systems analysis
- Graph theory
Background:
- Networks are crucial for modeling complex systems across various scientific disciplines.
- While lower-order network patterns (nodes, edges) are well-understood, higher-order network organization remains largely unexplored.
- Understanding subgraph patterns is key to uncovering deeper network structures.
Purpose of the Study:
- To develop a generalized framework for clustering networks based on higher-order connectivity patterns.
- To provide mathematical guarantees for the optimality of network clusters.
- To enable scalable analysis of large-scale networks.
Main Methods:
- Developed a generalized framework for network clustering using higher-order connectivity.
- Incorporated mathematical guarantees for cluster optimality.
- Ensured the framework scales to networks with billions of edges.
Main Results:
- Revealed significant higher-order organization in various complex networks.
- Identified information propagation units in neuronal networks.
- Uncovered hub structures within transportation networks.
- Demonstrated the effectiveness of higher-order clustering in exposing network organization.
Conclusions:
- Complex networks possess rich, higher-order organizational structures.
- Clustering based on higher-order connectivity patterns is effective in revealing these structures.
- The developed framework offers a scalable and mathematically sound approach to network analysis.
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