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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
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Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
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Cohesin protein complexes are a molecular glue that holds two sister chromatids together. They play an important role both in mitosis and meiosis. In mitosis, all cohesin complexes present on the chromosomes are removed before the start of the anaphase stage.
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Overlapping community detection based on bridging structural features and fuzzy C-means.

Ailian Wang1, Mingwu Li1, Xuyang Gao1

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Summary

This study introduces an overlapping community optimization method (OSFCM) for social networks. The novel approach enhances accuracy in detecting complex community structures within networks.

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Area of Science:

  • Network Science
  • Data Mining
  • Social Network Analysis

Background:

  • Community structure detection is crucial for understanding complex networks.
  • Overlapping community structures more accurately represent real-world social networks than non-overlapping ones.
  • Existing methods may lack accuracy or efficiency in identifying overlapping communities.

Purpose of the Study:

  • To propose a novel overlapping community optimization method (OSFCM).
  • To improve the accuracy and performance of overlapping community detection in social networks.
  • To leverage network structural characteristics and fuzzy C-means clustering for community delineation.

Main Methods:

  • Abstracting feature vector matrices from network structural properties for each node.
  • Optimizing the feature matrix using a novel objective function gradient optimization method.
  • Applying Fuzzy C-Means (FCM) clustering for preliminary community delineation and subsequent calibration.

Main Results:

  • The OSFCM algorithm demonstrated higher delineation accuracy compared to existing methods.
  • Experimental results showed superior algorithmic performance across diverse real and synthetic network datasets.
  • The method effectively identified overlapping community structures in complex networks.

Conclusions:

  • The proposed OSFCM method is effective for detecting overlapping communities in social networks.
  • OSFCM offers improved accuracy and performance in community structure analysis.
  • This approach provides a valuable tool for understanding complex network organization.