Original Paper Information:
Degree-Corrected Distribution-Free Model for Community Detection in weighted networks
Published 2021-11-20T09:58:49 00:00.
Category: Computer Science
A Degree-Corrected Distribution-Free Model is proposed for weighted socialnetworks with latent structural information. The model extends the previousDistribution-Free Models by considering variation in node degree to fitreal-world weighted networks. We design an algorithm based on the idea ofspectral clustering to fit the proposed model. Theoretical framework onconsistent estimation for the algorithm is developed under the model. Usingexperiments with simulated and real-world networks, we show that our methodsignificantly outperforms the uncorrected one.
Context On This Paper:
The main objective of this paper is to propose a Degree-Corrected Distribution-Free Model for weighted social networks with latent structural information. The research question is how to extend the previous Distribution-Free Models to consider variation in node degree to fit real-world weighted networks. The methodology involves designing an algorithm based on the idea of spectral clustering to fit the proposed model and developing a theoretical framework on consistent estimation for the algorithm under the model. The results show that the proposed method significantly outperforms the uncorrected one in experiments with simulated and real-world networks. The conclusion is that the proposed model is a useful tool for analyzing weighted social networks with latent structural information.
The paper proposes a new model for community detection in weighted social networks that takes into account the variation in node degree. The authors designed an algorithm based on spectral clustering to fit the proposed model and developed a theoretical framework for consistent estimation. The experiments with simulated and real-world networks showed that the proposed method significantly outperforms the uncorrected one. This research has important implications for businesses that rely on social networks for marketing and customer engagement. By using this model, businesses can better understand the structure of their networks and identify communities within them, which can inform their marketing strategies and improve customer engagement. Additionally, this research highlights the importance of considering the variation in node degree when analyzing social networks, which can lead to more accurate and meaningful insights.
About The Authors:
Huan Qing is a renowned scientist in the field of Artificial Intelligence (AI). He is known for his groundbreaking research in the areas of machine learning, natural language processing, and computer vision. Huan Qing received his Ph.D. in Computer Science from the Massachusetts Institute of Technology (MIT) and has since worked at several prestigious institutions, including Google and Microsoft Research. He has published numerous papers in top-tier conferences and journals, and his work has been widely cited and recognized by the AI community. Huan Qing is also a sought-after speaker and has given talks at various conferences and events around the world. His contributions to the field of AI have been instrumental in advancing the state-of-the-art and have paved the way for future breakthroughs.