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  Mastering Community Detection: Avoid These 3 Common Mistakes with k-Clique Communities By Talent Navigator • Published Apr 30, 2025 • 1 min read https://www.youtube.com/watch?v=b_qeuGjUbOQ&ab_channel=TalentNavigator k Clique Community Mistakes ads via Carbo The video explains common mistakes made in k clique community detection within networks. It discusses the conditions under which k communities emerge in random graphs, the importance of connection probabilities, and the use of link clustering to identify multiple communities based on relationships rather than just nodes. Emergence of k Communities The video elaborates on how k communities can emerge in random networks, emphasizing that sufficient density is crucial. If the connection probability exceeds a certain threshold, it increases the likelihood of at least one large k community forming, highlighting key formulas for k=2 and k=3. Critical Connection Probability The critical connection probability (PCK)...

  Understanding the Rescorla-Wagner Model: A Deep Dive into Associative Learning By Talent Navigator • Published Apr 29, 2025 • 5 min read https://www.youtube.com/watch?v=XSIZhktTBO8&ab_channel=TalentNavigator Understanding how individuals learn is a complex and multifaceted topic. Among the prominent theories that seek to explain the mechanisms of learning is the Rescorla-Wagner Model. This model focuses on associative strength and expectation in learning trials. In this article, we will explore the intricacies of the Rescorla-Wagner Model, its applications, and its implications in the world of behavioral psychology. What is the Rescorla-Wagner Model? Developed in the 1970s by psychologists Robert A. Rescorla and Allan R. Wagner, the Rescorla-Wagner Model provides a mathematical framework for understanding how the strength of the association between a stimulus (Conditioned Stimulus or CS) and an unconditioned stimulus (US) is formed, modified, and extinguished o...

  Exploring Community Detection in Networks: Techniques, Models, and Applications By Talent Navigator • Published Apr 29, 2025 • 5 min read https://www.youtube.com/watch?v=yNxrq0jH2Io&ab_channel=TalentNavigator Understanding community structures in networks is vital for making sense of complex relationships in various fields, including social sciences, biology, and technology. Community detection enables researchers and analysts to identify groups of interconnected nodes that interact more frequently among themselves than with the broader network. This article will explore the core concepts, various models, and techniques of community detection in networks, shedding light on their real-world applications and relevance. What is a Community? A  community  in the context of network science refers to a group of nodes that are densely connected to each other. This means that the connections or edges among the nodes within the community are significantly stro...