An Application of Innovative Algorithm of Integrated Social Network Analysis with Statistical Lstm Chain Network Analysis (Slstm-Cna) for Entrepreneurial Team Member Selection

Abstract

Selecting the right members for an entrepreneurial team is a critical step in ensuring the success and viability of a startup venture. Each team member brings unique skills, experiences, and perspectives that contribute to the overall vision and execution of the business idea. The factors such as cultural fit, adaptability, and a shared commitment to the venture's mission and values can foster cohesion and synergy within the team. This paper presents an innovative algorithm for the selection of entrepreneurial team members, Leveraging insights from social network dynamics and LSTM-based predictive modeling, the algorithm aims to identify individuals with the highest potential for contributing to the success of a startup venture. By analyzing social network structures, the algorithm identifies key influencers and connectors within professional networks, thereby facilitating the identification of candidates who possess valuable connections and collaborative capacities. Furthermore, SLSTM-CNA enables the algorithm to forecast future network trends and dynamics, aiding in the selection of team members who can adapt and thrive in evolving entrepreneurial ecosystems. simulated entrepreneurial ecosystem, the algorithm identified individuals with the highest potential for contributing to startup success based on various criteria, including network centrality, connectivity, and predictive network dynamics. For instance, individuals with a centrality score exceeding 0.7 demonstrated a 25% higher likelihood of facilitating valuable connections critical for business growth. Additionally, candidates identified through SLSTM-CNA exhibited a 30% increase in adaptability to changing network structures compared to those selected through conventional methods