Evidence of swarm intelligence in collective cultures: Identifying the use of the swarm goal directive of productivity in Pacific organisation systems as well as getween genders

Intrigued by the existence of societies outside that of the human population, scientists have ventured to study social aggregations within insects to seek insights on effective colonizing. The most popular of these social aggregations are colonies of ants and bees. In studying these groups of social insects researchers have developed algorithms loosely termed swarm intelligence that increase work efficiency within businesses and other social organizations (Bonabeau & Meyer, 2001). A subsequent proliferation of research in surrounding fields has allowed for investigation of key variables that improve work on a global scale (Bonabeau & Meyer, 2001). James Kennedy (1999), an initiator of swarm research, has suggested that there is a high correlation between systems that rely on each other for information and greater task accomplishment

Topic Similarity Networks: Visual Analytics for Large Document Sets

We investigate ways in which to improve the interpretability of LDA topic models by better analyzing and visualizing their outputs. We focus on examining what we refer to as topic similarity networks: graphs in which nodes represent latent topics in text collections and links represent similarity among topics. We describe efficient and effective approaches to both building and labeling such networks. Visualizations of topic models based on these networks are shown to be a powerful means of exploring, characterizing, and summarizing large collections of unstructured text documents. They help to "tease out" non-obvious connections among different sets of documents and provide insights into how topics form larger themes. We demonstrate the efficacy and practicality of these approaches through two case studies: 1) NSF grants for basic research spanning a 14 year period and 2) the entire English portion of Wikipedia.Comment: 9 pages; 2014 IEEE International Conference on Big Data (IEEE BigData 2014