Abstract
Abstract. In this paper we made analysis for the stack overflow tags by using different criteria's in network science, one of the advantages of network analysis is that complex of connections can be made cleared, we started this work in first step by extracted data from dataset after that applied network concepts node degree distribution, node importance (centrality measures), also we provided a brief demonstration of how we can use graph network and tools to analyze semi-structured text as (Tags).
Highlights
Stack Overflow is the largest online community for programmers to learn, share their knowledge, and advance their careers
We carry out an analysis of the Stack Overflow tags viewed as a network, or a graph
Stack Overflow Dataset consists of following files that are treated as tables in our Database Design Figure 2:
Summary
Stack Overflow is the largest online community for programmers to learn, share their knowledge, and advance their careers. Stack Overflow, because it allowing answering users to monitor questions relevant to their old of expertise and to answer promptly to submitted questions. Users holding an “advanced” status in Stack Overflow are allowed to generate new tags, with the rest of the community limiting themselves in using the existing tags for their questions. We carry out an analysis of the Stack Overflow tags viewed as a network, or a graph. The resulting network edges are weighted, the higher the weight of the relevant edge between them will be; for example, if the tag java is found to co-exist with the tag android in 1000 questions, the weight of the edge between the nodes java and android in our graph will be 1000. Section 3: expose the construction of our first graph based on the whole data set, along with some limited analysis. We first created the project on google cloud after that we connected this project with google datasets and we used google big query tools to extract data Figure 1: shows the steps
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