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Understanding the Dynamics of the Stack Overflow Community through Social Network Analysis and Graph Algorithms

Rapheal Cyril Igbudu, Rowanda Ahmed

TL;DR

This thesis examines the evolving landscape of health information quality within the digital ecosystem, emphasizing the challenges posed and the multifaceted nature of quality, and introduces an ensemble deep learning model for traffic flow forecasting, and an efficient multi-objective optimization method for influence maximization.

Abstract

This thesis conducts a focused literature review on online communities, centering on Stack Overflow, employing social network analysis and graph algorithms. It examines the evolving landscape of health information quality within the digital ecosystem, emphasizing the challenges posed and the multifaceted nature of quality. The significance of online communities, notably Stack Overflow, as hubs for social interaction and knowledge sharing is underscored. Proposing advanced approaches, the thesis introduces an ensemble deep learning model for traffic flow forecasting, an efficient multi-objective optimization method for influence maximization, and a graph convolutional neural network-based approach for link prediction.

Understanding the Dynamics of the Stack Overflow Community through Social Network Analysis and Graph Algorithms

TL;DR

This thesis examines the evolving landscape of health information quality within the digital ecosystem, emphasizing the challenges posed and the multifaceted nature of quality, and introduces an ensemble deep learning model for traffic flow forecasting, and an efficient multi-objective optimization method for influence maximization.

Abstract

This thesis conducts a focused literature review on online communities, centering on Stack Overflow, employing social network analysis and graph algorithms. It examines the evolving landscape of health information quality within the digital ecosystem, emphasizing the challenges posed and the multifaceted nature of quality. The significance of online communities, notably Stack Overflow, as hubs for social interaction and knowledge sharing is underscored. Proposing advanced approaches, the thesis introduces an ensemble deep learning model for traffic flow forecasting, an efficient multi-objective optimization method for influence maximization, and a graph convolutional neural network-based approach for link prediction.
Paper Structure (63 sections, 5 figures)

This paper contains 63 sections, 5 figures.

Table of Contents

  1. Introduction
  2. Background and Context
  3. Historical development of the research topic
  4. Current state of research on social network analysis in online communities
  5. Comparative Analysis
  6. Graph-Based Approaches (2018)
  7. Cognitive and Emotional Networks (2019)
  8. Graph Neural Networks (GNNs) (2020)
  9. Network Embedding Techniques (2021)
  10. Gaps in Available Research
  11. Summary
  12. Methodological Review
  13. Description of research methods used in reviewed studies
  14. Research methods used in the reviewed studies
  15. Data collection
  16. ...and 48 more sections

Figures (5)

  • Figure 1: Node Degree Centrality
  • Figure 2: Application of linear algebra models to directed graphs: This figure demonstrates the importance of linear algebra models in preparing data for machine learning tasks. These models help in structuring and understanding complex network data, ensuring accurate data representation for advanced analytical techniques.
  • Figure 3: A Simple Baseline Algorithm for Graph Classification ref37: The algorithm provides a foundational approach for more complex graph classification tasks, offering a benchmark against which other methods can be compared. It is a crucial step in developing more sophisticated models that can accurately classify and predict graph behaviors.
  • Figure : Figures 1: Graph network visualizer
  • Figure : Figures 2a and 2b illustrate specific outcomes from the Neo4j analysis on Stack Overflow data