Social Network Influencer Ranking Based On Link Analysis
Supervisor: Prof. Papagelis
Type: Network Analysis
Overview
This capstone project, under the supervision of Prof. Papagelis, explores the application of link analysis methods, like PageRank and weighted PageRank, to rank influencers on social networking services. It focuses on the burgeoning field of digital marketing and online communication, aiming to provide a systematic approach to measuring the influence and reach of key SNS personalities.
Methodology
- Data Collection: Gathering data from various social networking platforms.
- Implementation of Link Analysis Algorithms: Utilizing PageRank and weighted PageRank for influencer ranking.
- Algorithm Optimization: Tuning algorithms for optimal performance in the SNS context.
Evaluation
- Effectiveness of Algorithms: Analyzing the precision and accuracy of the link analysis methods.
- Comparative Analysis: Comparing the performance of PageRank and weighted PageRank.
- Impact Assessment: Evaluating the impact of influencers identified by the algorithms.
Key Findings
- Validation of link analysis methods like PageRank in the SNS domain.
- Insights into the varying impacts of standard and weighted PageRank.
- Recommendations for effective influencer ranking in digital marketing.
Personal Contributions
- Development of the data collection methodology.
- Implementation and optimization of link analysis algorithms.
- Critical analysis and interpretation of the results.
Conclusion
The project successfully demonstrates the applicability of link analysis algorithms in the context of social media, providing a new perspective on influencer ranking in digital marketing.
Project Report
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Project Slides
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