Graphs and Matrices
Unit -3
Based on – “Matthew Ganis, Avinash Kohirkar, Social Media Analytics:
Techniques and Insights for Extracting Business Value Out of Social
Media Pearson 2016”
Presenter Name – Shiv Tripathi
April 2025
,Network Structures
Definition:
• Network structures define how individual entities (node
are connected through relationships (edges) in a netwo
• These structures help in analyzing the spread of
information, influence dynamics, and the formation of
communities. In social media analytics, network
structures enable the identification of key influencers,
understanding community behavior, and predicting
trends based on connectivity patterns.
, Importance of Network Structures in Social Media Analytics:
• Influencer Identification: Understanding who drives
trends, engagement, and interactions in social network
• Community Detection: Recognizing groups of users
with shared interests and behaviors.
• Information Spread Analysis: Studying how news,
trends, or misinformation propagates.
• Fraud Detection: Identifying bot networks, fake
accounts, and spam clusters.
• Recommendation Systems: Personalizing content b
analyzing how users are interconnected.
Unit -3
Based on – “Matthew Ganis, Avinash Kohirkar, Social Media Analytics:
Techniques and Insights for Extracting Business Value Out of Social
Media Pearson 2016”
Presenter Name – Shiv Tripathi
April 2025
,Network Structures
Definition:
• Network structures define how individual entities (node
are connected through relationships (edges) in a netwo
• These structures help in analyzing the spread of
information, influence dynamics, and the formation of
communities. In social media analytics, network
structures enable the identification of key influencers,
understanding community behavior, and predicting
trends based on connectivity patterns.
, Importance of Network Structures in Social Media Analytics:
• Influencer Identification: Understanding who drives
trends, engagement, and interactions in social network
• Community Detection: Recognizing groups of users
with shared interests and behaviors.
• Information Spread Analysis: Studying how news,
trends, or misinformation propagates.
• Fraud Detection: Identifying bot networks, fake
accounts, and spam clusters.
• Recommendation Systems: Personalizing content b
analyzing how users are interconnected.
