Graphs and Matrices
Unit -2
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
March 2025
,• Overview:
• Understanding network structures is crucial in social media
analytics.
• This lecture explores foundational concepts such as adjacen
matrices, paths, connectivity, distance, breadth-first search
and network datasets.
• These concepts help in understanding how users, interactio
and information flow in social networks.
• Importance in Social Media Analytics:
• Helps in identifying influencers, viral content spread, and u
behavior analysis.
• Enables businesses to improve customer engagement
strategies.
, The Adjacency Matrix
• Definition:
• A square matrix used to represent a finite graph, indicating the presence (1) or absenc
of edges between nodes.
• The matrix can be directed or undirected.
• Structure:
• Rows and columns represent nodes.
• If node A is connected to node B, the matrix entry at (A, B) is 1, otherwise 0.
• Types:
• Unweighted adjacency matrix: Contains 0s and 1s.
• Weighted adjacency matrix: Stores edge weights instead of 1s.
• Example:
• Graph with nodes A, B, and C with edges A→B and B→C:
• |010|
• |001|
• |000|
• Use in Social Media Analytics:
• Represents user connections (friendships, followers, interactions).
• Used in recommendation algorithms.
• Analyzing network density and influence.
Unit -2
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
March 2025
,• Overview:
• Understanding network structures is crucial in social media
analytics.
• This lecture explores foundational concepts such as adjacen
matrices, paths, connectivity, distance, breadth-first search
and network datasets.
• These concepts help in understanding how users, interactio
and information flow in social networks.
• Importance in Social Media Analytics:
• Helps in identifying influencers, viral content spread, and u
behavior analysis.
• Enables businesses to improve customer engagement
strategies.
, The Adjacency Matrix
• Definition:
• A square matrix used to represent a finite graph, indicating the presence (1) or absenc
of edges between nodes.
• The matrix can be directed or undirected.
• Structure:
• Rows and columns represent nodes.
• If node A is connected to node B, the matrix entry at (A, B) is 1, otherwise 0.
• Types:
• Unweighted adjacency matrix: Contains 0s and 1s.
• Weighted adjacency matrix: Stores edge weights instead of 1s.
• Example:
• Graph with nodes A, B, and C with edges A→B and B→C:
• |010|
• |001|
• |000|
• Use in Social Media Analytics:
• Represents user connections (friendships, followers, interactions).
• Used in recommendation algorithms.
• Analyzing network density and influence.
