Answers.Body: <p><a href="https://en.wikipedia.org/wiki/Network_theory" rel="nofollow noreferrer">Network analysis</a> does not necessarily use deep learning techniques, while geometric deep learning (GDL) on graphs uses only deep learning techniques (that is, you train a neural network using gradient descent or other optimization methods). You can do some network analysis using GDL.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-06-27T18:44:48.590Z
Answers.LastActivityDate: 2019-06-27T18:44:48.590Z
Answers.UserDisplayName: nbro
Answers.Body: <p>Both study properties of a network. The literature under respective titles seems to focus on certain topics.</p>
  
  <p>Network analysis seems to focus on understanding the structure of a network. Centrality , modularity, assortativity etc are metrics used to study properties of networks. Key areas of research are for egs community detection, centrality measures, clustering algorithms, link prediction</p>
  
  <p>GDL is oriented more towards using dataset structured as a graph as an input to machine learning problems like classification, regression.
  Key areas of research include graph representation, neural architecture.</p>
  
  <p>Some problems like link prediction for example are present in both domains. 
  Some problems like network construction for egs isn't covered deeply in both areas. </p>
  
  <p>Some disciplines like algebraic graph theory appear in both disciplines. 
  Fiedler vector for egs  is studied in network analysis for community 
  detection . Spectral analysis, matrix factorisation are ideas being explored in representation learning. </p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-09-10T16:51:12.093Z
Answers.LastActivityDate: 2019-09-10T16:51:12.093Z
Answers.UserDisplayName: Kiko
Body: <p>Both of them deal with data of graph structure like a network community. Is there a big difference there?</p>
  
Tags: <convolutional-neural-networks><comparison><graphs><geometric-deep-learning>
Title: What are the differences between network analysis and geometric deep learning on graphs?
UserDisplayName: unsmoother
ThreadText: # What are the differences between network analysis and geometric deep learning on graphs?
  
  (Asked by: username_0 on 2019-06-27)
  
  Both of them deal with data of graph structure like a network community. Is there a big difference there?
  
  **Tags:** convolutional-neural-networks, comparison, graphs, geometric-deep-learning
  
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  ## 2 answers
  
  ### Answer by username_1 on 2019-06-27. Score: 1 | Accepted answer
  
  [Network analysis](https://en.wikipedia.org/wiki/Network_theory) does not necessarily use deep learning techniques, while geometric deep learning (GDL) on graphs uses only deep learning techniques (that is, you train a neural network using gradient descent or other optimization methods). You can do some network analysis using GDL.
  
  
  ---
  
  ### Answer by username_2 on 2019-09-10. Score: 1
  
  Both study properties of a network. The literature under respective titles seems to focus on certain topics.
  
  Network analysis seems to focus on understanding the structure of a network. Centrality , modularity, assortativity etc are metrics used to study properties of networks. Key areas of research are for egs community detection, centrality measures, clustering algorithms, link prediction
  
  GDL is oriented more towards using dataset structured as a graph as an input to machine learning problems like classification, regression.
  Key areas of research include graph representation, neural architecture.
  
  Some problems like link prediction for example are present in both domains.
  Some problems like network construction for egs isn't covered deeply in both areas.
  
  Some disciplines like algebraic graph theory appear in both disciplines.
  Fiedler vector for egs is studied in network analysis for community
  detection . Spectral analysis, matrix factorisation are ideas being explored in representation learning.
  
  
  ---
  
  