Network Analysis Resources & Updates

📄Stanford Network Analysis Platform (SNAP)

💥Purpose:
SNAP is a general-purpose network analysis and graph mining library.
🔹Language: It is written in C++.
🔹Scalability: SNAP easily scales to handle massive networks with hundreds of millions of nodes and billions of edges.

💥Functionality:
Efficiently manipulates large graphs.
Calculates structural properties.
Generates regular and random graphs.
Supports attributes on nodes and edges.
🔹Python Interface: Snap.py provides a Python interface for SNAP, combining the performance benefits of SNAP with the flexibility of Python.

💥Stanford Large Network Dataset Collection:
This collection includes over 50 large network datasets:
🔹Social networks: Represent online social interactions between people.
🔹Networks with ground-truth communities: These are community structures in social and information networks.
🔹Communication networks: Email communication networks, where edges represent communication between individuals.

💥Tutorials and Recent Events:
SNAP hosts tutorials on topics such as deep learning for network biology, representation learning on networks, and more.
They have organized workshops and tutorials at conferences like ISMB, The Web Conference, and WWW.

🌐 Study

📲Channel: @ComplexNetworkAnalysis

#paper #Graph #code #Python #Tutorials #Dataset

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www.tgoop.com/complexnetworkanalysis/1070

946 viewsedited  Mar 13, 2024 at 02:08

📄Stanford Network Analysis Platform (SNAP)

💥Purpose:
SNAP is a general-purpose network analysis and graph mining library.
🔹Language: It is written in C++.
🔹Scalability: SNAP easily scales to handle massive networks with hundreds of millions of nodes and billions of edges.

💥Functionality:
Efficiently manipulates large graphs.
Calculates structural properties.
Generates regular and random graphs.
Supports attributes on nodes and edges.
🔹Python Interface: Snap.py provides a Python interface for SNAP, combining the performance benefits of SNAP with the flexibility of Python.

💥Stanford Large Network Dataset Collection:
This collection includes over 50 large network datasets:
🔹Social networks: Represent online social interactions between people.
🔹Networks with ground-truth communities: These are community structures in social and information networks.
🔹Communication networks: Email communication networks, where edges represent communication between individuals.

💥Tutorials and Recent Events:
SNAP hosts tutorials on topics such as deep learning for network biology, representation learning on networks, and more.
They have organized workshops and tutorials at conferences like ISMB, The Web Conference, and WWW.

🌐 Study

📲Channel: @ComplexNetworkAnalysis

#paper #Graph #code #Python #Tutorials #Dataset

BY Network Analysis Resources & Updates