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Network Analysis and Visualization with JSON and NetworkX

Discover the power of network information manipulation, sharing, and visualization with Dr. Greg Bernstein's Grotto Networking. Gain insights into network topology using JSON format through tools like NetworkX and the web-based editor available at Grotto Networking. Learn about various data formats including XML and GraphML, while also exploring extensive documentation on reading and writing in NetworkX. Follow our guides to seamlessly convert Python data structures to JSON and enhance your network analysis with enriched attributes for nodes and links.

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Network Analysis and Visualization with JSON and NetworkX

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  1. Network Information: Manipulation, Sharing,and Visualization Dr. Greg Bernstein Grotto Networking www.grotto-networking.com

  2. Sharing Network Topology • JSON Format: NetworkX intrinsic • Example (free) drawing tool: Web based editor http://www.grotto-networking.com/GraphEdit.html • JSON is the modern data exchange standard for the web. See: http://www.json.org/ • XML Based Format: GraphML • Example (free) drawing tool: http://www.yworks.com/en/products_yed_about.html • Can read with NetworkX but not custom link properties. • Many other formats • GML, LEDA, Pajek, etc… See NetworkX documentation and web

  3. NetworkX JSON Graph format Minimal Example: • Just a list of nodes and links Can add attributes to nodes and links • Can add any kind of attributes we need Node with extra attributes: Link with extra attributes:

  4. Reading & Writing NetworkX JSON • Crucial import • Documentation • http://networkx.github.io/documentation/latest/reference/readwrite.json_graph.html • Reading from a file example:

  5. Demand’s in Python & JSON • Demands in Python • Use Python tuple as index to Python dictionary • Demands in JSON • Use a JSON list of JSON objects

  6. Demand conversion: PythonJSON • Key import • Documentation • https://docs.python.org/2/library/json.html?highlight=json#json • Need a bit of extra work • See file: jsonconverter.py for two simple helper functions: demands_to_j(obj) and j_to_demands(d_list) • Example usage:

  7. Candidate Paths in Python & JSON • Python • A dictionary indexed by demand pair whose value is a list of paths • JSON • A list of JSON objects, each object containing a list of paths, with each path a list of nodes.

  8. Candidate Paths: PythonJSON • Key import • Need a bit of extra work • See file: jsonconverter.py for two simple helper functions: paths_to_j(path_dict)and j_to_path(path_list) • Example usage: • from jsonconverter import paths_to_j, j_to_paths

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