DiLeNA

Graph Downloader:

Usage Bitcoin

The other DLTs are downloaded with Python3 code

Examples:

to download DLT transactions in a certain time interval:

./main.sh -dlt xrp -start "2020-04-01-00:00:00" -end "2020-04-01-00:01:00" -res 'res/xrp.net' -cores 4
./main.sh -dlt eth -api 'your Etherscan.io key' -start "2020-04-01-00:00:00" -end "2020-04-01-00:01:00" -res 'res/ethereum.net' -cores 4
./main.sh -dlt doge -start "2020-04-01-00:00:00" -end "2020-04-01-00:01:00" -res 'res/doge.net' -cores 4
./main.sh -dlt btc -start "2020-04-01-00:00:00" -end "2020-04-01-00:01:00" -res 'res/btc.net'
./main.sh -dlt ltc -start "2020-04-01-00:00:00" -end "2020-04-01-00:01:00" -res 'res/ltc.net'
./main.sh -dlt dash -start "2020-04-01-00:00:00" -end "2020-04-01-00:01:00" -res 'res/dash.net'
./main.sh -dlt zec -start "2020-04-01-00:00:00" -end "2020-04-01-00:01:00" -res 'res/zec.net'

For Bitcoin only, a previous version of the sofftware is still available, which might turn out to be faster. To download in this alternative way

./main.sh -dlt btc2 -start "2020-04-01" -end "2020-04-01" 

For this version of the software the following tools are required:

• Node.js
• Pm2

Build the src files (for Bitcoin):

$ npm run flow:build

The server is meant to be ran using pm2.

$ pm2 start ecosystem.config.js

will start the server instances configured inside ecosystem.config.js.

By default the web server will serve on port 8888.

For Ripple only, python-ripple-lib library is required, and it can be installed with the following command:

pip3 install python-ripple-lib

---

For dowloading Etherum blocks an Etherscan.io key is needed, otherwise restrictions occur
For Ripple, Ethereum and Dogecoin, Zcash, Dash, Litecoin it is necessary to install python3
Ripple requires library ripple_api

Graph Analyzer:

This project compute metrics for pajek graph generated by the graph downloader

Metrics calculated:

• Number of Nodes
• Number of Edges
• Tot Degree Distribution
• In Degree Distribution
• Out Degree Distribution
• Clustering coefficient
• Average path length (only main component)

Compare data with random graph for small world property.

Getting Started

Networkx is the only Python3 library that must be installed to use the software

python3 main.py -graph=string -result=string -process=int -weight=bool

• -graph = path of graph reachable from src.
• -result = name of file with results.
• -processNum = (OPTIONAL) number of process for better performance, default is 1 (sequential execution) recommended core number.

For example:

python3 main.py -graph="graph.net" -result='res'

Output

Some notes about format:

• All degrees are key-value structure, the number of pairs can be different.

Example:

{
	"loaded_graph": {
		"global": {
			"nodes_number": int, 
			"edges_number": int, 
			"clustering_coefficient": float, 
			"degree_distribution_tot": {
				"degree1": float
			}, 
			"degree_distribution_in": {
				"degree1": float, 
				"degree2": float
			}, 
			"degree_distribution_out": {
				"degree1": float
			}
		}, 
		"main_component": {
			"nodes_number": int, 
			"edges_number": int, 
			"clustering_coefficient": float, 
			"average_path_length": float, 
			"average_weighted_path_length": float, 
			"degree_distribution_tot": {
				"degree1": float,
				"degree2": float
			}, 
			"degree_distribution_in": {
				"degree1": float, 
				"degree2": float
			}, 
			"degree_distribution_out": {
				"degree1": float
			}
		}
	}, 
	"random_graph": {
		"global": {
			"nodes_number": int, 
			"edges_number": int, 
			"clustering_coefficient": float, 
			"degree_distribution_tot": {
				"degree1": float,
				"degree2": float
			}, 
			"degree_distribution_in": {
				"degree1": float, 
				"degree2": float
			}, 
			"degree_distribution_out": {
				"degree1": float,
				"degree2": float,
				"degree3": float
			}
		}, 
		"main_component": {
			"nodes_number": int, 
			"edges_number": int, 
			"clustering_coefficient": float, 
			"average_path_length": float, 
			"degree_distribution_tot": {
				"degree1": float
			}, 
			"degree_distribution_in": {
				"degree1": float, 
				"degree2": float,
				"degree3": float, 
				"degree4": float
			}, 
			"degree_distribution_out": {
				"degree1": float,
				"degree2": float,
				"degree3": float
			}
		}
	}, 
	"small_world": {
		"L": "NaN"/float, 
		"C": "NaN"/float
	}
}