github.com/santiment
Multichain analytic platform for social, market and onchain data
On-chain, social, development activity, prices and volume data and charts
Custom-built and unique terabytes of processed on-chain, social, github and fundamental data sets
On-chain, social, development activity, prices and volume data
github.com/bloxy-info
The best Ethereum analitycal browser
github.com/bitquery/activecube-graphql
GraphQL tool for Multichain data
github.com/bitquery/explorer
Multichain browser with little analytical tools
blockchain-etl
Public Blockchain Datasets with the ability to query using Google BigQuery
blockchain-etl
ETL tools for blockchain data
Labeling analytic tool for Ethereum
github.com/ClickHouse/ClickHouse
ClickHouse is a fast open-source OLAP database management system.
It is column-oriented and allows to generate analytical reports using SQL queries in real-time.
This tool has become the standard for the analysis of blockchain data, it combines high speed and flexibility.
github.com/graphframes/graphframes
GraphFrames is a package for Apache Spark which provides DataFrame-based Graphs. It provides high-level APIs in Scala,
Java, and Python. It aims to provide both the functionality of GraphX and extended functionality taking advantage
of Spark DataFrames. This extended functionality includes motif finding, DataFrame-based serialization, and highly
expressive graph queries.
github.com/apache/spark/tree/master/graphx
GraphX is a new component in Spark for graphs and graph-parallel computation. At a high level, GraphX extends
the Spark RDD by introducing a new Graph abstraction: a directed multigraph with properties attached to each vertex
and edge.
There is only the Scala API and does not have a Python API.
Apache Giraph is an iterative graph processing system built for high scalability. For example, it is currently used at Facebook to analyze the social graph formed by users and their connections. Giraph originated as the open-source counterpart to Pregel, the graph processing architecture developed at Google and described in a 2010 paper.
github.com/neo4j
The well-known graph database has wide functionality for graph analysis. The free version has significant limitations
and can be recommended for analyzing graphs with a total number of edges and vertices of less than 10M.
I do not recommend using it in a production.
github.com/aws
github.com/awslabs/amazon-neptune-tools
github.com/aws-samples/amazon-neptune-samples
Amazon Neptune is a purpose-built, high-performance graph database engine optimized for storing billions
of relationships and querying the graph with milliseconds latency. Neptune supports the popular graph models property
graph and W3C's Resource Description Framework (RDF), and it also supports their respective query languages, Apache
TinkerPop Gremlin and SPARQL, to allow you to build queries that efficiently navigate highly connected datasets.
github.com/networkx
NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions
of complex networks.
Commonly used to visualize graphs.
github.com/QuantStack/ipycytoscape
Visualize graphs using Cytoscape.js in a Jupyter Notebook.
You can either create graphs using the ipycytoscape API or create them from NetworkX, JSON and Pandas Dataframes.
github.com/gephi/gephi
Gephi is the leading visualization and exploration App for all kinds of graphs and networks.
github.com/plotly
Easy way to build webApp with network visualization
github.com/lferry007/LargeVis
This is the official implementation of the LargeVis model by the original authors, which is used to visualize
large-scale and high-dimensional data (Tang, Liu, Zhang and Mei). It now supports visualizing both high-dimensional
feature vectors and networks. The package also contains a very efficient algorithm for constructing K-nearest neighbor
graph (K-NNG).
github.com/xgfs/verse
Versatile Graph Embeddings from Similarity Measures
github.com/rapidsai/cugraph
cuGraph Graph Analytics Library is a collection of GPU accelerated graph algorithms.
github.com/hudson-and-thames/mlfinlab
MlFinlab is a python package which helps portfolio managers and traders who want to leverage the power of machine
learning by providing reproducible, interpretable, and easy to use tools.
github.com/joblib/joblib
Joblib is a set of tools to provide lightweight pipelining in Python. Joblib is optimized to be fast and robust
on large data in particular and has specific optimizations for numpy arrays.
github.com/dask/dask
Dask is a flexible library for parallel computing in Python. You can use numpy-like or pandas-like syntax.
Google Dataset Search allows to find a data you are interested in.