SIOC Post profile for "AI3:::Adaptive Information"
type: Document
description: A SIOC profile describes the structure and contents of a weblog in a machine readable form. For more information please refer to http://sioc-project.org/.
primary topic: CWPK #62: Network and Graph Analysis
generator agent: http://rdfs.org/sioc/wp-sioc.php

CWPK #62: Network and Graph Analysis
type: Post
has container: weblog
has creator: Mike Bergman
maker: Mike Bergman
created: 2020-11-05T17:07:15Z
content: Knowledge Graphs Deserve Attention in Their Own Right We first introduced NetworkX in installment CWPK #56 of our Cooking with Python and KBpedia series. The purpose of NetworkX in that installment was to stage data for graph visualizations. In today's installment, we look at the other side of the NetworkX coin; that is, as a graph analytics capability. We will also discuss NetworkX in relation to staging data for machine learning. The idea of graphs or networks is at the center of the concept of knowledge graphs. Graphs are unique information artifacts that can be analyzed in their own right as well as being foundations for many unique analytical techniques, including for machine learning and its deep learning subset. Still, graphs as conceptual and mathematical structures are of relatively recent vintage. For example, the field known as graph theory is less than 300 years old. I outlined much of the intellectual history of graphs and their role in analysis in a 2012 article, The Age of the Graph. Graph or network analysis has three principal aspects. The first aspect is to analyze the nature of the graph itself, with its connections, topologies and paths. The second is to use the structural aspects of the graph representation in order to conduct unique analyses. Some of these analyses relate to community or influence or relatedness. The third aspect is to use various or all aspects of the graph representation of the domain to provide, through dimensionality reduction, tractable and feature-rich methods for analyzing or conducting data science work useful to the domain. We'll briefly cover the first two aspects in this installment. The remaining installments in this Part VI relate more to the third aspect of graph and deep graph representation. Initial Setup We will pick up with our NetworkX work from CWPK #56 to start this installment. (See the concluding sections below if you have not already generated the graph_specs.csv file.) Since I have been away from the co…
encoded: Knowledge Graphs Deserve Attention in Their Own Right We first introduced NetworkX in installment CWPK #56 of our Cooking with Python and KBpedia series. The purpose of NetworkX in that installment was to stage data for graph visualizations. In today’s installment, we look at the other side of the NetworkX coin; that is, as a graph analytics capability. We will also discuss NetworkX in relation to staging data for machine learning. The idea of graphs or networks is at the center of the concept of knowledge graphs. Graphs are unique information artifacts that can be analyzed in their own right as well as being foundations for many unique analytical techniques, including for machine learning and its deep learning subset. Still, graphs as conceptual and mathematical structures are of relatively recent vintage. For example, the field known as graph theory is less than 300 years old. I outlined much of the intellectual history of graphs and their role in analysis in a 2012 article, The Age of the Graph. Graph or network analysis has three principal aspects. The first aspect is to analyze the nature of the graph itself, with its connections, topologies and paths. The second is to use the structural aspects of the graph representation in order to conduct unique analyses. Some of these analyses relate to community or influence or relatedness. The third aspect is to use various or all aspects of the graph representation of the domain to provide, through dimensionality reduction, tractable and feature-rich methods for analyzing or conducting data science work useful to the domain. We’ll briefly cover the first two aspects in this installment. The remaining installments in this Part VI relate more to the third aspect of graph and deep graph representation. Initial Setup We will pick up with our NetworkX work from CWPK #56 to start this installment. (See the concluding sections below if you have not already generated the graph_specs.csv file.) Since I have been away from the co…
topic: CWPK
topic: KBpedia
topic: Semantic Web Tools
links to: NetworkX
links to: CWPK #56
links to: Cooking with Python and KBpedia
links to: graphs
links to: networks
links to: knowledge graphs
links to: machine learning
links to: deep learning
links to: graph theory
links to: The Age of the Graph
links to: cb1-1
links to: cb2-1
links to: cb2-2
links to: cb2-3
links to: cb3-1
links to: cb3-2
links to: cb3-3
links to: cb3-4
links to: cb3-5
links to: cb3-6
links to: cb3-7
links to: cb3-8
links to: cb3-9
links to: cb3-10
links to: cb3-11
links to: cb3-12
links to: cb3-13
links to: cb3-14
links to: cb3-15
links to: cb3-16
links to: cb3-17
links to: cb3-18
links to: cb3-19
links to: cb3-20
links to: cb3-21
links to: cb3-22
links to: cb3-23
links to: cb3-24
links to: cb3-25
links to: cb3-26
links to: cb3-27
links to: cb3-28
links to: cb3-29
links to: cb3-30
links to: directed graph
links to: cb4-1
links to: cb5-1
links to: small-world network
links to: cb6-1
links to: cb6-2
links to: cb6-3
links to: cb6-4
links to: cb7-1
links to: cb8-1
links to: cb9-1
links to: cb10-1
links to: cb11-1
links to: cb11-2
links to: cb11-3
links to: cb12-1
links to: cb13-1
links to: cb14-1
links to: cb14-2
links to: cb15-1
links to: cb15-2
links to: cb15-3
links to: cb15-4
links to: cb15-5
links to: cb15-6

Mike Bergman
type: User

Mike Bergman
type: Person

Cooking with Python and KBpedia
label: CWPK listing

DeepGraphs

documentation

convert to and from other data formats

NumPy

SciPy

pandas

PyTorch

read and write graphs

pickle

JSON

YAML

GraphML

GEFX

Deep Graph Library

built-in converters

DeepSNAP

PyTorch Geometric

CWPK #33

community structure

Louvain Community Detection

Exploring and Analyzing Network Data with Python

Analysis of Large-Scale Networks: NetworkX

Graph Algorithms: Graph Analysis and Graph Learning

Network Analysis with Python and NetworkX Cheat Sheet

Functions to convert NetworkX graphs to and from other formats

Knowledge Graph Representation with Jointly Structural and Textual Encoding

KBpedia

code listing covering the series

direct download

notify me