Nodz

A project to play with graphs and test some ideas about networks science. Full code is under MIT License.

Features

So far:

implementing graphs core definitions (central graph, nodes, links, etc)
random graphs with preferential attachment (Barabasi Albert) and GNP (fixed nodes size, links by probability)
basic stats: degree distribution, size, etc
gephi export for data type. Just enough to create data visualizations of graphs, this is not a gexf library with all gexf features
large structures definition: sets, iterators. Implementations so far are local, but everything is ready for other definitions
connected component

Next features (working on it)

walkthroughs

Features to implement one day

graph features: graph diameter, etc
neo4j import and export
observability: observer over nodes to detect changes (node creation, deletion, or links changes. Even, for some nodes, changes of states)
gexf import and export (again, not planning a full support for gexf)

Features that sound like good ideas, but not sure yet

REST endpoint to deal with graphs
DSL for non technical use (create a random graph, print basic stats, etc)
Sort of jupyter interface to use that DSL
Event based changes on graphs, apache kafka sounds like a good option
Distibute calculation (but, a huge amount of work)
graph data visualization using a dedicated server (cross project, using d3.js ?)

Implementation details

examples/ contains nice code, to show some use cases
graphs/ is about main definitions.
internal/ is about main implementations.
Use of iterators: Some graphs are HUGE, so using iterators is more flexible and efficient than slices (lazy loading / pagination)
internal/local/: split definitions and local implementations. Local implementations are "in memory" implementations of general definitions

Types of graph

ValueBasedGraph: nodes holding content (NV type) are linked with values (LV type). Use it when you do not care about the graph structure.
CentralStructureGraph: linked nodes and you need global operations (all nodes iterations, adjacency matrix, etc)
PeersStructureGraph: linked nodes but you discover graph from a node. No global operations

So far:

Type	Implementation	Local	Directed
Value	DirectedValuesGraph	YES	YES
Central	MapGraph	YES	MIXED

Wait, what ? How do I start with your project ?

Open the examples/ code and read code to get the purpose of some important classes
Start with general definition of a graph
Read graphs/ interfaces if you need more details about nodes, neighbors or links. There should be no surprise, it is basic definition
Have a look at graphs_test/ to understand some high level features
Then, you may want to dig into internal_test/local_test

You like graphs or network science ?

This part is about links or mentions about graphs / network science related stuff. It just is "hey, look at that if you want, I found it interesting". I don't make money by advertising, I am not in position of any conflict of interest, it is pure personal opinion.

frameworks / show me some code

Neo4j database, community version on premise is an excellent graph database. Neo4j is, to me, very pushy about its cloud solution (Aura).
Apache GraphX: Not a fan, but it exists and I wanted to mention it. Sounds more like an abandoned POC to me
NetworkX: perfect for its purpose, easy to use, powerful. If language is not a question, I would recommand Python and NetworkX for sure !

Tools

Gephi is a classic and efficient vizualisation tool for graphs in general, excellent for big graphs

Books

Barabasi: Networks Science: Author has a style, many ideas, not a lot of details about some key parts. Brilliant, really good to understand complex networks ideas. But... Not for a first read
Mentzer, Fortunato, Davis: a first course in network science : very good too, covers more topics and is easier, first read material for sure!
Boullier: Propagations (in french). WHAT A BOOK ! Very clever ideas about propagations and related use of data. A source of inspiration to go further than technical implementations

Videos

FASCINATING phenomenon : percolation

Not about graphs, but related somehow

Russel, Norvig: Artificial intelligence: THE book about AI. But get ready, it is a huge book that goes really in depth. Chapter 3 is excellent about exploration, and explains applications of graphs for walkthroughs

zefrenchwan / nodz