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

1. ValueBasedGraph: nodes holding content (NV type) are linked with values (LV type). Use it when you do not care about the graph structure.
2. CentralStructureGraph: linked nodes and you need global operations (all nodes iterations, adjacency matrix, etc)
3. 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 ?

1. Open the examples/ code and read code to get the purpose of some important classes
2. Start with general definition of a graph
3. Read graphs/ interfaces if you need more details about nodes, neighbors or links. There should be no surprise, it is basic definition
4. Have a look at graphs_test/ to understand some high level features
5. 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