On the MAGE algorithm page I've found the Graph Neural Network algorithm, but it says that it has not been implemented yet.
Is there a way to get link prediction in Memgraph?
Link prediction can be achieved using node2vec from MAGE.
For this to work, you will need:
The MAGE graph library
Memgraph Lab - the graph explorer for querying Memgraph and visualizing graphs
gqlalchemy - a Python driver and object graph mapper (OGM)
The complete procedure is described in the article Link prediction with Node2Vec in Physics Collaboration Network.
Related
I prototyped a tiny search engine with PageRank that worked on my computer. I am interested in building a Knowledge Graph on top of it, and it should return only queried webpages that are within the right context, similarly to how Google found relevant answers to search questions. I saw a lot of publicity around Knowledge Graphs, but not a lot of literature and almost no pseudocode like guideline of building one. Does anyone know good references on how such Knowledge Graphs work internally, so that there will be no need to create models about a KG?
Knowledge graph is a buzzword. It is a sum of models and technologies put together to achieve a result.
The first stop on your journey starts with Natural language processing, Ontologies and Text mining. It is a wide field of artificial intelligence, go here for a research survey on the field.
Before building your own models, I suggest you try different standard algorithms using dedicated toolboxes such as gensim. You will learn about tf-idf, LDA, document feature vectors, etc.
I am assuming you want to work with text data, if you want to do image search using other images it is different. Same for the audio part.
Building models is only the first step, the most difficult part of Google's knowledge graph is to actually scale to billions of requests each day ...
A good processing pipeline can be built "easily" on top of Apache Spark, "the current-gen Hadoop". It provides a resilient distributed datastore which is mandatory if you want to scale.
If you want to keep your data as a graph, as in graph theory (like pagerank), for live querying, I suggest you use Bulbs which is a framework which is "Like an ORM for graphs, but instead of SQL, you use the graph-traversal language Gremlin to query the database". You can switch the backend from Neo4j to OpenRDF (useful if you do ontologies) for instance.
For graph analytics you can use Spark, GraphX module or GraphLab.
Hope it helps.
I know I'm really late but first to clarify some terminology: Knowledge Graph and Ontology are similar (I'm talking in the Semantic Web paradigm). In the semantic web stack the foundation is RDF which is a language for defining graphs as triples (Subject, Predicate, Object). RDFS is a layer on top of RDF. It defines a meta-model, e.g., predicates such as rdf:type and nodes such as rdfs:Class. Although RDFS provides a meta-model there is no logical foundation for it so there are no reasoners that can validate the model or do further reasoning on it. The layer on top of RDFS is OWL (Web Ontology Language). That has a formal semantics defined by Description Logic which is a decidable subset of First Order Logic. It has more predefined nodes and links such as owl:Class, owl:ObjectProperty, etc. So when people use the term ontology they typically mean an OWL model. When they use the term Knowledge Graph it may refer to an ontology defined in OWL (because OWL is still ultimately an RDF graph) or it may mean just a graph in RDF/RDFS.
I said that because IMO the best way to build a knowledge graph is to define an ontology and then use various semantic web tools to load data (e.g., from spreadsheets) into the ontology. The best tool to start with IMO is the Protege ontology editor from Stanford. It's free and for a free open source tool very reliable and intuitive. And there is a good tutorial for how to use Protege and learn OWL as well as other Semantic Web tools such as SPARQL and SHACL. That tutorial can be found here: New Protege Pizza Tutorial (disclosure: that links to my site, I wrote the tutorial). If you want to get into the lower levels of the graph you probably want to check out a triplestore. It is a graph database designed for OWL and RDF models. The free version of Franz Inc's AllegroGraph triplestore is easy to use and supports 5M triples. Another good triplestore that is free and open source is part of the Apache Jena framework.
I am working on this project where I need to run some graph algorithms on a graph representing English Wikipedia articles. This would need to be in real time
I have tried
WikiMapUW which has only a PHP interface
WikipediaLinkGraph which is not as fast as the previous one but has a Python interface
Was wondering if there are some obvious fast Java / Python APIs available that I have missed and are better ?
I am currently working on a project based on graph and I am searching for an algorithm for slicing an dynamic graph. I have already done some research but most algorithms that I have found works only for a static graph. In my environment, the graph is dynamic, it means that users add/delete elements, create/delete dependences at runtime.
(In reality I am working with UML models but UML models can be also represented by typed graphs, wich are composed of typed Vertices and edges)
I also search for the terms graph fragmentation but I did not find anything. And I would like to know if exist such algorithm for slicing a dynamic graph?
[UPDATE]
Sorry for not being clear and I am updating my question.Let me first expose the context.
In MDE (Model Driven Engineering), large-scale industrial systems involve nowadays hundreds of developpers working on hundreds of models representing pars of the whole system specification. In a such context, the approach commonly adopted is to use a central repository. The solution I provide for my project (I am currently working on a research lab), is a solution which is peer-to-peer oriented, that means that every developper has his own replication of the system specification.
My main problem is how to replicate this data, the models.
For instance, imagine Alice and Bob working on this UML diagram and Alice has the whole diagram in his repository. Bob wants to have the elements {FeedOrEntry, Entry}, how can I slice this diagram UML?
I search for the terms of "model Slicing".I have found one paper which gives an approach for slicing UML Class Diagrams but the problem with this algorithm is it only works for a static graph. In our context, developpers add/update/remove elements constantly and the shared elements should be consistent with the other replicas.
Since UML Models can also be seen as a graph, I also search for the terms for "graph slicing" or "graph fragment" but I have found nothing useful.
And I would like to know if exist such algorithm for slicing a dynamic graph
If you make slicing atomic, I see no problem with using algorithm shown in paper you linked.
However, for your consistency constraints, I believe that your p2p approach is incompatible. Alternative is merge operation, but I have no idea how would that operation work. It probably, at least partially, would have to be done manually.
Sounds like maybe you need a NoSQL graph database such as Neo4J or FlockDB. They can store billions of vertexes and edges.
What about to normalize the graph to an adjacent tree model? Then you can use a DFS or BFS to slice the graph?
I have to write a report on the following question:
Evaluate different layout algorithms / interaction with graphs
Where can I find useful resource to understand graph layout algorithms?
I have no experience in graph programming or visualisation, so resources starting from basics would be very helpful.
Thank You
You might want to check the extensive documentation of the igraph software, which has some description of its internal layout generators. There are also nice illustrations on aiSee website.
For more academic reference, I would suggest browsing the following tutorials: Graph Drawing Tutorial (106 pages) or Graph and Network Visualization (69 pages).
Another useful resource: Handbook of Graph Drawing and Visualization (26 chapters, available as PDF).
The GraphViz project contain lots of different approaches and links to several references.
I want to write something that will draw finite state automata. Does anyone know any algorithms that are related to this?
EDIT: I should mention that I know about graphviz. I want to build my own draw program/function, so what I'm looking for is some more theoretical stuff/pseudo-code for algorithms.
Graph drawing is a fairly complex subject due to the fact that different graphs need to be drawn in different ways - there is no one algorithm fits all approach.
May I suggest the following resource:
http://cs.brown.edu/people/rtamassi/papers/gd-tutorial/gd-constraints.pdf
It should be a good starting point, page 15 provides a number of links and books to follow up.
To get started with graph drawing algorithms, see this famous paper:
"A technique for drawing directed graphs" (1993), by Emden R. Gansner, Eleftherios Koutsofios, Stephen C. North, Kiem-phong Vo, IEEE Transactions on Software Engineering.
It describes the algorithm used by dot, a graphviz drawing program. On the linked page you will find many more references. You will also find some more papers when you google for "drawing directed graphs".
Also, you might find OpenFst convenient, a general toolkit for finite-state machines. It has a binary called fstdraw, which will output a finite-state machine in a format that can be read by dot.
Check out Graphviz. It's an open source graph visualization software.
EDIT: Check out the documentation section which links to some of the layout algorithms used.
Maybe, I'm a little late in answering this question. Anyway this is a very comprehensive reference to the different types of graphs and the algorithms to visualize them.
http://www.cs.brown.edu/~rt/gdhandbook/