I am studying graph databases (Neo4J).
I know graph databases come from graph theory, which has basis on Euler paper to solve the 7 bridges problem.. eulerian path/eulerian circuit.
I am trying to find an example, something, to show how graph databases use the Eulerian path to solve some problem.
Ideally I would need a real example from a source, but even any help to understand this better.. thanks!
I believe you have a misimpression. Even though Euler wrote a paper based on the 7 bridges problem (which was probably the first paper on graph theory), that does not mean that the design of neo4j uses his solution to that specific problem in any way.
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Hello I have got an assignment to write a program, which will visualize a Graph, implemented in any way. I have no problem in implementing Graph, but I have no idea how can I visualize it.
The rules are:
- include graphs with vertex and edge labels, directed and non-directed;
- drawing clarity (avoiding large clusters of vertices and edges, avoiding cutting edges and breaking them in many places)
Do you have any tips or suggestions which language or tool should I use to do it, or when can I find help to deal with this type of problem, I'd be glad. I don't know how to make anything graphic, never done that. I am coding mostly in Java, Python and C++, but I am not advanced.
I am working on a similar problem. The approach I'm working with currently has been first creating a node, then animating it, and repeating for all nodes
https://dl1683.github.io/DataStructuresInJavaScript/index.html.
The Github code might be useful to you. I am not a web dev guy so be careful about doing everything I did.
The question itself is pretty simple... This is a vechicle routing problem.
I have a directed graph
I need to get a linear programming model that will somehow tell me the shortest circuit that visits all nodes, and starts and ends at the star. You are allowed to go over an edge more than once. A node is any crossing on the image.
We had like 4 hours of this in class, I have no idea where to even start...
I'm not expection anyone to give me the full model, but I was hoping someone would tell a strategy so I could do this.
Thank you in advance.
I would start from Dijkstra's algorithm for undirected graphs. There are some variants with similar or better performance. Take a look at https://en.wikipedia.org/wiki/Shortest_path_problem#Undirected_graphs, pick a choice, and keep us informed ... :)
The answer here seems to be very easy. (That is, very easy in theory, very hard and a lot of work to code in practice).
This seems like a straight-forward TSP (Travelling Salesperson Problem). Read about some general literature on TSP. You need to set up and solve a TSP where your nodes are the "cities" in a TSP. You also need to include your star as a city in the TSP.
The Dijkstra algorithm will not give you a solution. The Dijkstra algorithm is used for finding fastest/shortest paths between a node and other nodes in a (typically road-) network. However, distance-wise your problem is super-simple: Getting the cheapest travel cost (and path) from one node to another node in your problem is (almost) trivial.
If you are to solve this problem "for real" (not just discuss it), you need to acquire a TSP solver that is able to take your network (both edges and nodes) as input. Your input needs to specify which edges are directed and which are uni-directed. A lot of work in practice: Even if you use a tool, you still need to familiarize yourself with the tool.
I would like to test my recently created algorithm on large (50+ node) graphs. Preferrably, they would specifically be challenging graphs, and known tours would exist (for at least most of them).
Problem sets for this problem do not seem as easy to find as with the TSP. I am aware of the Flinder's challenge set available at http://www.flinders.edu.au/science_engineering/csem/research/programs/flinders-hamiltonian-cycle-project/fhcpcs.cfm
However, they seem to be directed. I can probably alter my algorithm to work for directed, but it will take time and likely induce bugs. I'd prefer to know if it can work for undirected first.
Does anyone know where problem sets are available? Thank you.
quick edit:
Now I am unsure if the flinder's set is directed or not.... It doesn't say. Examples make it seem like maybe it actually is undirected.
Check this video:
https://www.youtube.com/watch?v=G1m7goLCJDY
Also check the in depth sequel to the video.
You can determine yourself how many nodes you want to add to the graph.
It does require you to construct the data yourself, which should be deable.
One note: the problem is about a path, not a cycle, but you can overcome this by connecting the start and end node.
I am new to Neo4j and I am trying to compare two graphs in Neo4j. My first question is how to create two different graphs on Neo4j. I created something like below which is just like two disconnected graphs.
Neo4j graph
Is this the best way we can have two graphs in Neo4j for comparison or there is another way?
My second question is based on my requirement to find the common vertices and common edges in both graphs. How to get this information? I am using Java to connect to Neo4j to create and run Cypher queries.
There are some questions asked on this platform about this but none seems to be quite what I am looking for.
If anyone can even suggest better ways to implement similarity algorithms on large graphs, I would really appreciate it.
Well, in the standard Neo4j browser, you can only have a single Network view. You could use the concept of "virtual nodes", or use styling features that allow you to detect the differences visually. Perhaps this https://youtu.be/7iMraBHtTqE inspires you.
Disclaimer : I am the owner of Graphileon.
I need to find connected components for a huge dataset. (Graph being Undirected)
One obvious choice is MapReduce. But i'm a newbie to MapReduce and am quiet short of time to pick it up and to code it myself.
I was just wondering if there is any existing API for the same since it is a very common problem in Social Network Analysis?
Or atleast if anyone is aware of any reliable(tried and tested) source using which atleast i can get started with the implementation myself?
Thanks
I blogged about it for myself:
http://codingwiththomas.blogspot.de/2011/04/graph-exploration-with-hadoop-mapreduce.html
But MapReduce isn't a good fit for these Graph analysis things. Better use BSP (bulk synchronous parallel) for that, Apache Hama provides a good graph API on top of Hadoop HDFS.
I've written a connected components algorithm with MapReduce here: (Mindist search)
https://github.com/thomasjungblut/tjungblut-graph/tree/master/src/de/jungblut/graph/mapreduce
Also a BSP version for Apache Hama can be found here:
https://github.com/thomasjungblut/tjungblut-graph/blob/master/src/de/jungblut/graph/bsp/MindistSearch.java
The implementation isn't as difficult as in MapReduce and it is at least 10 times faster.
If you're interested, checkout the latest version in TRUNK and visit our mailing list.
http://hama.apache.org/
http://apache.org/hama/mail-lists.html
I don't really know if an API is available which has methods to find strongly connected components. But, I implemented the BFS algorithm to find distance from source node to all other nodes in the graph (the graph was a directed graph as big as 65 million nodes).
The idea was to explore the neighbors (distance of 1) for each node in one iteration and feeding the output of reduce back to map, until the distances converge. The map emits the shortest distances possible from each node, and reduce updated the node with the shortest distance from the list.
I would suggest to check this out. Also, this could help. These two links would give you the basic idea about graph algorithms in map reduce paradigm (if you are already not familiar). Essentially, you need to twist the algorithm to use DFS instead of BFS.
You may want to look at the Pegasus project from Carnegie Mellon University. They provide an efficient - and elegant - implementation using MapReduce. They also provide binaries, samples and a very detailed documentation.
The implementation itself is based on the Generalized Iterative Matrix-Vector multiplication (GIM-V) proposed by U Kang in 2009.
PEGASUS: A Peta-Scale Graph Mining System - Implementation and
Observations U Kang, Charalampos E. Tsourakakis, Christos Faloutsos In
IEEE International Conference on Data Mining (ICDM 2009)
EDIT:
The official implementation is actually limited to 2.1 billions nodes (node id are stored as integers). I'm creating a fork on github (https://github.com/placeiq/pegasus) to share my patch and other enhancements (eg. Snappy compression).
It is a little old question but here is something you want to checkout. We implemented connected component using map-reduce on Spark platform.
https://github.com/kwartile/connected-component