Graphs, Vertices & Edges¶
Introduction¶
A Graph consists of vertices
and edges
. Edges are stored as documents in edge collections
.
A vertex can be a document of a document collection
or of an edge collection
(so edges
can be used as vertices
). Which collections are used within a named graph is defined via edge definitions
.
Graphs allow you to structure your models in line with your domain and group them logically in collections and giving you the power to query them in the same graph queries.
Are graphs and graph databases useful in data modeling, and if so, for what and under which circumstances?
Info
Mathematically, a graph (directed, unlabelled, without multiple edges) is nothing but a relation. It consists of a set V
of vertices and a subset E
(the edges) of the Cartesian product V x V
. There is an edge from v to w
, if and only if the pair (v,w)
is contained in E
.
Similarly, a bipartite graph is just a subset of a Cartesian product A x B
for two disjoint sets A
and B
. It is only when we go to labelled graphs (in which every edge carries a label) or multiple edges that we get a richer structure. Note that an undirected graph can just be seen as a symmetric directed one.
Coming from Relational World¶
In a relational database, we would probably store the vertices of a graph in one table and the edges in a second one. Each edge would have a foreign key for its starting vertex and one for its ending vertex.
In the case of a bipartite graph, we can simply use two tables A
and B
for the two vertex sets, and the edge table simply contains one foreign
key for A and one for B. Note that this data model is also known as “link table” or “junction table”, which is the standard solution for an m:n
relation.
The fundamental query operation on a graph is to find all neighbours of a vertex. This operation can be performed in the above setup, but it involves a join between the vertex table with itself, using the link table (the edges). Thus, finding the neighbours of a vertex will involve at least some index lookup and complexity O(k)
where k is the number of neighbouring vertices.
GDN is a document store that offer efficient joins in the query language. So one can actually use a vertex
collection and an edge
collection and achieve above complexity guarantees. Additionally store arbitrary labelling information for both vertices
and edges
along with their corresponding JSON documents.
Vertex
collections resemble the data tables with the objects to connect. While simple graph queries with fixed number of hops via the relation table may be doable in SQL with several nested joins, GDN can handle an arbitrary number of these hops over edge collections  this is called traversal
.
To get the O(k)
neighbour lookup GDN uses a special edge index that is a hash table tolerating repeated keys and keeping elements with equal keys together in a linked list. The joins are simply necessary to combine the edge
documents with their corresponding vertices
.
Also edges
in one edge
collection may point to several vertex
collections. Its common to have attributes attached to edges, i.e. a label naming this interconnection. Edges have a direction, with their relations _from
and _to
pointing from one document to another document stored in vertex
collections.
In queries you can define in which directions the edge
relations may be followed i.e.,
 OUTBOUND:
_from
→_to
 INBOUND:
_from
←_to
 ANY:
_from
↔_to
Edges, Identifiers, Handles¶
A graph data model always consists of at least two collections: the relations
between the nodes in the graphs are stored in an edges collection
, the nodes
in the graph are stored in documents in regular collections
.
Edges in are special documents. In addition to the system attributes _key
, _id
and _rev
, they have the attributes _from
and _to
, which contain document handles, namely the startpoint and the endpoint of the edge.
Example:
 the “edge” collection stores the information that a company’s reception is subunit to the services unit and the services unit is subunit to the CEO. You would express this relationship with the
_from
and_to
attributes 
the
normal
collection stores all the properties about the reception, e.g. that 20 people are working there and the room number etc 
_from
is the document handle of the linked vertex (incoming relation) _to
is the document handle of the linked vertex (outgoing relation)
Edge collections are special collections that store edge documents. Edge documents are connection documents that reference other documents. The type of a collection must be specified when a collection is created and cannot be changed afterwards.
To change edge endpoints you would need to remove old document/edge and insert new one. Other fields can be updated as in default collection.
Edges are normal documents that always contain a
_from
and a_to
attribute.
Querying graphs¶
Storing (and retrieving) a graph is one thing, but the actual problems only begin when we want to query information about a graph.
Finding the neighbours of a vertex
is one crucial question one might have about a graph (or relation, which is the same thing). However, when we deal with graphs (or relations) in practice, we usually have a lot more questions, here we just mention a few that come to mind:
 Find all neighbours of a
vertex
only usingedges
with a givenproperty
orlabel
.  Find all neighbours of a
vertex
with a givenproperty
orlabel
.  Find all paths with a fixed length L in the graph starting at some given
vertex
.  Find the shortest (or lightest when working with weights) path from vertex
V
to vertexW
.  Find the distances between any two vertices in the graph.
 Perform a depth first search for some vertex starting at a given vertex.
 Perform a breadth first search for some vertex starting at a given vertex.
 Find a minimal spanning tree for the graph.
 Perform any mapreduce like computation as is possible in the Pregel framework by Google, for example “Pagerank” or “Find connected components”.
 Solve the traveling salesman problem in the graph.
GDN provides several Graph Functions for working with edges and vertices, to analyze them and their relations
Manipulating graph collections with regular document functions¶
The underlying collections of the graphs are still accessible using the standard methods for collections. However GDN graph module adds an additional layer on top of these collections giving you the following guarantees:
 All modifications are executed transactional
 If you delete a
vertex
, alledges
referring to this vertex will be deleted too.  If you insert an
edge
, it is checked if the edge matches the edge definitions.  Your edge collections will only contain valid edges and you will never have loose ends.
Warning
These guarantees are lost if you access the collections in any other way than the graph module, so if you delete documents from your vertex collections directly, the edges pointing to them will be remain in place.
Existing inconsistencies in your data will not be corrected when you create a graph. Therefore, make sure you start with sound data as otherwise there could be dangling edges after all. The GDN graph module guarantees to not introduce new inconsistencies only.
FILTERs on edge document attributes OR Multiple edge collections?¶
If you want to only traverse
edges of a specific type, there are two ways to achieve this.

The first would be an attribute in the edge document i.e.
type
, where you specify a differentiator for the edge like "friends", "family", "married" or "workmates", so you can laterFILTER e.type = "friends"
if you only want to follow the friend edges. 
Another way, which may be more efficient in some cases, is to use different
edge
collections for different types of edges, so you havefriend_edges
,family_edges
,married_edges
andworkmate_edges
as collection names. You can then configure several graphs including a subset of the available edge and vertex collections. To only followfriend
edges, you would specifyfriend_edges
as sole edge collection.
Both approaches have advantages and disadvantages. FILTER
operations on edge attributes will do comparisons on each traversed edge
, which may become CPUintense. When not finding the edges in the first place because of the collection containing them is not traversed at all, there will never be a reason to actually check for their type attribute with FILTER.
The multiple edge collections approach is limited by the number of collections that can be used simultaneously in one query. Every collection used in a query requires some resources inside GDN and the number is therefore limited (max: 10 collections) to cap the resource requirements. You may also have constraints on other edge attributes, such as a hash index
with a unique constraint, which requires the documents to be in a single collection for the uniqueness guarantee, and it may thus not be possible to store the different types of edges in multiple edge collections.
So, if your edges have about a dozen different types, it’s okay to choose the collection
approach, otherwise the FILTER
approach is preferred. You can still use FILTER
operations on edges of course. You can get rid of a FILTER
on the type with the former approach, everything else can stay the same.
What data should be in Edge and what should be in a Vertex?¶
The main objects in your data model, such as users, groups or articles, are usually considered to be vertices.
For each type of object, a document collection (also called vertex collection) should store the individual entities. Entities can be connected by edges to express and classify relations between vertices. It often makes sense to have an edge collection per relation type.
GDN does not require you to store your data in graph structures with edges and vertices, you can also decide to embed attributes such as which groups a user is part of, or _id
s of documents in another document instead of connecting the documents with edges. It can be a meaningful performance optimization for 1:n relationships, if your data is not focused on relations and you don't need graph traversal with varying depth. It usually means to introduce some redundancy and possibly inconsistencies if you embed data, but it can be an acceptable tradeoff.
Vertices¶
Let's say we have two vertex collections, Users
and Groups
. Documents in the Groups
collection contain the attributes of the Group, i.e. when it was founded, its subject, an icon URL and so on. Users
documents contain the data specific to a user  like all names, birthdays, Avatar URLs, hobbies...
Edges¶
We can use an edge collection to store relations between users and groups. Since multiple users may be in an arbitrary number of groups, this is an m:n relation. The edge collection can be called UsersInGroups
with i.e. one edge with _from
pointing to Users/John
and _to
pointing to Groups/BowlingGroupHappyPin
. This makes the user John a member of the group Bowling Group Happy Pin. Attributes of this relation may contain qualifiers to this relation, like the permissions of John in this group, the date when he joined the group etc.
So roughly put, if you use documents and their attributes in a sentence, nouns would typically be vertices, verbs become the edges.
You can see this in the knows graph below:
> Alice knows Bob, who in term knows Charlie.
Advantages of this approach¶
Graphs give you the advantage of not just being able to have a fixed number of m:n relations in a row, but an arbitrary number. Edges can be traversed in both directions, so it's easy to determine all groups a user is in, but also to find out which members a certain group has. Users could also be interconnected to create a social network.
Using the graph data model, dealing with data that has lots of relations stays manageable and can be queried in very flexible ways, whereas it would cause headache to handle it in a relational database system.
Example Graphs¶
GDN comes with a set of easily graspable graphs that are used to demonstrate the APIs. You can use the add samples
tab in the create graph
window in the web interface and use it to create instances of these graphs in your GDN fabric. Once you've created them, you can them in GUI.
The Knows_Graph¶
A set of persons knowing each other:
The knows graph consists of one vertex collection persons
connected via one edge collection knows
. It will contain five persons Alice, Bob, Charlie, Dave and Eve. We will have the following directed relations:
 Alice knows Bob
 Bob knows Charlie
 Bob knows Dave
 Eve knows Alice
 Eve knows Bob
Note
With the default "Search Depth" of 2 of the graph viewer you may not see all edges of this graph.
The Social Graph¶
A set of persons and their relations:
This example has female and male persons as vertices in two vertex collections  female
and male
. The edges are their connections in the relation
edge collection.
The City Graph¶
A set of european cities, and their fictional traveling distances as connections:
The example has the cities as vertices in several vertex collections  germanCity
and frenchCity
. The edges are their interconnections in several edge collections french / german / international Highway
.
The Traversal Graph¶
This graph was designed to demonstrate filters in traversals. It has some labels to filter on it.
The example has all its vertices in the circles collection, and an edges edge collection to connect them.
Circles have unique numeric labels. Edges have two boolean attributes (theFalse always being false, theTruth always being true) and a label sorting B  D to the left side, G  K to the right side. Left and right side split into Paths  at B and G which are each direct neighbours of the rootnode A. Starting from A the graph has a depth of 3 on all its paths.
Note
With the default "Search Depth" of 2 of the graph viewer you may not see all nodes of this graph.
The k Shortest Paths Graph¶
The vertices in this graph are train stations of cities in Europe and North America and the edges represent train connections between them, with the travel time for both directions as edge weight.
See the k Shortest Paths page for query examples.
The World Graph¶
The world country graph structures its nodes like that: world → continent → country → capital. In some cases edge directions aren't forward (therefore it will be displayed disjunct in the graph viewer). It has two ways of creating it. One using the named graph utilities (worldCountry), one without (worldCountryUnManaged).
It is used to demonstrate raw traversal operations.
The Mps Graph¶
This graph was created to demonstrate a use case of the shortest path algorithm. Even though the algorithm can only determine one shortest path, it is possible to return multiple shortest paths with two separate queries. Therefore the graph is named after the **m**ultiple **p**ath **s**earch use case.
The example graph consists of vertices in the mps_verts
collection and edges in the mps_edges
collection. It is a simple traversal graph with start node A and end node C.
Higher volume graph examples¶
All of the above examples are rather small so they are easier to comprehend and can demonstrate the way the functionality works. Example: Pokec social network
Graph Functions¶
A lot of graph functions accept a vertex (or edge) example as parameter as defined in the next sections.
Examples will explain the API on the the city graph:
Get vertex from of an edge¶
Get the source vertex of an edge
graph._fromVertex(edgeId)
Returns the vertex defined with the attribute _from of the edge with edgeId as its _id.
Parameters
 edgeId (required) _id attribute of the edge
Get vertex to of an edge¶
Get the target vertex of an edge
graph._toVertex(edgeId)
Returns the vertex defined with the attribute _to of the edge with edgeId as its _id.
Parameters
 edgeId (required) _id attribute of the edge
Get Neighbors¶
Get all neighbors
of the vertices defined by the example
graph._neighbors(vertexExample, options)
The function accepts an id, an example, a list of examples or even an empty example as parameter for vertexExample.
The complexity of this method is O(n*m^x) with n being the vertices defined by the parameter vertexExamplex, m the average amount of neighbors and x the maximal depths. Hence the default call would have a complexity of O(n*m);
Parameters
 vertexExample (optional)
 options (optional) An object defining further options. Can have the following values:
 direction: The direction of the edges. Possible values are outbound, inbound and any (default).
 edgeExamples: Filter the edges
 neighborExamples: Filter the neighbor vertices
 edgeCollectionRestriction : One or a list of edgecollection names that should be considered to be on the path.
 vertexCollectionRestriction : One or a list of vertexcollection names that should be considered on the intermediate vertex steps.
 minDepth: Defines the minimal number of intermediate steps to neighbors (default is 1).
 maxDepth: Defines the maximal number of intermediate steps to neighbors (default is 1).
Get Common Neighbors¶
Get all common neighbors
of the vertices defined by the examples.
graph._commonNeighbors(vertex1Example, vertex2Examples, optionsVertex1, optionsVertex2)
This function returns the intersection of graph_module._neighbors(vertex1Example, optionsVertex1) and graph_module._neighbors(vertex2Example, optionsVertex2).
For parameter documentation see _neighbors.
The complexity of this method is O(n*m^x) with n being the maximal amount of vertices defined by the parameters vertexExamples, m the average amount of neighbors and x the maximal depths. Hence the default call would have a complexity of O(n*m);
Count Common Neighbors¶
Get the amount of common neighbors of the vertices defined by the examples.
graph._countCommonNeighbors(vertex1Example, vertex2Examples, optionsVertex1, optionsVertex2)
Similar to _commonNeighbors but returns count instead of the elements.
Get Common Properties¶
Get the vertices of the graph that share common properties
.
graph._commonProperties(vertex1Example, vertex2Examples, options)
The function accepts an id, an example, a list of examples or even an empty example as parameter for vertex1Example and vertex2Example.
The complexity of this method is O(n) with n being the maximal amount of vertices defined by the parameters vertexExamples.
Parameters
 vertex1Examples (optional) Filter the set of source vertices
 vertex2Examples (optional) Filter the set of vertices compared to.
 options (optional) An object defining further options. Can have the following values:
 vertex1CollectionRestriction : One or a list of vertexcollection names that should be searched for source vertices.
 vertex2CollectionRestriction : One or a list of vertexcollection names that should be searched for compare vertices.
 ignoreProperties : One or a list of attribute names of a document that should be ignored.
Count Common Properties¶
Get the amount of vertices of the graph that share common properties.
graph._countCommonProperties(vertex1Example, vertex2Examples, options)
Similar to _commonProperties but returns count instead of the objects.
Get Paths¶
The _paths
function returns all paths of a graph.
graph._paths(options)
This function determines all available paths in a graph.
The complexity of this method is O(n*n*m) with n being the amount of vertices in the graph and m the average amount of connected edges;
Parameters
 options (optional) An object containing options, see below:
 direction: The direction of the edges. Possible values are any, inbound and outbound (default).
 followCycles (optional): If set to true the query follows cycles in the graph, default is false.
 minLength (optional): Defines the minimal length a path must have to be returned (default is 0).
 maxLength (optional): Defines the maximal length a path must have to be returned (default is 10).
Get Shortest Path¶
The _shortestPath
function returns all shortest paths of a graph.
graph._shortestPath(startVertexExample, endVertexExample, options)
This function determines all shortest paths in a graph. The function accepts an id, an example, a list of examples or even an empty example as parameter for start and end vertex.
The length of a path is by default the amount of edges from one start vertex to an end vertex. The option weight allows the user to define an edge attribute representing the length.
Parameters
 startVertexExample (optional) An example for the desired start Vertices.
 endVertexExample (optional) An example for the desired end Vertices.
 options (optional) An object containing options, see below:
 direction: The direction of the edges as a string. Possible values are outbound, inbound and any (default).
 edgeCollectionRestriction: One or multiple edge collection names. Only edges from these collections will be considered for the path.
 startVertexCollectionRestriction: One or multiple vertex collection names. Only vertices from these collections will be considered as start vertex of a path.
 endVertexCollectionRestriction: One or multiple vertex collection names. Only vertices from these collections will be considered as end vertex of a path.
 weight: The name of the attribute of the edges containing the length as a string.
 defaultWeight: Only used with the option weight. If an edge does not have the attribute named as defined in option weight this default is used as length. If no default is supplied the default would be positive Infinity so the path could not be calculated.
Get Distance To¶
The _distanceTo
function returns all paths and there distance within a graph.
graph._distanceTo(startVertexExample, endVertexExample, options)
This function is a wrapper of graph._shortestPath. It does not return the actual path but only the distance between two vertices.
Absolute Eccentricity¶
Get the eccentricity of the vertices defined by the examples.
graph._absoluteEccentricity(vertexExample, options)
The function accepts an id, an example, a list of examples or even an empty example as parameter for vertexExample.
Parameters
 vertexExample (optional) Filter the vertices.
 options (optional) An object defining further options. Can have the following values:
 direction: The direction of the edges. Possible values are outbound, inbound and any (default).
 edgeCollectionRestriction : One or a list of edgecollection names that should be considered to be on the path.
 startVertexCollectionRestriction : One or a list of vertexcollection names that should be considered for source vertices.
 endVertexCollectionRestriction : One or a list of vertexcollection names that should be considered for target vertices.
 weight: The name of the attribute of the edges containing the weight.
 defaultWeight: Only used with the option weight. If an edge does not have the attribute named as defined in option weight this default is used as weight. If no default is supplied the default would be positive infinity so the path and hence the eccentricity can not be calculated.
Get Eccentricity¶
Get the normalized eccentricity of the vertices defined by the examples.
graph._eccentricity(vertexExample, options)
Similar to _absoluteEccentricity but returns a normalized result.
Get Absolute Closeness¶
Get the closeness of the vertices defined by the examples.
graph._absoluteCloseness(vertexExample, options)
The function accepts an id, an example, a list of examples or even an empty example as parameter for vertexExample.
Parameters
 vertexExample (optional) Filter the vertices.
 options (optional) An object defining further options. Can have the following values:
 direction: The direction of the edges. Possible values are outbound, inbound and any (default).
 edgeCollectionRestriction : One or a list of edgecollection names that should be considered to be on the path.
 startVertexCollectionRestriction : One or a list of vertexcollection names that should be considered for source vertices.
 endVertexCollectionRestriction : One or a list of vertexcollection names that should be considered for target vertices.
 weight: The name of the attribute of the edges containing the weight.
 defaultWeight: Only used with the option weight. If an edge does not have the attribute named as defined in option weight this default is used as weight. If no default is supplied the default would be positive infinity so the path and hence the closeness can not be calculated.
Get Closeness¶
Get the normalized closeness of graphs vertices.
graph._closeness(options)
Similar to _absoluteCloseness but returns a normalized value.
Get Absolute Betweenness¶
Get the betweenness of all vertices in the graph.
graph._absoluteBetweenness(vertexExample, options)
Parameters
 vertexExample (optional) Filter the vertices, see Definition of examples
 options (optional) An object defining further options. Can have the following values:
 direction: The direction of the edges. Possible values are outbound, inbound and any (default).
 weight: The name of the attribute of the edges containing the weight.
 defaultWeight: Only used with the option weight. If an edge does not have the attribute named as defined in option weight this default is used as weight. If no default is supplied the default would be positive infinity so the path and hence the betweenness can not be calculated.
Get Betweenness¶
Get the normalized betweenness of graphs vertices.
graph_module._betweenness(options)
Similar to _absoluteBetweenness but returns normalized values.
Get Radius¶
Get the radius of a graph.
Parameters
 options (optional) An object defining further options. Can have the following values:
 direction: The direction of the edges. Possible values are outbound, inbound and any (default).
 weight: The name of the attribute of the edges containing the weight.
 defaultWeight: Only used with the option weight. If an edge does not have the attribute named as defined in option weight this default is used as weight. If no default is supplied the default would be positive infinity so the path and hence the radius can not be calculated.
Get Diameter¶
Get the diameter of a graph.
graph._diameter(graphName, options)
Parameters
 options (optional) An object defining further options. Can have the following values:
 direction: The direction of the edges. Possible values are outbound, inbound and any (default).
 weight: The name of the attribute of the edges containing the weight.
 defaultWeight: Only used with the option weight. If an edge does not have the attribute named as defined in option weight this default is used as weight. If no default is supplied the default would be positive infinity so the path and hence the radius can not be calculated.