Graph Analytics Engine API Documentation

This API call fetches data from the deployment and loads it into memory of the engine for later processing. One can either use a named graph or a list of vertex collections and a list of edge collections. Currently, the API call always loads all vertices and edges from these collections. However, it is possible to select which attribute data is loaded alongside the vertices and the edge topology. These attribute values are stored into a column store, in which each column corresponds to an attribute and has as many rows as there are vertices in the graph. Each loaded graph will get a numerical ID, with which it can be used in computations. This is an asynchronous job which returns the job id immediately. Use the GET graph API with the returned graph ID to get information on errors and the outcome of the loading.

This API fetches data from the ArangoGraph deployment via AQL and load it into memory of the engine for later processing. (NOT IMPLEMENTED YET)

Process a previously loaded graph with the weakly connected components algorithm (WCC) and store the results in-memory. This essentially means that the direction of edges is ignored and then the connected components of the undirected graph are computed. The computation will return a numerical job id, with which the results can later be queried or written back to the database. This is an asynchronous job which returns the job id immediately. Use the GET job API with the job id to get information on progress, errors and the outcome of the computation.

Process a previously loaded graph with the strongly connected components algorithm (SCC) and store the results in-memory. This means that the direction of the edges is taken into account and two vertices A and B will be in the same strongly connected component if and only if there is a directed path from A to B and from B to A. The computation will return a numerical job id, with which the results can later be queried or written back to the database. This is an asynchronous job which returns the job id immediately. Use the GET job API with the job id to get information on progress, errors and the outcome of the computation.

Process a previously loaded graph and a computation which has computed connected components (weakly or strongly) by aggregating some vertex data over each component found. The result will be one distribution map for each connected component. It is stored in memory. The computation will return a numerical job id, with which the results can later be queried or written back to the database. This is an asynchronous job which returns the job id immediately. Use the GET job API with the job id to get information on progress, errors and the outcome of the computation.

Process a previously loaded graph with the pagerank algorithm and store the results in-memory. There are some parameters controlling the computation like the damping factor and the maximal number of supersteps. See the input message documentation for details. The computation will return a numerical job id, with which the results can later be queried or written back to the database. This is an asynchronous job which returns the job id immediately. Use the GET job API with the job id to get information on progress, errors and the outcome of the computation.

Process a previously loaded graph with custom python based execution algorithm and store the results in-memory. See the input message documentation for details. The computation will return a numerical job id, with which the results can later be queried or written back to the database. This is an asynchronous job which returns the job id immediately. Use the GET job API with the job id to get information on progress, errors and the outcome of the computation.

Process a previously loaded graph with the irank algorithm and store the results in-memory. The "irank" algorithms is a variant of pagerank, which changes the initial weight of each vertex. Rather than being 1/N where N is the number of vertices, the value is here different depending on from which vertex collection the vertex comes. If V is from vertex collection C and N is the number of vertices in C, then the initial weight of V is 1/N. As with pagerank, the total sum of ranks stays the same as an invariant of the algorithm. There are some parameters controlling the computation like the damping factor and the maximal number of supersteps. See the input message documentation for details. The computation will return a numerical job id, with which the results can later be queried or written back to the database. This is an asynchronous job which returns the job id immediately. Use the GET job API with the job id to get information on progress, errors and the outcome of the computation.

Process a previously loaded graph with the label propagation algorithm and store the results in-memory. There are some parameters controlling the computation like the name of the attribute to choose the start label from, a flag to indicate if the synchronous or the asynchronous variant is used and the maximal number of supersteps. See the input message documentation for details. The computation will return a numerical job id, with which the results can later be queried or written back to the database. This is an asynchronous job which returns the job id immediately. Use the GET job API with the job id to get information on progress, errors and the outcome of the computation.

Process a previously loaded graph with the attribute propagation algorithm and store the results in-memory. The algorithm basically reads a list of labels from a column for each vertex (see the loaddata operation, for which one can configure which attributes are loaded into the column store). The value can be empty or a string or a list of strings and the set of labels for each vertex is initialized accordingly. The algorithm will then simply propagate each label in each label set along the edges to all reachable vertices and thus compute a new set of labels. After a specified maximal number of steps or if no label set changes any more the algorithm stops. BEWARE: If there are many labels in the system and the graph is well-connected then the result can be huge! There are some parameters controlling the computation like the name of the attribute to choose the start label from, whether the synchronous or the asynchronous variant is to be used, if we propagate along the the edges forwards or backwards and the maximal number of supersteps. See the input message documentation for details. The computation will return a numerical job id, with which the results can later be queried or written back to the database. This is an asynchronous job which returns the job id immediately. Use the GET job API with the job id to get information on progress, errors and the outcome of the computation.

Process a previously loaded graph with the linerank algorithm and store the results in-memory. The algorithm measures the importance of a vertex by aggregating the importance of its incident edges. This represents the amount of information that flows through the vertex, therefore the result of this algorithm can be taken as an approximation for betweenness centrality, which is much more computation-intensive. The edge importance is computed by the probability that a random walker, visiting edges via vertices with random restarts, will stay at the edge.

Stores the results from previous jobs into the deployment. One can specify a number of job ids but the requirement is that they produce the same number of results. For example, results from different algorithms which produce one result per vertex can be written to the database together. The target collection must already exist and must be writable. The job produces one document per result and one can prescribe which attribute names should be used for which result. There are some parameters controlling the computation. See the input message description for details. The computation will return a numerical job id, with which the progress can be monitored. This is an asynchronous job which returns the job id immediately. Use the GET job API with the job id to get information on progress, errors and the outcome of the job.

List the graphs in the engine.

Get information about a specific graph.

Delete a specific graph from memory.

List the jobs in the engine (loading, computing or storing).

Get information about a specific job (in particular progress and result when done).

Delete a specific job.

Shutdown service.