Graphs TitanGraph

---

class TitanGraph

Proxy to a graph in Titan, supports Gremlin query

Attributes

name	Set or get the name of the graph object.
status	Current graph life cycle status.

Methods

__init__(self[, name, _info])	Initialize the graph.
__init__(self, entity)	<Missing Doc>
__init__(self, entity)	<Missing Doc>
annotate_degrees(self, output_property_name[, degree_option, ...])	Make new graph with degrees.
annotate_weighted_degrees(self, output_property_name[, ...])	Calculates the weighted degree of each vertex with respect to an (optional) set of labels.
clustering_coefficient(self[, output_property_name, ...])	Coefficient of graph with respect to labels.
copy(self[, name])	Make a copy of the current graph.
export_to_graph(self)	Export from TitanGraph to Graph.
graph_clustering(self, edge_distance)	Performs graph clustering over an initial titan graph.
graphx_connected_components(self, output_property)	Implements the connected components computation on a graph by invoking graphx api.
graphx_pagerank(self, output_property[, input_edge_labels, ...])	Determine which vertices are the most important.
graphx_triangle_count(self, output_property[, input_edge_labels])	Number of triangles among vertices of current graph.
ml.belief_propagation(self, prior_property, posterior_property)	Classification on sparse data using Belief Propagation.
query.gremlin(self, gremlin)	Executes a Gremlin query.
vertex_sample(self, size, sample_type[, seed])	Make subgraph from vertex sampling.

__init__(self, name=None)

Initialize the graph.

Parameters:name : (default=None)