Analytical App¶

AppAssets¶

class graphscope.framework.app.AppAssets(algo, gar=None, **kwargs)[source]¶

A class holds the bytes of the gar resource.

Assets includes name (for builtin algorithm), and gar (for user defined algorithm), and its type (one of cpp_pie, cython_pie, cython_pregel.

The instance of this class can be passed to init graphscope.App.

algo¶

Name of the algorithm

Type: str

type¶

Type of the algorithm

Type: str

gar¶

Byte content of user defined algorithm

Type: bytes

signature¶

Unique identifier of this assets.

Type: str

__init__(algo, gar=None, **kwargs)[source]¶

Init assets of the algorithm.

Parameters

algo (str) – Represent specific algo inside resource.
gar (bytes or BytesIO, optional) – The bytes that encodes the application’s source code. Default to None
kwargs – Other params, e.g. vd_type and md_type in cython app.

__repr__() → str[source]¶: Return repr(self).

__weakref__¶: list of weak references to the object (if defined)

property algo¶

Algorithm name, e.g. sssp, pagerank.

Returns: Algorithm name of this asset.
Return type: str

property gar¶

Gar resource.

Returns: gar resource of this asset.
Return type: bytes

is_compatible(graph)[source]¶

Determine if this algorithm can run on this type of graph.

Parameters

graph (Graph) – A graph instance.

Raises

InvalidArgumentError –
- gs_conf.yaml not exist in gar resource. - App is not compatible with graph or - Algo not found in gar resource.
ScannerError –
- Yaml file format is incorrect.

property signature¶

Generate a signature of the app assets by its algo name (and gar resources).

Used to uniquely identify a app assets. :returns: signature of this assets :rtype: str

property type¶

Algorithm type, one of cpp_pie, cython_pie or cython_pregel.

Returns: Algorithm type of this asset.
Return type: str

BuiltIn apps¶

graphscope.bfs(graph, src=0)[source]¶

Breadth first search from the src on projected simple graph.

Parameters

graph (Graph) – A projected simple graph.
src (int, optional) – Source vertex of breadth first search. Defaults to 0.

Returns

a context with each vertex with a distance from the source.

Return type

VertexDataContext

Examples:

import graphscope as gs
sess = gs.session()
g = sess.g()
pg = g.project(vertices={"vlabel": []}, edges={"elabel": []})
r = gs.bfs(pg, 6)  # use 6 as source vertex
s.close()

graphscope.pagerank(graph, delta=0.85, max_round=10)[source]¶

Evalute PageRank on a graph.

Parameters

graph (Graph) – A projected simple graph.
delta (float, optional) – Dumping factor. Defaults to 0.85.
max_round (int, optional) – Maximum number of rounds. Defaults to 10.

Returns

A context with each vertex assigned with the pagerank value.

Return type

VertexDataContext

Examples:

import graphscope as gs
sess = gs.session()
g = sess.g()
pg = g.project(vertices={"vlabel": []}, edges={"elabel": []})
r = gs.pagerank(pg, delta=0.85, max_round=10)
s.close()

graphscope.sssp(graph, src=0)[source]¶

Compute single source shortest path on the graph.

Parameters

graph (Graph) – A projected simple graph.
src (int, optional) – The source vertex. Defaults to 0.

Returns

A context with each vertex assigned with the shortest distance from the src.

Return type

VertexDataContext

Examples:

import graphscope as gs
sess = gs.session()
g = sess.g()
pg = g.project(vertices={"vlabel": []}, edges={"elabel": []})
r = gs.sssp(pg, src=0)
s.close()

graphscope.wcc(graph)[source]¶

Evaluate weakly connected components on the graph.

Parameters: graph (Graph) – A projected simple graph.
Returns: A context with each vertex assigned with the component ID.
Return type: VertexDataContext

Examples:

import graphscope as gs
sess = gs.session()
g = sess.g()
pg = g.project(vertices={"vlabel": []}, edges={"elabel": []})
r = gs.wcc(pg)
s.close()

graphscope.cdlp(graph, max_round=10)[source]¶

Evaluate Community Detection with Label Propagation.

Parameters

graph (Graph) – A projected simple graph.
max_round (int, optional) – Maximum rounds. Defaults to 10.

Returns

A context with each vertex assigned with a community ID.

Return type

VertexDataContext

Examples:

import graphscope as gs
sess = gs.session()
g = sess.g()
pg = g.project(vertices={"vlabel": []}, edges={"elabel": []})
r = gs.cdlp(g, max_round=10)
s.close()

graphscope.clustering(graph)[source]¶

Local clustering coefficient of a node in a Graph is the fraction of pairs of the node’s neighbors that are adjacent to each other.

Parameters: graph (Graph) – A projected simple graph.
Returns: A context with each vertex assigned the computed clustering value.
Return type: VertexDataContext

Examples:

import graphscope as gs
sess = gs.session()
g = sess.g()
pg = g.project(vertices={"vlabel": []}, edges={"elabel": []})
r = gs.clustering(pg)
s.close()

graphscope.degree_centrality(graph, centrality_type='both')[source]¶

The degree centrality values are normalized by dividing by the maximum possible degree in a simple graph n-1 where n is the number of nodes in G.

Parameters

graph (Graph) – A projected simple graph.
centrality_type (str, optional) – Available options are in/out/both. Defaults to “both”.

Returns

A context with each vertex assigned with the computed degree centrality.

Return type

VertexDataContext

Examples:

import graphscope as gs
sess = gs.session()
g = sess.g()
pg = g.project(vertices={"vlabel": []}, edges={"elabel": []})
r = gs.degree_centrality(pg, centrality_type="both")
s.close()

graphscope.eigenvector_centrality(graph, tolerance=1e-06, max_round=100)[source]¶

Compute the eigenvector centrality for the graph. See more about eigenvector centrality here: https://networkx.org/documentation/networkx-1.10/reference/generated/networkx.algorithms.centrality.eigenvector_centrality.html

Parameters

graph (Graph) – A projected simple graph.
tolerance (float, optional) – Defaults to 1e-06.
max_round (int, optional) – Defaults to 100.

Returns

A context with each vertex assigned with a gv-centrality.

Return type

VertexDataContext

Examples:

import graphscope as gs
sess = gs.session()
g = sess.g()
pg = g.project(vertices={"vlabel": []}, edges={"elabel": []})
r = gs.eigenvector_centrality(pg)
s.close()

graphscope.hits(graph, tolerance=0.01, max_round=100, normalized=True)[source]¶

Compute HITS on graph.

Hyperlink-Induced Topic Search (HITS; also known as hubs and authorities) is a link analysis algorithm that rates Web pages. See more here: https://en.wikipedia.org/wiki/HITS_algorithm

Parameters

graph (Graph) – A projected simple graph.
tolerance (float, optional) – Defaults to 0.01.
max_round (int, optional) – Defaults to 100.
normalized (bool, optional) – Whether to normalize the result to 0-1. Defaults to True.

Returns

A context with each vertex assigned with the HITS value.

Return type

VertexPropertyContext

Examples:

import graphscope as gs
sess = gs.session()
g = sess.g()
pg = g.project(vertices={"vlabel": []}, edges={"elabel": []})
r = gs.hits(pg)
s.close()

graphscope.k_core(graph, k: int)[source]¶

K-cores of the graph are connected components that are left after all vertices of degree less than k have been removed.

Parameters

graph (Graph) – A projected simple graph.
k (int) – The k for k-core.

Returns

A context with each vertex assigned with a boolean: 1 if the vertex satisfies k-core, otherwise 0.

Return type

VertexDataContext

Examples:

import graphscope as gs
sess = gs.session()
g = sess.g()
pg = g.project(vertices={"vlabel": []}, edges={"elabel": []})
r = gs.k_core(pg)
s.close()

graphscope.katz_centrality(graph, alpha=0.1, beta=1.0, tolerance=1e-06, max_round=100, normalized=True)[source]¶

Compute the Katz centrality.

See more details for Katz centrality here: https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.centrality.katz_centrality_numpy.html

Parameters

graph (Graph) – A projected simple graph.
alpha (float, optional) – Auttenuation factor. Defaults to 0.1.
beta (float, optional) – Weight attributed to the immediate neighborhood. Defaults to 1.0.
tolerance (float, optional) – Error tolerance. Defaults to 1e-06.
max_round (int, optional) – Maximun number of rounds. Defaults to 100.
normalized (bool, optional) – Whether to normalize result values. Defaults to True.

Returns

A context with each vertex assigned with the computed katz_centrality.

Return type

VertexDatacontext

Examples:

import graphscope as gs
sess = gs.session()
g = sess.g()
pg = g.project(vertices={"vlabel": []}, edges={"elabel": []})
r = gs.katz_centrality(pg)
s.close()

graphscope.lpa(graph, max_round=10)[source]¶

Evaluate (multi-) label propagation on a property graph.

Parameters

graph (Graph) – A property graph.
max_round (int, optional) – Maximum number of rounds. Defaults to 10.

Returns

A context with each vertex, following an array of propagated labels.

Return type

LabeledVertexPropertyContext

Examples:

import graphscope as gs
sess = gs.session()
g = sess.g()
r = gs.lpa(g)
s.close()

graphscope.triangles(graph)[source]¶

Evaluate triangle counting of the graph G.

Parameters: graph (Graph) – A projected simple graph.
Returns: A context with each vertex assigned with the triangle counting result.
Return type: VertexDataContext

Examples:

import graphscope as gs
sess = gs.session()
g = sess.g()
pg = g.project(vertices={"vlabel": []}, edges={"elabel": []})
r = gs.triangles(pg)
s.close()

graphscope.louvain(graph, min_progress=1000, progress_tries=1)[source]¶

Compute best partition on the graph by louvain.

Parameters

graph (Graph) – A projected simple graph.
min_progress – The minimum delta X required to be considered progress, where X is the number of nodes that have changed their community on a particular pass. Delta X is then the difference in number of nodes that changed communities on the current pass compared to the previous pass.
progress_tries – number of times the min_progress setting is not met before exiting form the current level and compressing the graph.

Returns

A context with each vertex assigned with id of community it belongs to.

Return type

VertexDataContext

References

[1] Blondel, V.D. et al. Fast unfolding of communities in large networks. J. Stat. Mech 10008, 1-12(2008).

[2] https://github.com/Sotera/distributed-graph-analytics

[3] https://sotera.github.io/distributed-graph-analytics/louvain/

Examples:

import graphscope as gs
s = gs.session()
g = s.load_from('The parameters for loading a graph...')
pg = g.project(vertices={"vlabel": []}, edges={"elabel": ["weight"]})
r = gs.louvain(pg)
s.close()

App object¶

class graphscope.framework.app.App(graph, app_assets: graphscope.framework.app.AppAssets)[source]¶

An application that can run on graphs and produce results.

Analytical engine will build the app dynamic library when instantiate a app instance. The dynamic library will be reused if subsequent app’s signature matches one of previous ones.

key¶

Identifier of the app, associated with the dynamic library path. set by analytical engine after library is built.

Type: str

signature¶

Combination of app_assets’s and graph’s signature.

Type: str

session_id¶

Session id of the session that associated with the app.

Type: str

algo¶

Algorithm name of app_assets.

Type: str

gar¶

Gar content of app_assets.

Type: str

__init__(graph, app_assets: graphscope.framework.app.AppAssets)[source]¶

Create an application using given gar file, or given application: class name.

Parameters

graph (Graph) – A Graph instance.
app_assets – A AppAssets instance.

Raises

TypeError – The type of app_assets incorrect.

__weakref__¶: list of weak references to the object (if defined)

property algo¶

Algorithm name, e.g. sssp, pagerank.

Returns: Algorithm name of this asset.
Return type: str

property gar¶

Gar resource.

Returns: gar resource of this asset.
Return type: bytes

property key¶: A unique identifier of App.

loaded()[source]¶

Since key is only set by engine after it load the app, and unset to None when unload,: we can use the key to detect whether the app is loaded.

Returns: The app is loaded or not.
Return type: bool

property session_id¶

Return the session_id, which is copied from the graph.

Returns: Id of the session which loaded the app.
Return type: str

property signature¶: Signature is computed by all critical components of the App.

unload()[source]¶: Unload app. Both on engine side and python side. Set the key to None.

Functions¶

graphscope.framework.app.load_app(algo[, gar])

Load an app from gar.