GraphScope models graph data as Property Graph, in which the edges/vertices are labeled and each label may have many properties.

## Building a Graph¶

To load a property graph to GraphScope, we provide a method g() defined in Session.

First, we create a session, then a graph instance inside that session.

sess = graphscope.session()
graph = sess.g()


The class Graph has several methods:

def add_vertices(self, vertices, label="_", properties=[], vid_field=0):
pass

def add_edges(self, edges, label="_", properties=[], src_label=None, dst_label=None, src_field=0, dst_field=1):
pass


These methods helps users to construct the schema of the property graph iteratively.

We can add a kind of vertices to graph.

The parameters contain:

• A loader for data source, which can be a file location, or a numpy, etc. See more details in Loader object.

• The label name of the vertex.

• A list of properties, the names should consistent to the header_row of the data source file or pandas. This list is optional. When use default value, all columns except the vertex_id column will be added as properties.

• The column used as vertex_id. The value in this column of the data source will be used for src/dst when loading edges.

Let’s see an example.

graph = graph.add_vertices(
label="student",  # Label name
properties=["name", "lesson_number", "avg_score"],  # Columns loaded as property
vid_field="student_id"  # Columns used for vertex_id
)


We can also omit certain parameters for vertices.

• properties can be empty, which means that all columns are selected as properties;

• vid can be represented by a number of index. Default is 0, which is the first column.

In the simplest case, the configuration can only contains a loader. In this case, the first column is used as vid, and the rest columns are used as properties.

graph.add_vertices("file:///home/admin/student.v", label="student")


Then we can add a kind of edges to graph.

The parameter contains:

• a Loader object for data source, it tells graphscope where to find the data for this label, it can be a file location, or a numpy, etc.

• The label name of the edge.

• a list of properties, the names should consistent to the header_row of the data source file or pandas. This list is optional. When it omitted or empty, all columns except the src/dst columns will be added as properties.

• The label name of the source vertex.

• The label name of the destination vertex.

• The column use for source vertex id.

• The column used for destination vertex id.

Let’s see an example.

graph = graph.add_edges(
label="group",  # Label name
properties=["group_id", "member_size"],  # Selected column names in group.e, will load as properties
src_label="student",  # Label name of the source vertex
dst_label="student",  # Label name of the destination vertex
src_field="leader_student_id",  # Use leader_student_id column as src id
dst_field="member_student_id",  # Use member_student_id column as dst id
)


In some cases, an edge label may connect two kinds of vertices. For example, in a graph, two kinds of edges are labeled with group but represents two relations. i.e., teacher -> group <- student and student <- group <- student. In this case, we can simple add the relation again with the same edge label, but with different source and destination label.

graph = graph.add_edges("file:///home/admin/group.e",
label="group",
properties=["group_id", "member_size"],
src_label="student", dst_label="student",
)

label="group",
properties=["group_id", "member_size"],
src_label="teacher", dst_label="student",
src_field="teacher_in_charge_id", dst_field="member_student_id"
)


Some parameters can omitted for edges. e.g., properties can be empty, which means to select all columns

graph = graph.add_edges(
label="group",
src_label="student", dst_label="student",
)


Src and dst fields can be assigned by number, which represents the column index in the data source.

The following statement means the first column is used as src_id and the second column is used as dst_id:

graph = graph.add_edges(
label="group",
src_label="student", dst_label="student",
src_field=0, dst_field=1,
)


The default value of src_field is 0, and default value of dst_field is 1. So if your edges use the first column as source vid, and second column as destination vid, you can just use the default value for the parameter.

graph = graph.add_edges(
label="group",
src_label="student", dst_label="student",
)


If there is only one vertex label in the graph, the label of vertices can be omitted. GraphScope will infer the source and destination vertex label is that very label.

graph = sess.g()
# GraphScope will assign src_label and dst_label to student automatically.


Moreover, the vertices can be totally omitted. graphscope will extract vertices ids from edges, and a default label _ will assigned to all vertices in this case.

Note this have some constraints that there cannot be any manually added vertex in graphs. It only serve the most simple cases.

graph = sess.g()
# After loaded, the graph will have an vertex label called _, and an edge label called group.


The class Graph has three meta options, which are:

• oid_type, can be int64_t or string. Default to int64_t cause it’s more faster and costs less memory.

• directed, bool, default to True. Controls load an directed or undirected Graph.

• generate_eid, bool, default to True. Whether to automatically generate an unique id for all edges.

Let’s make the example complete:

sess = graphscope.session()
graph = sess.g()

"student",
["name", "lesson_nums", "avg_score"],
"student_id",
)
"/home/admin/teacher.v", "teacher", ["name", "salary", "age"], "teacher_id"
)
"group",
["group_id", "member_size"],
src_label="student",
dst_label="student",
)
"group",
["group_id", "member_size"],
src_label="teacher",
dst_label="student",
)


A more complex example to load LDBC snb graph can be find here.

## Graphs from Numpy and Pandas¶

The datasource aforementioned is an object of Loader. A loader wraps a location or the data itself. graphscope supports load a graph from pandas dataframes or numpy ndarrays.

import pandas as pd

df_v = pd.read_csv('student.v', sep=',', usecols=['student_id', 'lesson_nums', 'avg_score'])

# use a dataframe as datasource, properties omitted, col_0/col_1 will be used as src/dst by default.
# (for vertices, col_0 will be used as vertex_id by default)


import numpy

array_e = [df_e[col].values for col in ['leader_student_id', 'member_student_id', 'member_size']]
array_v = [df_v[col].values for col in ['student_id', 'lesson_nums', 'avg_score']]



## Graphs from Given Location¶

When a loader wraps a location, it may only contains a str. The string follows the standard of URI. When receiving a request for loading graph from a location, graphscope will parse the URI and invoke corresponding loader according to the schema.

Currently, graphscope supports loaders for local, s3, oss, hdfs: Data is loaded by libvineyard , libvineyard takes advantage of fsspec to resolve specific scheme and formats. Any additional specific configurations can be passed in kwargs of Loader, and these configurations will directly be passed to corresponding storage class. Like host and port to HDFS, or access-id, secret-access-key to oss or s3.

from graphscope.framework.loader import Loader


User can implement customized driver to support additional data sources. Take ossfs as an example, User need to subclass AbstractFileSystem, which is used as resolve to specific protocol scheme, and AbstractBufferFile to do read and write. The only methods user need to override is _upload_chunk, _initiate_upload and _fetch_range. In the end user need to use fsspec.register_implementation('protocol_name', 'protocol_file_system') to register corresponding resolver.