Automatic feature extraction and node role assignment for transfer learning on graphs
Automatic feature extraction and node role assignment for transfer learning on graphs; based on the ReFeX/RolX algorithms [1, 2] of Henderson, et al.
A fundamental problem for learning on graphs is extracting meaningful features.
GraphRole provides the
RecursiveFeatureExtractor class to automate this process by extracting recursive features capturing local and neighborhood ("regional") structural properties of a given graph. The specific implementation follows that of the ReFeX algorithm . Node features (e.g., degree) and ego-net features (e.g., neighbors, number of internal vs. external edges) are extracted and then recursively aggregated over each node's neighbors' features until no additional information is encoded. As is shown in , these recursive, "regional" features facilitate node classification and perform quite well in transfer learning tasks.
GraphRole also provides the
RoleExtractor class for node role assignment (a form of classification). Different nodes play different structural roles in a graph, and using recursive regional features, these roles can be identified and assigned to collections of nodes. As they are structural in nature, node roles differ from and are often more intuitive than the commonly used communities of nodes. In particular, roles can generalize across graphs whereas the notion of communities cannot . Identification and assignment of node roles has been shown to facilitate many graph learning tasks.
Please see [1, 2] for more technical details.
This package is hosted on PyPI and can be installed via
$ pip install graphrole
To instead install from source:
$ git clone https://github.com/dkaslovsky/GraphRole.git $ cd GraphRole $ python setup.py install
An example of
GraphRole usage is found in the
examples directory. The notebook
(also available via nbviewer)
walks through feature extraction and role assignment for the well-known
karate_club_graph that is included with
NetworkX. Recursive features are extracted and used to learn role assignments for each node in the graph. The graph is shown above with each node colored corresponding to its role.
The extracted roles reflect structural properties of the graph at the node level. The nodes
33 (dark green) are central to the graph and are connected to many other nodes. Nodes
32 are assigned to a similar role (red). In contrast, the roles colored as dark blue, light blue, and pink are found at the periphery of the graph. Notably, nodes need not be near one another to be assigned to the same role; instead nodes with similar properties are grouped together across the graph by their role assignments.
Although not reflected by this example, weighted and directed graphs are also supported and will yield weighted and directed variants of the extracted features.
For general usage, begin by importing the two feature and role extraction classes:
>>> from graphrole import RecursiveFeatureExtractor, RoleExtractor
Features are then extracted from a graph
G into a
>>> feature_extractor = RecursiveFeatureExtractor(G) >>> features = feature_extractor.extract_features()
Next, these features are used to learn roles. The number of roles is automatically determined by
a model selection procedure when
n_roles=None is passed to the
RoleExtractor class instance.
n_roles can be set to a desired number of roles to be extracted.
>>> role_extractor = RoleExtractor(n_roles=None) >>> role_extractor.extract_role_factors(features)
The role assignment for each node can be retrieved as a dictionary:
Alternatively, roles can be viewed as a soft assignment and a node's percent membership to each role
can be retrieved as a
An interface for graph data structures is provided in the
graphrole.graph.interface module. Implementations for
igraph are included.
igraph package is not included in
requirements.txt and thus will need to be manually installed
if desired. This is due to additional installation requirements beyond
pip install python-igraph; see
the igraph documentation for more details. Note that all tests
igraph will be skipped if it is not installed.
To add an implementation of an additional graph library or data structure:
- Subclass the
graphrole.graph.interface.base.pyand implement the required methods
- Update the
graphrole.graph.interface.__init__.pyto make the new subclass discoverable
- Add tests by trivially implementing a
setUpClass()classmethod of a subclass of
- If desired, a similar procedure allows the feature extraction tests to be run using the added interface
by again trivially implementing a
setUpClass()classmethod of a subclass of
Model explanation ("sense making") will be added to the
RoleExtractor class in a future release.
To run tests:
$ python -m unittest discover -v
As noted above, the tests for the
igraph interface are skipped when
igraph is not installed. Because this package is intentionally not required, the test coverage reported above is much lower than when
igraph is installed and its interface tests are not skipped (97% coverage to date).
 Henderson, et al. It’s Who You Know: Graph Mining Using Recursive Structural Features.
 Henderson, et al. RolX: Structural Role Extraction & Mining in Large Graphs.
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