neuralogic.nn.module.meta package

Submodules

neuralogic.nn.module.meta.magnn module

class MAGNNLinear(in_channels: int, out_channels: int, output_name: str, feature_name: str, relation_name: str, type_name: str | None, meta_paths: ~typing.List[str], activation: ~neuralogic.core.constructs.function.function.TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>, aggregation: ~neuralogic.core.constructs.function.function.AggregationFunction = <neuralogic.core.constructs.function.function.AggregationFunction object>)[source]

Bases: MAGNNMean

Intra-metapath Aggregation module with Linear encoder from “MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding”. Which can be expressed as:

\[\mathbf{h}_{P(v,u)} = \mathbf{W}_p \cdot MEAN(\{\mathbf{x}_t | \forall t \in P(v,u) \})\]
\[\mathbf{h}^P_{v} = act(\sum_{u \in N^P_v} \mathbf{h}_{P(v,u)})\]

Where act is an activation function, \(P(v,u)\) is a single metapath instance, \(N^P_{v}\) is set of metapath-based neighbors.

Parameters:

  • in_channels (int) – Input feature size.

  • out_channels (int) – Output feature size.

  • output_name (str) – Output (head) predicate name of the module.

  • feature_name (str) – Feature predicate name to get features from.

  • relation_name (str) – Relation predicate name for connectivity checks between entities.

  • type_name (Optional[str]) – Metapath type predicate name. If none, meta_paths will be used instead.

  • meta_paths (List[str]) – Name of types forming a single metapath.

  • activation (TransformationFunction) – Activation function of the output. Default: Transformation.SIGMOID

  • aggregation (AggregationFunction) – Aggregation function of the output. Default: Aggreagtion.SUM

class MAGNNMean(output_name: str, feature_name: str, relation_name: str, type_name: str | None, meta_paths: ~typing.List[str], activation: ~neuralogic.core.constructs.function.function.TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>, aggregation: ~neuralogic.core.constructs.function.function.AggregationFunction = <neuralogic.core.constructs.function.function.AggregationFunction object>)[source]

Bases: Module

Intra-metapath Aggregation module with Mean encoder from “MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding”. Which can be expressed as:

\[\mathbf{h}_{P(v,u)} = MEAN(\{\mathbf{x}_t | \forall t \in P(v,u) \})\]
\[\mathbf{h}^P_{v} = act(\sum_{u \in N^P_v} \mathbf{h}_{P(v,u)})\]

Where act is an activation function, \(P(v,u)\) is a single metapath instance, \(N^P_{v}\) is set of metapath-based neighbors.

Parameters:

  • output_name (str) – Output (head) predicate name of the module.

  • feature_name (str) – Feature predicate name to get features from.

  • relation_name (str) – Relation predicate name for connectivity checks between entities.

  • type_name (Optional[str]) – Metapath type predicate name. If none, meta_paths will be used instead.

  • meta_paths (List[str]) – Name of types forming a single metapath.

  • activation (TransformationFunction) – Activation function of the output. Default: Transformation.SIGMOID

  • aggregation (AggregationFunction) – Aggregation function of the output. Default: Aggreagtion.SUM

neuralogic.nn.module.meta.meta module

class MetaConv(in_channels: int, out_channels: int, output_name: str, feature_name: str, role_name: str | None, roles: ~typing.List[str], activation: ~neuralogic.core.constructs.function.function.TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>, aggregation: ~neuralogic.core.constructs.function.function.AggregationFunction = <neuralogic.core.constructs.function.function.AggregationFunction object>)[source]

Bases: Module

Metagraph Convolutional Unit layer.

From Meta-GNN: metagraph neural network for semi-supervised learning in attributed heterogeneous information networks. Which can be expressed as:

\[\mathbf{x}^{\prime}_i = act(\mathbf{W_0} \cdot \mathbf{x}_i + {agg}_{j \in \mathcal{N}_r(i)} \sum_{k \in \mathcal{K}} (\mathbf{W_k} \cdot \mathbf{x}_j))\]

Where act is an activation function, agg aggregation function (by default average), \(W_0\) is a learnable root parameter and \(W_k\) is a learnable parameter for each role.

Parameters:

  • in_channels (int) – Input feature size.

  • out_channels (int) – Output feature size.

  • output_name (str) – Output (head) predicate name of the module.

  • feature_name (str) – Feature predicate name to get features from.

  • role_name (str, optional) – Role predicate name to use for role relations. When None, elements from roles are used instead.

  • roles (List[str]) – List of relations’ names.

  • activation (TransformationFunction, optional) – Activation function of the output. Default: Transformation.SIGMOID.

  • aggregation (AggregationFunction, optional) – Aggregation function of nodes’ neighbors. Default: Aggregation.AVG.

Module contents