3 مستودعات
Awesome GitHub RepositoriesInductive Learning
Methods for learning patterns from graph structures that can be generalized to previously unseen nodes or graphs.
Distinct from Graph Representation Learning: Distinct from Graph Representation Learning: focuses on the inductive ability to apply learned patterns to new datasets specifically.
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Awesome Inductive Learning GitHub Repositories
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- memgraph/memgraph
memgraph/memgraph
4,163عرض على GitHubMemgraph is an in-memory, distributed graph database designed for high-performance labeled property graph management. It utilizes a Cypher query engine for declarative data retrieval and manipulation, providing a scalable knowledge graph backend that integrates vector search and graph traversals. The system distinguishes itself as a real-time graph analytics platform, employing native C++ and CUDA implementations to execute complex network analysis and dynamic community detection on streaming data. It provides specialized support for AI integration, including GraphRAG capabilities, the constr
The product aggregates neighbor representations to learn inductive patterns applicable to new datasets.
C++cyphergraphgraph-algorithms - williamleif/graphsage
williamleif/GraphSAGE
3,657عرض على GitHubGraphSAGE is a graph neural network framework designed for inductive representation learning on large-scale graphs. It functions as an inductive graph embedding tool and neighborhood aggregation engine, enabling the generation of numerical node representations that generalize to previously unseen data. The system distinguishes itself by computing node embeddings through the aggregation of features from local neighborhoods rather than relying on a global lookup table. This approach allows the framework to operate as both a supervised graph classifier for predicting categorical node classes and
Implements an inductive learning framework for generating node embeddings that generalize to unseen data.
Python - dsgiitr/graph_nets
dsgiitr/graph_nets
1,237عرض على GitHubGraph Nets is a graph neural network library and educational toolkit implemented in PyTorch, providing implementations of popular graph representation learning algorithms and research papers. The project covers core graph machine learning tasks including semi-supervised node classification, inductive and unsupervised node embedding generation, and neighborhood feature aggregation. The library supports diverse algorithmic approaches for processing network structures, ranging from shared-parameter graph convolutions and attention-weighted neighborhood aggregation to spectral Chebyshev filtering
Leverage node feature information through sampling and aggregation strategies to efficiently compute embeddings for previously unseen graph nodes.
Jupyter Notebookchebyshev-polynomialsdeepwalkgraph-attention-networks
