Abstract: Graph neural networks (GNNs) have achieved considerable success in dealing with graph-structured data by the message-passing mechanism. Actually, this mechanism relies on a fundamental ...
Abstract: This paper proposes a graph linear canonical transform (GLCT) by decomposing the linear canonical parameter matrix into fractional Fourier transform, scale transform, and chirp modulation ...
Depending on the underlying graph, you also need to handle cycles intelligently. In social networks, mutual relationships are ...
Scalable, high performance knowledge graph memory system with semantic retrieval, contextual recall, and temporal awareness. Provides any LLM client that supports the model context protocol (e.g., ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback