arXiv:2104.08163
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Finding Motifs in Knowledge Graphs using Compression
원문 초록
We introduce a method to find network motifs in knowledge graphs. Network motifs are useful patterns or meaningful subunits of the graph that recur frequently. We extend the common definition of a network motif to coincide with a basic graph pattern. We introduce an approach, inspired by recent work for simple graphs, to induce these from a given knowledge graph, and show that the motifs found reflect the basic structure of the graph. Specifically, we show that in random graphs, no motifs are found, and that when we insert a motif artificially, it can be detected. Finally, we show the results of motif induction on three real-world knowledge graphs.
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Knowledge Graph Completion with Mixed Geometry Tensor Factorization
Grokking in the Wild: Data Augmentation for Real-World Multi-Hop Reasoning with Transformers
ConceptFormer: Towards Efficient Use of Knowledge-Graph Embeddings in Large Language Models
DiffusionCom: Structure-Aware Multimodal Diffusion Model for Multimodal Knowledge Graph Completion
Knowledge Graph Embedding via Graph Attenuated Attention Networks
DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge
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