We introduce a special type of graph embedding called book embedding and apply it to posets. A book embedding scheme for bipartite graphs is given, and is used to extend the embeddings to general k-partite graphs. Finally, we view the Hasse diagram of a poset as a directed k-partite graph and use this scheme to derive a book embedding for arbitrary posets. Using this book embedding scheme, we a...

The selection of an embedding scheme is an important step in the modeling and prediction of chaotic dynamical systems. Theoretical work in embedding selection abounds in the literature. However in neural network research, mostly compute intensive methods for embedding selection exist. In this paper, we propose a novel embedding selection scheme based on cluster analysis. A neural network implem...

We show the equivalence of two state-of-the-art link prediction/knowledge graph completion methods: Nickel et al’s holographic embedding and Trouillon et al.’s complex embedding. We first consider a spectral version of the holographic embedding, exploiting the frequency domain in the Fourier transform for efficient computation. The analysis of the resulting method reveals that it can be viewed ...

Task mapping in modern high performance parallel computers can be modeled as a graph embedding problem, which simulates the mapping as embedding one graph into another and try to find the minimum wirelength for the mapping. Though embedding problems have been considered for several regular graphs, such as hypercubes into grids, binary trees into grids, et al, it is still an open problem for hyp...