… and the target graphs are related in such a way that the spectra of the source … graph that
are independently constructed, we propose to learn a pair of “aligned” bases on the two graphs …

… spectral alignment when learning graph convolution kernels. We align the spectral
representations of different brain graphs … the impact of aligning spectral embeddings in learning …

… The LPE model addresses key limitations of previous graph Transformers and is aligned
with the first four etiquettes presented in section 2.2. By concatenating the eigenvalues with the …

… or more recently, also leveraging spectral graph theory and the … led to the use of low-rank
spectral representations [37]. Thus, … the graph representation that we will use to align these two …

… I derive sufficient conditions for a small coarse graph to approximate a larger graph in the
sense of restricted spectral approximation. Crucially, this result holds for any number of levels …

… in comparative graph analysis namely network alignment, the … of spectral network alignment
techniques. Given the input graphs as the form of adjacency matrices, spectral alignment …

… described above, a knowledge graph can be represented as … Graph Entity Alignment In this
context, entity alignment aims to … as the source graph and the other as the target graph, and …

… We study the spectral gap of the Erdős–Rényi random graph through the connectivity … we
prove a condition on a graph that will imply an upper bound on the spectral gap. This lemma …

… alignment models, we apply the graph convolutional network (GCN) to achieve more robust
network embedding for the alignment … However, the conventional spectral-based GCN and …

… Those unreliable graphs might lead to suboptimal clustering results. To fill these gaps, in …
novel multi-view spectral clustering model which performs graph fusion and spectral clustering …