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Found 10 papers, 1 papers with code
Vertex Nomination in Richly Attributed Networks
In this paper, we explore, both theoretically and practically, the dual roles of content (i. e., edge and vertex attributes) and context (i. e., network topology) in vertex nomination.
Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings
In both cases, we prove that when the underlying graph is generated according to a latent space model called the random dot product graph, which includes the popular stochastic block model as a special case, an out-of-sample extension based on a least-squares objective obeys a central limit theorem about the true latent position of the out-of-sample vertex.
Recovering shared structure from multiple networks with unknown edge distributions
In increasingly many settings, data sets consist of multiple samples from a population of networks, with vertices aligned across these networks.
Out-of-sample extension of graph adjacency spectral embedding
Many popular dimensionality reduction procedures have out-of-sample extensions, which allow a practitioner to apply a learned embedding to observations not seen in the initial training sample.
Vertex nomination: The canonical sampling and the extended spectral nomination schemes
There are vertex nomination schemes in the literature, including the optimally precise canonical nomination scheme~$\mathcal{L}^C$ and the consistent spectral partitioning nomination scheme~$\mathcal{L}^P$.
On consistent vertex nomination schemes
Given a vertex of interest in a network $G_1$, the vertex nomination problem seeks to find the corresponding vertex of interest (if it exists) in a second network $G_2$.
Statistical inference on random dot product graphs: a survey
In this survey paper, we describe a comprehensive paradigm for statistical inference on random dot product graphs, a paradigm centered on spectral embeddings of adjacency and Laplacian matrices.
Query-by-Example Search with Discriminative Neural Acoustic Word Embeddings
Query-by-example search often uses dynamic time warping (DTW) for comparing queries and proposed matching segments.
On the Consistency of the Likelihood Maximization Vertex Nomination Scheme: Bridging the Gap Between Maximum Likelihood Estimation and Graph Matching
Given a graph in which a few vertices are deemed interesting a priori, the vertex nomination task is to order the remaining vertices into a nomination list such that there is a concentration of interesting vertices at the top of the list.
Laplacian Eigenmaps from Sparse, Noisy Similarity Measurements
In particular, we consider Laplacian eigenmaps embeddings based on a kernel matrix, and explore how the embeddings behave when this kernel matrix is corrupted by occlusion and noise.
