Search Results for author:
Found 32 papers, 5 papers with code
Detection of Model-based Planted Pseudo-cliques in Random Dot Product Graphs by the Adjacency Spectral Embedding and the Graph Encoder Embedding
In this paper, we explore the capability of both the Adjacency Spectral Embedding (ASE) and the Graph Encoder Embedding (GEE) for capturing an embedded pseudo-clique structure in the random dot product graph setting.
Gotta match 'em all: Solution diversification in graph matching matched filters
We present a novel approach for finding multiple noisily embedded template graphs in a very large background graph.
Adversarial contamination of networks in the setting of vertex nomination: a new trimming method
Here, a common suite of methods relies on spectral graph embeddings, which have been shown to provide both good algorithmic performance and flexible settings in which regularization techniques can be implemented to help mitigate the effect of an adversary.
Lost in the Shuffle: Testing Power in the Presence of Errorful Network Vertex Labels
Many two-sample network hypothesis testing methodologies operate under the implicit assumption that the vertex correspondence across networks is a priori known.
Clustered Graph Matching for Label Recovery and Graph Classification
Given a collection of vertex-aligned networks and an additional label-shuffled network, we propose procedures for leveraging the signal in the vertex-aligned collection to recover the labels of the shuffled network.
Leveraging semantically similar queries for ranking via combining representations
In modern ranking problems, different and disparate representations of the items to be ranked are often available.
Subgraph nomination: Query by Example Subgraph Retrieval in Networks
This paper introduces the subgraph nomination inference task, in which example subgraphs of interest are used to query a network for similarly interesting subgraphs.
The Importance of Being Correlated: Implications of Dependence in Joint Spectral Inference across Multiple Networks
We describe how this omnibus embedding can itself induce correlation, leading us to distinguish between inherent correlation -- the correlation that arises naturally in multisample network data -- and induced correlation, which is an artifice of the joint embedding methodology.
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.
Graph matching between bipartite and unipartite networks: to collapse, or not to collapse, that is the question
Graph matching consists of aligning the vertices of two unlabeled graphs in order to maximize the shared structure across networks; when the graphs are unipartite, this is commonly formulated as minimizing their edge disagreements.
Vertex Nomination, Consistent Estimation, and Adversarial Modification
Given a pair of graphs $G_1$ and $G_2$ and a vertex set of interest in $G_1$, the vertex nomination (VN) problem seeks to find the corresponding vertices of interest in $G_2$ (if they exist) and produce a rank list of the vertices in $G_2$, with the corresponding vertices of interest in $G_2$ concentrating, ideally, at the top of the rank list.
Maximum Likelihood Estimation and Graph Matching in Errorfully Observed Networks
Given a pair of graphs with the same number of vertices, the inexact graph matching problem consists in finding a correspondence between the vertices of these graphs that minimizes the total number of induced edge disagreements.
On a 'Two Truths' Phenomenon in Spectral Graph Clustering
Clustering is concerned with coherently grouping observations without any explicit concept of true groupings.
Matched Filters for Noisy Induced Subgraph Detection
To illustrate the possibilities and challenges of such problems, we use an algorithm that can exploit a partially known correspondence and show via varied simulations and applications to {\it Drosophila} and human connectomes that this approach can achieve good performance.
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.
Semiparametric spectral modeling of the Drosophila connectome
We present semiparametric spectral modeling of the complete larval Drosophila mushroom body connectome.
Vertex Nomination Via Seeded Graph Matching
Consider two networks on overlapping, non-identical vertex sets.
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.
Information Recovery in Shuffled Graphs via Graph Matching
While many multiple graph inference methodologies operate under the implicit assumption that an explicit vertex correspondence is known across the vertex sets of the graphs, in practice these correspondences may only be partially or errorfully known.
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.
Semi-External Memory Sparse Matrix Multiplication for Billion-Node Graphs
In contrast, we scale sparse matrix multiplication beyond memory capacity by implementing sparse matrix dense matrix multiplication (SpMM) in a semi-external memory (SEM) fashion; i. e., we keep the sparse matrix on commodity SSDs and dense matrices in memory.
Distributed, Parallel, and Cluster Computing
Scalable Out-of-Sample Extension of Graph Embeddings Using Deep Neural Networks
Several popular graph embedding techniques for representation learning and dimensionality reduction rely on performing computationally expensive eigendecompositions to derive a nonlinear transformation of the input data space.
Community Detection and Classification in Hierarchical Stochastic Blockmodels
We propose a robust, scalable, integrated methodology for community detection and community comparison in graphs.
Fast Embedding for JOFC Using the Raw Stress Criterion
The Joint Optimization of Fidelity and Commensurability (JOFC) manifold matching methodology embeds an omnibus dissimilarity matrix consisting of multiple dissimilarities on the same set of objects.
Graph Matching: Relax at Your Own Risk
Indeed, experimental results illuminate and corroborate these theoretical findings, demonstrating that excellent results are achieved in both benchmark and real data problems by amalgamating the two approaches.
Seeded Graph Matching Via Joint Optimization of Fidelity and Commensurability
We present a novel approximate graph matching algorithm that incorporates seeded data into the graph matching paradigm.
Spectral Clustering for Divide-and-Conquer Graph Matching
We present a parallelized bijective graph matching algorithm that leverages seeds and is designed to match very large graphs.
Perfect Clustering for Stochastic Blockmodel Graphs via Adjacency Spectral Embedding
Vertex clustering in a stochastic blockmodel graph has wide applicability and has been the subject of extensive research.
A central limit theorem for scaled eigenvectors of random dot product graphs
We prove a central limit theorem for the components of the largest eigenvectors of the adjacency matrix of a finite-dimensional random dot product graph whose true latent positions are unknown.
Seeded Graph Matching
Given two graphs, the graph matching problem is to align the two vertex sets so as to minimize the number of adjacency disagreements between the two graphs.
