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Vertex Nomination Via Local Neighborhood Matching
TLDR
In this paper, the authors proposed a method to identify vertices in a local neighborhood of the vertices of interest in the first network that have verifiable corresponding nodes in the second network, referred to as seeds.Abstract:
Consider two networks on overlapping, non-identical vertex sets. Given vertices of interest in the first network, we seek to identify the corresponding vertices, if any exist, in the second network. While in moderately sized networks graph matching methods can be applied directly to recover the missing correspondences, herein we present a principled methodology appropriate for situations in which the networks are too large for brute-force graph matching. Our methodology identifies vertices in a local neighborhood of the vertices of interest in the first network that have verifiable corresponding vertices in the second network. Leveraging these known correspondences, referred to as seeds, we match the induced subgraphs in each network generated by the neighborhoods of these verified seeds, and rank the vertices of the second network in terms of the most likely matches to the original vertices of interest. We demonstrate the applicability of our methodology through simulations and real data examples.read more
Citations
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Journal ArticleDOI
Alignment strength and correlation for graphs
TL;DR: It is illustrated that exact graph matching runtime and also matchability are both functions of $\rho_T$, with thresholding behavior starkly illustrated in matchability.
Journal ArticleDOI
Vertex nomination: The canonical sampling and the extended spectral nomination schemes
Jordan Yoder,Li Chen,Henry Pao,Eric W. Bridgeford,Keith Levin,Donniell E. Fishkind,Carey E. Priebe,Vince Lyzinski +7 more
TL;DR: This paper introduces a scalable, Markov chain Monte Carlo-based approximation of the canonical scheme, and extends the spectral partitioning nomination scheme to include a novel semisupervised clustering framework to improve upon the precision of $\mathcal{L}^P$.
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Vertex nomination, consistent estimation, and adversarial modification
Joshua Agterberg,Youngser Park,Jonathan Larson,Christopher White,Carey E. Priebe,Vince Lyzinski +5 more
TL;DR: In this paper, the authors define and derive the analogue of Bayes optimality for VN with multiple vertices of interest, and define the notion of maximal consistency classes in vertex nomination.
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Vertex Nomination, Consistent Estimation, and Adversarial Modification
Joshua Agterberg,Youngser Park,Jonathan Larson,Christopher White,Carey E. Priebe,Vince Lyzinski +5 more
TL;DR: This paper demonstrates that this vertex nomination scheme performs effectively in the uncontaminated setting; adversarial network contamination adversely impacts the performance of the VN scheme; and network regularization successfully mitigates the impact of the contamination.
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Subgraph nomination: Query by Example Subgraph Retrieval in Networks
TL;DR: This paper introduces the subgraph nomination inference task, in which example subgraphs of interest are used to query a network for similarly interesting sub graphs, and examines the nuanced effect that user-supervision can have on performance, both analytically and across real and simulated data examples.
References
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Journal ArticleDOI
The Hungarian method for the assignment problem
TL;DR: This paper has always been one of my favorite children, combining as it does elements of the duality of linear programming and combinatorial tools from graph theory, and it may be of some interest to tell the story of its origin this article.
{SNAP Datasets}: {Stanford} Large Network Dataset Collection
Jure Leskovec,Andrej Krevl +1 more
TL;DR: A collection of more than 50 large network datasets from tens of thousands of node and edges to tens of millions of nodes and edges that includes social networks, web graphs, road networks, internet networks, citation networks, collaboration networks, and communication networks.
Journal ArticleDOI
Stochastic blockmodels: First steps
TL;DR: Estimation techniques are developed for the special case of a single relation social network, with blocks specified a priori, and an extension of the model allows for tendencies toward reciprocation of ties beyond those explained by the partition.
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Mixed membership stochastic blockmodels
TL;DR: The mixed membership stochastic block model as discussed by the authors extends block models for relational data to ones which capture mixed membership latent relational structure, thus providing an object-specific low-dimensional representation.