Bayesian Vertex Nomination Using Content and Context

Suwan, Shakira; Lee, Dominic S.; Priebe, Carey E.

doi:10.1002/wics.1365

Cited by 7 publications

(7 citation statements)

References 26 publications

(40 reference statements)

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“…Recently proposed methods for vertex nomination [3,[17][18][19][20][21][22] have been quite successful in a variety of settings when the definition of similar is defined explicitly by a domain expert. Approaches are mainly combinatorial or spectral.…”

Section: Vertex Nominationmentioning

confidence: 99%

Leveraging semantically similar queries for ranking via combining representations

Helm¹,

Abdin²,

Pedigo³

et al. 2021

Preprint

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In modern ranking problems, different and disparate representations of the items to be ranked are often available. It is sensible, then, to try to combine these representations to improve ranking. Indeed, learning to rank via combining representations is both principled and practical for learning a ranking function for a particular query. In extremely data-scarce settings, however, the amount of labeled data available for a particular query can lead to a highly variable and ineffective ranking function. One way to mitigate the effect of the small amount of data is to leverage information from semantically similar queries. Indeed, as we demonstrate in simulation settings and real data examples, when semantically similar queries are available it is possible to gainfully use them when ranking with respect to a particular query. We describe and explore this phenomenon in the context of the bias-variance trade off and apply it to the data-scarce settings of a Bing navigational graph and the Drosophila larva connectome.

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Section: Vertex Nominationmentioning

confidence: 99%

Leveraging semantically similar queries for ranking via combining representations

Helm¹,

Abdin²,

Pedigo³

et al. 2021

Preprint

Self Cite

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“…Research involving social media communication networks has typically focused on homophily, the tendency of users to connect to others with similar properties (Barberá, 2014). A number of papers have employed features drawn from both the content and structure of network entities in pursuit of latent user attributes (Pennacchiotti and Popescu, 2011b;Campbell et al, 2014;Suwan et al, 2015).…”

Section: Previous Workmentioning

confidence: 99%

Fluency detection on communication networks

Lippincott

Durme

2016

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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When considering a social media corpus, we often have access to structural information about how messages are flowing between people or organizations. This information is particularly useful when the linguistic evidence is sparse, incomplete, or of dubious quality. In this paper we construct a simple model to leverage the structure of Twitter data to help determine the set of languages each user is fluent in. Our results demonstrate that imposing several intuitive constraints leads to improvements in performance and stability. We release the first annotated data set for exploring this task, and discuss how our approach may be extended to other applications.

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“…The classical formulation of the VN inference task can be stated as follows: given a network with latent community structure in which one of the communities is of particular interest and given a few vertices from the community of interest, the task in VN is to order the remaining vertices in the network into a nomination list, with the aim of having vertices from the community of interest concentrate at the top of the list. Thus, VN can also be thought of as a method for inferring missing vertex labels, and is related to the class/labeled instances acquisition task and collective classification methods of .…”

Section: Introductionmentioning

confidence: 99%

Vertex nomination via seeded graph matching

Patsolic

Park

Lyzinski

et al. 2020

Statistical Analysis

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Consider two networks on overlapping, nonidentical vertex sets. Given vertices of interest (VOIs) 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/noisy for brute-force graph matching. Our methodology identifies vertices in a local neighborhood of the VOIs 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 VOIs. We demonstrate the applicability of our methodology through simulations and real data examples. K E Y W O R D Sgraph inference, graph matching, graph mining, seeded graph matching, stochastic block model, vertex nomination Abbreviations: SGM, seeded graph matching; VN, vertex nomination; VOI, vertex of interest.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Bayesian Vertex Nomination Using Content and Context

Cited by 7 publications

References 26 publications

Leveraging semantically similar queries for ranking via combining representations

Leveraging semantically similar queries for ranking via combining representations

Fluency detection on communication networks

Vertex nomination via seeded graph matching

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