no code implementations • 22 Jun 2024 • Amel Awadelkarim, Johan Ugander

In this work, we introduce and taxonomize approaches for jointly modeling distributions over top-$k$ partial orders and list lengths $k$, considering two classes of approaches: composite models that view a partial order as a truncation of a total order, and augmented ranking models that model the construction of the list as a sequence of choice decisions, including the decision to stop.

no code implementations • 30 Apr 2024 • David Liu, Arjun Seshadri, Tina Eliassi-Rad, Johan Ugander

In this work, we show that node-wise repulsion is, in aggregate, an approximate re-centering of the node embedding dimensions.

1 code implementation • 28 Feb 2024 • Serina Chang, Frederic Koehler, Zhaonan Qu, Jure Leskovec, Johan Ugander

A common network inference problem, arising from real-world data constraints, is how to infer a dynamic network from its time-aggregated adjacency matrix and time-varying marginals (i. e., row and column sums).

1 code implementation • NeurIPS 2020 • Arjun Seshadri, Stephen Ragain, Johan Ugander

Although the foundations of ranking are well established, the ranking literature has primarily been focused on simple, unimodal models, e. g. the Mallows and Plackett-Luce models, that define distributions centered around a single total ordering.

no code implementations • 30 Sep 2023 • Zhaonan Qu, Alfred Galichon, Johan Ugander

For a broad class of choice and ranking models based on Luce's choice axiom, including the Bradley--Terry--Luce and Plackett--Luce models, we show that the associated maximum likelihood estimation problems are equivalent to a classic matrix balancing problem with target row and column sums.

1 code implementation • NeurIPS 2023 • Martin Saveski, Steven Jecmen, Nihar B. Shah, Johan Ugander

We consider estimates of (i) the effect on review quality when changing weights in the assignment algorithm, e. g., weighting reviewers' bids vs. textual similarity (between the review's past papers and the submission), and (ii) the "cost of randomization", capturing the difference in expected quality between the perturbed and unperturbed optimal match.

1 code implementation • 17 May 2021 • Kiran Tomlinson, Johan Ugander, Austin R. Benson

Standard methods in preference learning involve estimating the parameters of discrete choice models from data of selections (choices) made by individuals from a discrete set of alternatives (the choice set).

no code implementations • 20 Jan 2020 • Arjun Seshadri, Johan Ugander

The Multinomial Logit (MNL) model and the axiom it satisfies, the Independence of Irrelevant Alternatives (IIA), are together the most widely used tools of discrete choice.

no code implementations • 8 Feb 2019 • Arjun Seshadri, Alexander Peysakhovich, Johan Ugander

An important class of such contexts is the composition of the choice set.

no code implementations • 5 Oct 2018 • Rahul Makhijani, Johan Ugander

There is a growing need for discrete choice models that account for the complex nature of human choices, escaping traditional behavioral assumptions such as the transitivity of pairwise preferences.

no code implementations • 13 Sep 2018 • Stephen Ragain, Johan Ugander

Ranking data arises in a wide variety of application areas but remains difficult to model, learn from, and predict.

no code implementations • 24 Jul 2018 • Stephen Ragain, Alexander Peysakhovich, Johan Ugander

As such, different models of the comparison process lead to different shrinkage estimators.

no code implementations • 16 May 2017 • Jon Kleinberg, Sendhil Mullainathan, Johan Ugander

In this work we study comparison-based choice functions, a simple but surprisingly rich class of functions capable of exhibiting so-called choice-set effects.

1 code implementation • 1 Aug 2016 • Bailey K. Fosdick, Daniel B. Larremore, Joel Nishimura, Johan Ugander

We place particular emphasis on the importance of specifying the appropriate graph labeling (stub-labeled or vertex-labeled) under which to consider a null model, a choice that closely connects the study of random graphs to the study of random contingency tables.

Methodology Social and Information Networks Data Analysis, Statistics and Probability Physics and Society Quantitative Methods

1 code implementation • NeurIPS 2016 • Stephen Ragain, Johan Ugander

As datasets capturing human choices grow in richness and scale -- particularly in online domains -- there is an increasing need for choice models that escape traditional choice-theoretic axioms such as regularity, stochastic transitivity, and Luce's choice axiom.

no code implementations • NeurIPS 2011 • Martin O. Larsson, Johan Ugander

Latent variable mixture models are a powerful tool for exploring the structure in large datasets.

no code implementations • 19 Nov 2011 • Lars Backstrom, Paolo Boldi, Marco Rosa, Johan Ugander, Sebastiano Vigna

It is not completely clear whether the selected individual is part of the five, so this could actually allude to distance five or six in the language of graph theory, but the "six degrees of separation" phrase stuck after John Guare's 1990 eponymous play.

Social and Information Networks Physics and Society

no code implementations • 18 Nov 2011 • Johan Ugander, Brian Karrer, Lars Backstrom, Cameron Marlow

Second, by studying the average local clustering coefficient and degeneracy of graph neighborhoods, we show that while the Facebook graph as a whole is clearly sparse, the graph neighborhoods of users contain surprisingly dense structure.

Social and Information Networks Physics and Society

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