Search Results for author: Francesco Bonchi

Found 25 papers, 11 papers with code

Query-Efficient Correlation Clustering with Noisy Oracle

no code implementations2 Feb 2024 Yuko Kuroki, Atsushi Miyauchi, Francesco Bonchi, Wei Chen

We study a general clustering setting in which we have $n$ elements to be clustered, and we aim to perform as few queries as possible to an oracle that returns a noisy sample of the similarity between two elements.

Clustering Multi-Armed Bandits

Fairness in Algorithmic Recourse Through the Lens of Substantive Equality of Opportunity

no code implementations29 Jan 2024 Andrew Bell, Joao Fonseca, Carlo Abrate, Francesco Bonchi, Julia Stoyanovich

Building upon an agent-based framework for simulating recourse, this paper demonstrates how much effort is needed to overcome disparities in initial circumstances.

Decision Making Fairness

Learning Multi-Frequency Partial Correlation Graphs

1 code implementation27 Nov 2023 Gabriele D'Acunto, Paolo Di Lorenzo, Francesco Bonchi, Stefania Sardellitti, Sergio Barbarossa

Despite the large research effort devoted to learning dependencies between time series, the state of the art still faces a major limitation: existing methods learn partial correlations but fail to discriminate across distinct frequency bands.

Time Series

Extracting the Multiscale Causal Backbone of Brain Dynamics

1 code implementation31 Oct 2023 Gabriele D'Acunto, Francesco Bonchi, Gianmarco De Francisci Morales, Giovanni Petri

The bulk of the research effort on brain connectivity revolves around statistical associations among brain regions, which do not directly relate to the causal mechanisms governing brain dynamics.

Setting the Right Expectations: Algorithmic Recourse Over Time

no code implementations13 Sep 2023 Joao Fonseca, Andrew Bell, Carlo Abrate, Francesco Bonchi, Julia Stoyanovich

The bulk of the literature on algorithmic recourse to-date focuses primarily on how to provide recourse to a single individual, overlooking a critical element: the effects of a continuously changing context.

Decision Making

Rebalancing Social Feed to Minimize Polarization and Disagreement

1 code implementation28 Aug 2023 Federico Cinus, Aristides Gionis, Francesco Bonchi

Social media have great potential for enabling public discourse on important societal issues.

Counterfactual Explanations for Graph Classification Through the Lenses of Density

1 code implementation27 Jul 2023 Carlo Abrate, Giulia Preti, Francesco Bonchi

Counterfactual examples have emerged as an effective approach to produce simple and understandable post-hoc explanations.

counterfactual Counterfactual Explanation +1

Fast and Effective GNN Training with Linearized Random Spanning Trees

no code implementations7 Jun 2023 Francesco Bonchi, Claudio Gentile, Francesco Paolo Nerini, André Panisson, Fabio Vitale

We present a new effective and scalable framework for training GNNs in node classification tasks, based on the effective resistance, a powerful tool solidly rooted in graph theory.

Node Classification

A Survey on the Densest Subgraph Problem and its Variants

no code implementations25 Mar 2023 Tommaso Lanciano, Atsushi Miyauchi, Adriano Fazzone, Francesco Bonchi

This survey provides a deep overview of the fundamental results and an exhaustive coverage of the many variants proposed in the literature, with a special attention on the most recent results.

Balancing Utility and Fairness in Submodular Maximization (Technical Report)

1 code implementation2 Nov 2022 Yanhao Wang, Yuchen Li, Francesco Bonchi, Ying Wang

Submodular function maximization is a fundamental combinatorial optimization problem with plenty of applications -- including data summarization, influence maximization, and recommendation.

Combinatorial Optimization Data Summarization +1

Learning Multiscale Non-stationary Causal Structures

no code implementations31 Aug 2022 Gabriele D'Acunto, Gianmarco De Francisci Morales, Paolo Bajardi, Francesco Bonchi

Our model allows sampling an MN-DAG according to user-specified priors on the time-dependence and multiscale properties of the causal graph.

Time Series Time Series Analysis +1

Cascade-based Echo Chamber Detection

1 code implementation9 Aug 2022 Marco Minici, Federico Cinus, Corrado Monti, Francesco Bonchi, Giuseppe Manco

Experiments on synthetic data show that our algorithm is able to correctly reconstruct ground-truth latent communities with their degree of echo-chamber behavior and opinion polarity.

Stance Detection

On learning agent-based models from data

no code implementations10 May 2022 Corrado Monti, Marco Pangallo, Gianmarco De Francisci Morales, Francesco Bonchi

In this paper, we propose a protocol to learn the latent micro-variables of an ABM from data.

GRAPHSHAP: Explaining Identity-Aware Graph Classifiers Through the Language of Motifs

no code implementations17 Feb 2022 Alan Perotti, Paolo Bajardi, Francesco Bonchi, André Panisson

Decoupling the feature space (edges) from a desired high-level explanation language (such as motifs) is thus a major challenge towards developing actionable explanations for graph classification tasks.

Computational Efficiency Graph Classification +1

Rewiring What-to-Watch-Next Recommendations to Reduce Radicalization Pathways

1 code implementation1 Feb 2022 Francesco Fabbri, Yanhao Wang, Francesco Bonchi, Carlos Castillo, Michael Mathioudakis

Hence, we define the problem of reducing the prevalence of radicalization pathways by selecting a small number of edges to "rewire", so to minimize the maximum of segregation scores among all radicalized nodes, while maintaining the relevance of the recommendations.

Recommendation Systems

The Evolving Causal Structure of Equity Risk Factors

no code implementations9 Nov 2021 Gabriele D'Acunto, Paolo Bajardi, Francesco Bonchi, Gianmarco De Francisci Morales

They link the evolution of the causal structure of equity risk factors with market volatility and a worsening macroeconomic environment, and show that, in times of financial crisis, exposure to different factors boils down to exposure to the market risk factor.

Management

Learning Ideological Embeddings from Information Cascades

1 code implementation28 Sep 2021 Corrado Monti, Giuseppe Manco, Cigdem Aslay, Francesco Bonchi

Modeling information cascades in a social network through the lenses of the ideological leaning of its users can help understanding phenomena such as misinformation propagation and confirmation bias, and devising techniques for mitigating their toxic effects.

Misinformation

Counterfactual Graphs for Explainable Classification of Brain Networks

1 code implementation16 Jun 2021 Carlo Abrate, Francesco Bonchi

In this paper we propose \emph{counterfactual graphs} as a way to produce local post-hoc explanations of any black-box graph classifier.

Classification counterfactual

Maxmin-Fair Ranking: Individual Fairness under Group-Fairness Constraints

no code implementations16 Jun 2021 David Garcia-Soriano, Francesco Bonchi

Our proposal is rooted in the distributional maxmin fairness theory, which uses randomization to maximize the expected satisfaction of the worst-off individuals.

Fairness

Learning Opinion Dynamics From Social Traces

1 code implementation2 Jun 2020 Corrado Monti, Gianmarco De Francisci Morales, Francesco Bonchi

In this work we propose an inference mechanism for fitting a generative, agent-like model of opinion dynamics to real-world social traces.

Graph Mining Link Sign Prediction +3

Query-Efficient Correlation Clustering

no code implementations26 Feb 2020 David García-Soriano, Konstantin Kutzkov, Francesco Bonchi, Charalampos Tsourakakis

Up to constant factors, our algorithm yields a provably optimal trade-off between the number of queries $Q$ and the worst-case error attained, even for adaptive algorithms.

Clustering

Probabilistic Causal Analysis of Social Influence

no code implementations6 Aug 2018 Francesco Bonchi, Francesco Gullo, Bud Mishra, Daniele Ramazzotti

Experiments on synthetic data show that our method is able to retrieve the genuine causal arcs w. r. t.

FA*IR: A Fair Top-k Ranking Algorithm

2 code implementations20 Jun 2017 Meike Zehlike, Francesco Bonchi, Carlos Castillo, Sara Hajian, Mohamed Megahed, Ricardo Baeza-Yates

In this work, we define and solve the Fair Top-k Ranking problem, in which we want to determine a subset of k candidates from a large pool of n >> k candidates, maximizing utility (i. e., select the "best" candidates) subject to group fairness criteria.

Fairness

Exposing the Probabilistic Causal Structure of Discrimination

no code implementations2 Oct 2015 Francesco Bonchi, Sara Hajian, Bud Mishra, Daniele Ramazzotti

Discrimination discovery from data is an important task aiming at identifying patterns of illegal and unethical discriminatory activities against protected-by-law groups, e. g., ethnic minorities.

valid

A Data-Based Approach to Social Influence Maximization

no code implementations30 Sep 2011 Amit Goyal, Francesco Bonchi, Laks. V. S. Lakshmanan

In particular, we introduce a new model, which we call credit distribution, that directly leverages available propagation traces to learn how influence flows in the network and uses this to estimate expected influence spread.

Databases

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