arXiv subjects

Subhankar Bhadra

Publications and source records attributed to Subhankar Bhadra.

6 recordsLinked to original sources

arXiv2025

Causal Inference Under Network Interference

We review recent advances in causal inference under interference, drawing on a complex and diverse body of work ranging from causal inference, network science, the health sciences, economics, and the social sciences. Interference in connected populations implies that the treatment assignments of units can affect the outcomes of other units directly (via spillover) and indirectly (via contagion). Examples include public health interventions, economic and financial interventions, and advertising on social media. We review tests for detecting interference, causal effects based on fixed and random potential outcomes, identification of causal effects, and design- and model-based estimators of causal effects based on experimental and observational data. We then discuss the scope of causal conclusions based on fixed and random potential outcomes and interference graphs. Using simulations, we demonstrate that conditioning on interference graphs limits causal conclusions when the variability across interference graphs is high. We conclude with a selection of open problems.

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arXiv2025

A Unified Framework for Community Detection and Model Selection in Blockmodels

Blockmodels are a foundational tool for modeling community structure in networks, with the stochastic blockmodel (SBM), degree-corrected blockmodel (DCBM), and popularity-adjusted blockmodel (PABM) forming a natural hierarchy of increasing generality. While community detection under these models has been extensively studied, much less attention has been paid to the model selection problem, i.e., determining which model best fits a given network. Building on recent theoretical insights about the spectral geometry of these models, we propose a unified framework for simultaneous community detection and model selection across the full blockmodel hierarchy. A key innovation is the use of loss functions that serve a dual role: they act as objective functions for community detection and as test statistics for hypothesis testing. We develop a greedy algorithm to minimize these loss functions and establish theoretical guarantees for exact label recovery and model selection consistency under each model. Extensive simulation studies demonstrate that our method achieves high accuracy in both tasks, outperforming or matching state-of-the-art alternatives. Applications to five real-world networks further illustrate the interpretability and practical utility of our approach. R code for implementing the method is available at https://github.com/subhankarbhadra/model-selection.

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arXiv2025

Causal inference in connected populations with contagion

We address a gap in the literature on causal inference in connected populations: while there is a growing body of work on estimating causal effects in connected populations, little is known about how contagion and other network processes impact causal effects and inference. Contagion and other network processes imply that the outcomes of units affect the outcomes of other units, which enables the effects of interventions to propagate throughout connected populations and complicates insight into causal effects and inference. We offer novel insight into how contagion and other network processes impact causal effects and inference based on closed-form expressions for causal effects under spillover and contagion. These closed-form expressions reveal that the main effects of interventions, spillover, and contagion are intertwined even in the simplest possible settings, and that contagion can decrease or increase causal effects. We discuss statistical implications, including asymptotic bias of model-based estimators ignoring contagion, the violation of neighborhood exposure assumptions by unrestricted contagion and its effect on design-based estimators, and possible remedies.

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arXiv2025

Scalable community detection in massive networks via predictive assignment

Massive network datasets are becoming increasingly common in scientific applications. Existing community detection methods encounter significant computational challenges for such massive networks due to two reasons. First, the full network needs to be stored and analyzed on a single server, leading to high memory costs. Second, existing methods typically use matrix factorization or iterative optimization using the full network, resulting in high runtimes. We propose a strategy called \textit{predictive assignment} to enable computationally efficient community detection while ensuring statistical accuracy. The core idea is to avoid large-scale matrix computations by breaking up the task into a smaller matrix computation plus a large number of vector computations that can be carried out in parallel. Under the proposed method, community detection is carried out on a small subgraph to estimate the relevant model parameters. Next, each remaining node is assigned to a community based on these estimates. We prove that predictive assignment achieves strong consistency under the stochastic blockmodel and its degree-corrected version. We also demonstrate the empirical performance of predictive assignment on simulated networks and two large real-world datasets: DBLP (Digital Bibliography \& Library Project), a computer science bibliographical database, and the Twitch Gamers Social Network.

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arXiv2024

A regression framework for studying relationships among attributes under network interference

To understand how the interconnected and interdependent world of the twenty-first century operates and make model-based predictions, joint probability models for networks and interdependent outcomes are needed. We propose a comprehensive regression framework for networks and interdependent outcomes with multiple advantages, including interpretability, scalability, and provable theoretical guarantees. The regression framework can be used for studying relationships among attributes of connected units and captures complex dependencies among connections and attributes, while retaining the virtues of linear regression, logistic regression, and other regression models by being interpretable and widely applicable. On the computational side, we show that the regression framework is amenable to scalable statistical computing based on convex optimization of pseudo-likelihoods using minorization-maximization methods. On the theoretical side, we establish convergence rates for pseudo-likelihood estimators based on a single observation of dependent connections and attributes. We demonstrate the regression framework using simulations and an application to hate speech on the social media platform X.

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arXiv2018

Detecting and Localizing Anomalous Cliques in Inhomogeneous Networks using Egonets

Cliques, or fully connected subgraphs, are among the most important and well-studied graph motifs in network science. We consider the problem of finding a statisti- cally anomalous clique hidden in a large network. There are two parts to this problem: (1) detection, i.e., determining whether an anomalous clique is present, and (2) localization, i.e., determining which vertices of the network constitute the detected clique. While this problem has been extensively studied under the homogeneous Erdos-Renyi model, little progress has been made beyond this simple setting, and no existing method can perform detection and localization in inhomogeneous networks within finite time. To address this gap, we first show that in homogeneous networks, the anomalousness of a clique depends solely on its size. This property does not carry over to inhomogeneous networks, where the identity of the vertices forming the clique plays a critical role, and a smaller clique can be more anomalous than a larger one. Building on this insight, we propose a unified method for clique detection and localization based on a class of subgraphs called egonets. The proposed method generalizes to a wide variety of inhomogeneous network models and is naturally amenable to parallel computing. We establish the theoretical properties of the proposed method and demonstrate its empirical performance through simulation studies and application to two real world networks.

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