arXiv subjects

Poonam Sharma

Publications and source records attributed to Poonam Sharma.

13 recordsLinked to original sources

arXiv2026

Tuning electronic properties and Schottky contact in graphene-based van der Waals heterostructures by electric gating and interlayer coupling

Van der Waals heterostructures (vdW HTSs) incorporating graphene (GE) have been an active area of research, both theoretical and experimental, due to their potential to yield devices with a wide variety of applications. In this paper, first-principles calculations are employed to investigate C$_{6}$N$_{6}$/GE, hg-C$_{3}$N$_{4}$/GE, and C$_{6}$N$_{6}$/hg-C$_{3}$N$_{4}$ 2D vdW HTSs. A systematic analysis of structural and thermodynamic stability, electronic, mechanical, and optical properties of semiconductor/metal and semiconductor/semiconductor interfaces is performed. Both semiconductor/metal HTSs form $n$-type Schottky contacts, which can be converted into $p$-type Schottky or Ohmic contacts by tuning the external perpendicular electric field and the interlayer coupling. In the semiconductor/semiconductor C$_{6}$N$_{6}$/hg-C$_{3}$N$_{4}$ HTS, the valence and conduction band edges originate from distinct layers, resulting in a type-II band alignment that promotes efficient electron-hole (e-h) separation. Furthermore, the band alignment can be effectively tuned between type-I and type-II by applying an external electric field and varying the interlayer distance. From the optical absorption spectra of the HTSs, we concluded that the C$_{6}$N$_{6}$/GE and hg-C$_{3}$N$_{4}$/GE exhibit an optical response across a wide frequency range, whereas the C$_{6}$N$_{6}$/hg-C$_{3}$N$_{4}$ HTS shows prominent activity primarily in the ultraviolet region. Using the $G_{0}W_{0}$+BSE approach, the exciton binding energies are also calculated for the gapped systems, namely C$_{6}$N$_{6}$, hg-C$_{3}$N$_{4}$ monolayers, and their HTS (C$_{6}$N$_{6}$/hg-C$_{3}$N$_{4}$), yielding values of 1.01 eV, 1.14 eV, and 1.18 eV, respectively, highlighting strong e-h interactions. Moreover, the band-edge analysis of C$_{6}$N$_{6}$/hg-C$_{3}$N$_{4}$ HTS further favors pronounced interlayer e-h coupling.

cond-mat.mtrl-sci↗

arXiv2026

Fairness-Aware Profit Maximization using Deep Reinforcement Learning

Given a social network represented as a graph where the nodes are the users and the edges represent the social relations, and a positive integer k, how to select k nodes to maximize the influence in the network remains an active area of research. In this paper, we consider a variant of the problem in which network users are associated with two parameters: a benefit value and a cost. A fixed budget is given, and the network is partitioned into communities. The task is to select a subset of users (the seed set) within the budget so that their initial activation maximizes the earned profit, while ensuring that each community realizes at least a minimum fraction of its total benefit under a maximin fairness criterion. For any seed set, the earned benefit is defined as the sum of the benefit values of the users influenced by the seed set, and the profit is defined as the difference between the earned benefit and the total cost. Formally, we call this the Fairness-Aware Profit Maximization Problem. We propose a Deep Reinforcement Learning-based approach for solving it: we first model the problem as a Markov Decision Process and subsequently propose a Deep Q-Learning Algorithm. The proposed solution has been implemented and tested on real-world social network datasets. From the reported results, we observed that the proposed approach yields a seed set whose initial activation produces up to 10 times more profit than the baseline methods. The implementation of our methodology is available at https://github.com/PoonamSharma-PY/DRL_FPM.git.

cs.SI↗

arXiv2026

Profit Maximization in Closed Social Networks

Diffusion of information, innovation, and ideas is an important phenomenon in social networks. Information propagates through the network and reaches from one person to the next. In many settings, it is meaningful to restrict diffusion so that each node can spread information to only a limited number of its neighbors rather than to all of them. Such social networks are called closed social networks. In recent years, social media platforms have emerged as an effective medium for commercial entities, where the objective is to maximize profit. In this paper, we study the Profit Maximization in Closed Social Networks (PMCSN) problem in the context of viral marketing. The input to the problem is a closed social network and two positive integers $\ell$ and $B$. The problem asks to select seed nodes within a given budget $B$; during the diffusion process, each node is restricted to choose at most $\ell$ outgoing links for information diffusion; and the objective is to maximize the profit earned by the seed set. The PMCSN problem generalizes the Influence Maximization problem, which is NP-hard. We propose two solution approaches for PMCSN: a sampling-based approximate solution and a marginal-gain-based heuristic solution. We analyze the sample complexity, running time, and space requirements of the proposed approaches. We conduct experiments on real-world, publicly available social network datasets. The results show that the seed sets and diffusion links chosen by our methods yield higher profit than baseline methods. The implementation and data are available at \texttt{https://github.com/PoonamSharma-PY/ClosedNetwork}.

cs.SI↗

arXiv2026

DeepPM: A Deep Learning-based Profit Maximization Approach in Social Networks

The problem of Profit Maximization asks to choose a limited number of influential users from a given social network such that the initial activation of these users maximizes the profit earned at the end of the diffusion process. This problem has a direct impact on viral marketing in social networks. Over the past decade, several traditional methodologies (i.e., non-learning-based, which include approximate solution, heuristic solution, etc.) have been developed, and many of them produce promising results. All these methods require the information diffusion model as input. However, it may not be realistic to consider any particular diffusion model as real-world diffusion scenarios will be much more complex and need not follow the rules for any particular diffusion model. In this paper, we propose a deep learning-based framework to solve the profit maximization problem. Our model makes a latent representation of the seed sets and is able to learn the diversified information diffusion pattern. We also design a noble objective function that can be optimized effectively using the proposed learning-based approach. The proposed model has been evaluated with the real-world datasets, and the results are reported. We compare the effectiveness of the proposed approach with many existing methods and observe that the seed set chosen by the proposed learning-based approach leads to more profit compared to existing methods. The whole implementation and the simulation code is available at: https://github.com/PoonamSharma-PY/DeepPM.

cs.SI↗

arXiv2026

Profit Maximization for Viral Marketing in Online Social Networks using Two Phase Diffusion Approach

Now-a-days, Online Social Networks (OSNs) are extensively used by different commercial houses for viral marketing. The key problem that arises in this context is to choose a limited number of highly influential users as the initial adopters of a brand such that the influence regarding the brand in the network gets maximized. Deviating from this standard setting, in this paper, we study the problem where every user of the network is associated with a selection cost and a benefit value. This benefit value can be earned from the user if (s)he is influenced by the brand. A fixed amount of budget is allocated for selecting the seed users. The goal of initial adopters is to choose a set of seed users within the budget such that the profit is maximized. We propose a two phase diffusion model for this problem where the goal is to split the diffusion process into two phases, and hence, split the budget into two halves. First, we spend the first half budget to select seed users for the first phase and observe the diffusion for a few rounds and then deploy the seed users for the second phase and successively complete the diffusion process. We prove several properties of the two phase influence function. Three solution approaches have been proposed for our problem with detailed analysis and illustrative examples. We conduct a number of experiments with three real-world social network datasets. From the experiments, we observe that the two phase diffusion approach leads to more amount of profit compared to the single-phase diffusion. In particular, for most instances, this improvement is greater than 18% and reaching as high as 40% by the proposed methodologies.

cs.SI↗

arXiv2025

A Reverse Reachable Set Based Approach for Motif Oriented Profit maximization in Social Networks

Profit Maximization is one of the key objectives for social media marketing, where the task is to choose a limited number of highly influential nodes such that their initial activation leads to maximum profit. In this paper, we introduce a variant of the Profit Maximization Problem where we consider that instead of nodes, benefits are assigned to some of the motifs of the graph, and these benefit values can be earned once a given threshold count of nodes from the motifs is influenced. The goal here is to choose a limited number of nodes for initial activation called seed nodes such that the motif-oriented profit gets maximized. Formally, we call our problem the Motif Oriented Profit Maximization Problem. We show that the problem is NP-hard to solve optimally. We propose a Reverse Reachable Set-based framework to solve our problem. The proposed methodology broadly divides into three steps: KPT Estimation and RR Set generation, Seed Set Selection, and Motif Oriented Profit Estimation. The proposed methodology has been analyzed to understand its time and space requirements. It has been implemented with real-world social network datasets, and the results are reported. We observe that the seed set selected by the proposed solution approaches leads to more profit compared to the seed sets selected by the existing methods. The whole implementation and data are available at: https://github.com/PoonamSharma-PY/MotifProfit.

cs.SI↗

arXiv2025

A Regret-Aware Framework for Effective Social Media Advertising

Social Media Advertisement has emerged as an effective approach for promoting the brands of a commercial house. Hence, many of them have started using this medium to maximize the influence among the users and create a customer base. In recent times, several companies have emerged as Influence Provider who provides views of advertisement content depending on the budget provided by the commercial house. In this process, the influence provider tries to exploit the information diffusion phenomenon of a social network, and a limited number of highly influential users are chosen and activated initially. Due to diffusion phenomenon, the hope is that the advertisement content will reach a large number of people. Now, consider that a group of advertisers is approaching an influence provider with their respective budget and influence demand. Now, for any advertiser, if the influence provider provides more or less influence, it will be a loss for the influence provider. It is an important problem from the point of view of influence provider, as it is important to allocate the seed nodes to the advertisers so that the loss is minimized. In this paper, we study this problem, which we formally referred to as Regret Minimization in Social Media Advertisement Problem. We propose a noble regret model that captures the aggregated loss encountered by the influence provider while allocating the seed nodes. We have shown that this problem is a computationally hard problem to solve. We have proposed three efficient heuristic solutions to solve our problem, analyzed to understand their time and space requirements. They have been implemented with real world social network datasets, and several experiments have been conducted and compared to many baseline methods.

cs.SI↗

arXiv2024

Defect engineering in two-dimensional pentagonal PdTe$_2$: Tuning electronic, optical, and magnetic properties

Recently, the successful synthesis of the pentagonal form of PdTe$_{2}$ monolayer (\emph{p}-PdTe$_{2}$) was reported [Liu~\emph{et al.}, Nature Materials \textbf{23}, 1339 (2024)]. In this work, we present an extensive first-principles density-functional theory (DFT) based computational study of vacancies in this material. Our study covers the evolution of the electronic, optical, and magnetic properties of various defect configurations and compares those to the pristine monolayer (\emph{p}-PdTe$_{2}$). We find that V$_{Pd}$ (V$_{Te}$) is the most stable defect in the~\emph{p}-PdTe$_{2}$ monolayer in the Te-rich (Pd-rich) limit. The defects alter the electronic properties of the monolayer significantly, leading to changes in their magnetic and optical properties due to the emergence of midgap impurity states. The defect complex V$_{Pd+4Te}$ is found to induce spin-polarization in the system with a total magnetic moment of 1.87 $\mu_{B}$. The obtained low diffusion energy barriers of 1.13 eV (in-plane) and 0.063 eV (top-bottom) corresponding to V$_{Te}$ indicates its facile migration probability is higher in the top-bottom direction at room temperature, as revealed by AIMD simulations as well. In order to guide the experimentalists, we also simulated the scanning-tunneling microscope (STM) images corresponding to all the defect configurations. Moreover, we also computed the electron-beam energies required for creating mono-vacancies. In the optical absorption spectra of the defective configurations, finite peaks appear below the band edge that are unique to the respective defective configuration. We have also computed the excess polarizability of the defective configurations with respect to the pristine one and found that maximum changes occur in the infrared and visible regions, providing insights into the change in their optical response as compared to the pristine monolayer.

cond-mat.mtrl-sci↗

arXiv2024

Influence of strain and point defects on the electronic structure and related properties of (111)NiO epitaxial films

(111)NiO epitaxial films are grown on c-sapphire substrates at various growth temperatures ranging from room-temperature to 600C using pulsed laser deposition (PLD) technique. Two series of samples, where different laser fluences are used to ablate the target, are studied here. Films grown with higher laser fluence, are found to be embedded with Ni-clusters crystallographically aligned with the (111)NiO matrix. While the layers grown with lower laser energy density exhibit p-type conductivity specially at low growth temperatures. X-ray diffraction study shows the coexistence of biaxial compressive and tensile hydrostatic strains in these samples, which results in an expansion of the lattice primarily along the growth direction. This effective uniaxial expansion {epsilon}_perpendicular increases with the reduction of the growth temperature. Band gap of these samples is found to decrease linearly with {epsilon}_perpendicular. This result is validated by density functional theory (DFT) calculations. Experimental findings and the theoretical study further indicate that V_Ni + O_I and V_O + Ni_I complexes exist as the dominant native defects in samples grown with Ni-deficient (low laser fluence) and Ni-rich (high laser fluence) conditions, respectively. P-type conductivity observed in the samples grown in Ni-deficient condition is more likely to be resulting from V_Ni + O_I defects than Ni-vacancies (V_Ni).

cond-mat.mtrl-sci↗

arXiv2023

Influence of native defects on magneto-optoelectronic properties of $\alpha$-MoO$_{3}$

Semiconducting oxides possess a variety of intriguing electronic, optical, and magnetic properties, and native defects play a crucial role in these systems. In this study, we study the influence of native defects on these properties of $\alpha$-MoO$_{3}$ using the first-principles density functional theory (DFT) calculations. From the formation energy calculations, it is concluded that Mo vacancies are difficult to form in the system, while O and Mo-O co-vacancies are energetically quite favorable. We further find that vacancies give rise to mid-gap states (trap states) that remarkably affect the magneto-optoelectronic properties of the material. Our calculations indicate that a single Mo vacancy leads to half-metallic behavior, and also induces a large magnetic moment of 5.98 $\mu_{B}$. On the other hand, for the single O vacancy case, the band gap disappears completely, but the system remains in a non-magnetic state. For Mo-O co-vacancies of two types considered in this work, a reduced band gap is found, along with an induced magnetic moment of 2.0 $\mu_{B}$. Furthermore, a few finite peaks below the main band edge are observed in the absorption spectra of configurations with Mo and O vacancies, while they are absent in the Mo-O co-vacancies of both types, just like in the pristine state. From the ab-initio molecular dynamics simulations, stability and sustainability of induced magnetic moment at room temperate is verified. Our findings will enable the development of defect strategies that maximize the functionality of the system, and further help in designing highly efficient magneto-optoelectronic and spintronic devices.

cond-mat.mtrl-sci↗

arXiv2023

Room Temperature d$^0$ Ferromagnetism in Carbon Doped LaH$_3$: Insights From Density Functional Theory Simulations

Employing the state-of-the-art Density Functional Theory with both GGA and hybrid HSE06 functional along with the incorporation of spin-orbit coupling, we have engineered stable room temperature ferromagnetism in non-magnetic LaH$_3$ through C substitution at octahedral and tetrahedral H sites where the induced magnetic moment is mostly contributed by 2p orbital of C atom. It is interesting that the magnetic signature is switched on with an impurity concentration as low as 1.04 at% with a magnetic moment of $\approx$ 1.0 $\mu_B$ per impurity, where the localized behavior of the 2p states of C along with significant exchange splitting energy can be attributed as the origin of the induced magnetic moment. The verification of the Stoner criterion in the material further confirmed the onset of ferromagnetism in the system, and the computed Curie temperature is found to be well above room temperature. Reduced formation energy and requirement of lower impurity concentration ensure practical feasibility towards a spintronic device where room temperature ferromagnetism is established from the non-magnetic host and the dopant.

cond-mat.mtrl-sci↗

arXiv2022

Profit Maximization using Social Networks in Two-Phase Setting

Now-a-days, \emph{Online Social Networks} have been predominantly used by commercial houses for viral marketing where the goal is to maximize profit. In this paper, we study the problem of Profit Maximization in the two\mbox{-}phase setting. The input to the problem is a \emph{social network} where the users are associated with a cost and benefit value, and a fixed amount of budget splitted into two parts. Here, the cost and the benefit associated with a node signify its incentive demand and the amount of benefit that can be earned by influencing that user, respectively. The goal of this problem is to find out the optimal seed sets for both phases such that the aggregated profit at the end of the diffusion process is maximized. First, we develop a mathematical model based on the \emph{Independent Cascade Model} of diffusion that captures the aggregated profit in an \emph{expected} sense. Subsequently, we show that selecting an optimal seed set for the first phase even considering the optimal seed set for the second phase can be selected efficiently, is an $\textsf{NP}$-Hard Problem. Next, we propose two solution methodologies, namely the \emph{single greedy} and the \emph{double greedy} approach for our problem that works based on marginal gain computation. A detailed analysis of both methodologies has been done to understand their time and space requirements. We perform an extensive set of experiments to demonstrate the effectiveness and efficiency of the proposed approaches with real-world datasets. From the experiments, we observe that the proposed solution approaches lead to more profit compared to the baseline methods and in particular, the double greedy approach leads to up to $5 \%$ improvement compared to its single\mbox{-}phase counterpart.

cs.DB↗

arXiv2011

The factorisation of glue and mass terms in SU(N) gauge theories

In this paper we investigate the structure of the glue in Zwanziger's gauge invariant expansion for the A^2-type mass term in Yang-Mills theory. We show how to derive this expansion, in terms of the inverse covariant Laplacian, and extend it to higher orders. In particular, we give an explicit expression, for the first time, for the next to next to leading order term. We further show that the expansion is not unique and give examples of the resulting ambiguity.

hep-th↗