arXiv subjects

Niteesh Sahni

Publications and source records attributed to Niteesh Sahni.

16 recordsLinked to original sources

arXiv2026

Synergies, Trade-offs, and Structural Pathways: A Directed Network Approach to SDG Prioritisation

To successfully implement the Sustainable Development Goals (SDGs), it is necessary to understand the process by which the achievement of one goal has a spillover effect in a development system. While existing research studies synergies and trade-offs among the SDGs, most empirical approaches operate at the goal level, treat interactions as undirected, or prioritise indicators without accounting for structural redundancy. In this paper, we propose a direction-sensitive and indicator-level network approach to detect high-impact and diversified entry points for policy intervention. By using statistically significant lagged correlations, we build a directed weighted network of SDG indicators and assign them into groups based on the balance of their positive and negative spillovers. Systemic effects are measured by weighted out-degree Opsahl centrality, and flow-based clustering is used to detect frequent paths of high positive spillovers. Applying the framework to the Indian context, it is found that the interlinkages in the SDGs are highly asymmetric and structured in specific structural subsystems. Although synergies slightly outweigh the total, trade-offs are still embedded in the sectors. Notably, the most influential indicators are focused on a single pathway of propagation, suggesting that influence-based prioritisation itself could result in redundant system-wide impacts. A cluster-based prioritisation approach leads to a more diversified set of interventions, triggering multiple structurally independent channels of beneficial spillovers. The proposed framework combines directionality, trade-off embedding, and structural propagation analysis in a single framework, providing a scalable solution for country-level SDG prioritisation under resource constraints.

math.DS↗

arXiv2026

A Network-Based Framework to Identify Synergies and Trade offs among SDG Indicators

Achieving the United Nations Sustainable Development Goals (SDGs) requires an understanding of the complex interlinkages that exist among their underlying indicators. While most existing research examines these interconnections at the goal level, policy interventions are typically designed and implemented at the indicator level, where synergies and trade-offs most directly emerge. This study addresses this gap by proposing a network-theoretic framework to assess indicator-level interactions in a systematic and data-driven manner. We introduce two complementary measures, the positive strength and negative strength of an indicator, which jointly capture the balance between synergistic and conflicting interactions within a national SDG indicator network. Based on these measures, indicators are classified as synergy- and trade-off-dominated according to their net systemic interaction structure. To move beyond classification, we further examine the structural drivers of synergy dominance using an explanatory regression framework, focusing on the roles of direct positive interactions and indirect network embeddedness. This analysis shows that indicators classified as synergy-dominated are typically characterized by a high concentration of direct synergies and additional support from indirect pathways through the network, allowing positive effects to extend beyond immediate neighbors. The framework is applied to two national case studies, India and Italy, to illustrate how the classification of indicators varies across development contexts. Overall, the proposed methodology provides a transparent and scalable tool for identifying the structural conditions under which indicator-level synergies emerge, thereby supporting a more nuanced understanding of how development actions can generate reinforcing effects across the SDG system.

math.DS↗

arXiv2025

A generic network theoretic based model to classify SDG indicators

To achieve the United Nations Sustainable Development Goals, coordinated action across their interlinked indicators is required. Although most of the research on the interlinkages of the SDGs is done at the goal level, policies are usually made and implemented at the level of indicators (or targets). Our study examines the existing literature on SDG interlinkages and indicator (or target) prioritization, highlighting important drawbacks of current methodologies. To address these limitations, we propose a generic network-based model that can quantify the importance of the SDG indicators and help policymakers in identifying indicators for maximum synergistic impact. Our model applies to any country, offering a tool for national policymakers. We illustrate the application of this model using data from India, identifying important indicators that are crucial for accelerating progress in the SDGs. While our main contribution lies in developing this network-theoretic methodology, we also provide supporting empirical evidence from existing literature for selected key observations.

stat.AP↗

arXiv2025

Structural Characterisations of (n-1,n)-Trees

We study higher-dimensional analogues of graph-theoretic trees within the class of pure n-simplicial complexes. Focusing on the case m = n-1 in Dewdney's (m, n)-tree framework, we introduce refined notions of path and circuit sequences that overcome the structural limitations of existing definitions. Using these refinements, we establish higher-dimensional analogues of the classical characterisations of trees in graphs, including equivalences based on connectivity, acyclicity, path uniqueness, and enumerative constraints. We further disprove two conjectures posed by Dewdney by constructing explicit counterexamples, and we formulate corrected versions that hold under additional necessary conditions in the case m = n-1. These results provide a structural characterisation of (n-1, n)-trees, parallel to the classical theory of graph-theoretic trees.

math.CO↗

arXiv2025

Analyzing Communicability and Connectivity in the Indian Stock Market During Crises

Understanding how information flows through the financial networks is important, especially during times of market turbulence. Unlike traditional assumptions where information travels along the shortest paths, real-world diffusion processes often follow multiple routes. To capture this complexity, we apply communicability, a network measure that quantifies the ease of information flow between nodes, even beyond the shortest path. In this study, we aim to examine how communicability responds to structural disruptions in financial networks during periods of high volatility. We compute communicability-based metrics on correlation-derived networks constructed from financial market data, and apply statistical testing through permutation methods to identify significant shifts in network structure. Our results show that approximately 70\% and 80\% of stock pairs exhibit statistically significant changes in communicability during the global financial crisis and the unprecedented COVID-19 crisis, respectively, at a significance level of 0.001. The observed shifts in shortest communicability path lengths offer directional cues about the nature and depth of each crisis. Furthermore, when used as features in machine learning classification models, communicability measures outperform the shortest-path-based measures in distinguishing between market stability and volatility periods. The performance of geometric measures was also comparable to that of topology-based measures. These findings offer valuable insights into the dynamic behavior of financial markets during times of crises and underscore the practical relevance of communicability in modeling systemic risk and information diffusion in complex networks.

q-fin.ST↗

arXiv2024

Identifying Core-Periphery Structures in Networks via Artificial Ants

Core periphery structure represents a meso-scale structure in networks, characterized by a dense interconnection of core nodes and sparse connections among peripheral nodes. In this paper, we introduce an innovative approach for detecting core periphery structure, leveraging Artificial Ants. Core-periphery structures play a crucial role in elucidating network organization across various domains. The proposed approach, inspired by the foraging behavior of ants, employs artificial pheromone trails to iteratively construct and refine solutions, thereby eliminating the need for arbitrary partitions that often constrain traditional methods. Our method is applied to a diverse selection of real world networks including historical, literary, linguistic, sports, and animal social networks highlighting its adaptability and robustness. We systematically compare the performance of our approach against established core-periphery detection techniques, emphasizing differences in node classification between the core and periphery. Experimental results show that our method achieves superior flexibility and precision, offering marked improvements in the accuracy of core periphery structure detection.

physics.soc-ph↗

arXiv2024

Exploring the core-periphery and community structure in the financial networks through random matrix theory

In finance, Random Matrix Theory (RMT) is an important tool for filtering out noise from large datasets, revealing true correlations among stocks, enhancing risk management and portfolio optimization. In this study, we use RMT to filter out noise from the full cross-correlation matrix of stock price returns for the NIFTY 200 and NIFTY 500 indices on the National Stock Exchange of India. In addition, we applied network theory tools to analyze market and sector modes as filtered correlation structures to study local interactions within financial networks. This allows us to study the very fundamental properties of networks, such as the core-periphery and the community structure of constructed networks over these filtered modes, and compare the results with the network constructed over the full cross-correlation matrix. The results suggest that the core-periphery structure is contained in the market mode, while the community structure is in the sector mode. Thus, both modes outperform the full cross-correlation in terms of capturing the essential respective structure of the network. Furthermore, we used these insights to build portfolios based on communities of the networks corresponding to the sector mode and the network corresponding to the full cross-correlation matrix. The results suggest that the portfolio constructed on the complete cross-correlation-based matrix performs better than the sector mode. These insights provide a greater understanding of RMT application in the financial market.

cs.SI↗

arXiv2024

Uncovering the hidden core-periphery structure in hyperbolic networks

The hyperbolic network models exhibit very fundamental and essential features, like small-worldness, scale-freeness, high-clustering coefficient, and community structure. In this paper, we comprehensively explore the presence of an important feature, the core-periphery structure, in the hyperbolic network models, which is often exhibited by real-world networks. We focused on well-known hyperbolic models such as popularity-similarity optimization model (PSO) and S1/H2 models and studied core-periphery structures using a well-established method that is based on standard random walk Markov chain model. The observed core-periphery centralization values indicate that the core-periphery structure can be very pronounced under certain conditions. We also validate our findings by statistically testing for the significance of the observed core-periphery structure in the network geometry. This study extends network science and reveals core-periphery insights applicable to various domains, enhancing network performance and resiliency in transportation and information systems.

physics.soc-ph↗

arXiv2024

A novel portfolio construction strategy based on the core-periphery profile of stocks

This paper highlights the significance of mesoscale structures, particularly the core-periphery structure, in financial networks for portfolio optimization. We build portfolios of stocks belonging to the periphery part of the Planar maximally filtered subgraphs of the underlying network of stocks created from Pearson correlations between pairs of stocks and compare its performance with some well-known strategies of Pozzi et. al. hinging around the local indices of centrality in terms of the Sharpe ratio, returns and standard deviation. Our findings reveal that these portfolios consistently outperform traditional strategies and further the core-periphery profile obtained is statistically significant across time periods. These empirical findings substantiate the efficacy of using the core-periphery profile of the stock market network for both inter-day and intraday trading and provide valuable insights for investors seeking better returns.

q-fin.ST↗

arXiv2024

Exploiting the geometry of heterogeneous networks: A case study of the Indian stock market

In this study, we model the Indian stock market as heterogenous scale free network, which is then embedded in a two dimensional hyperbolic space through a machine learning based technique called as coalescent embedding. This allows us to apply the hyperbolic kmeans algorithm on the Poincare disc and the clusters so obtained resemble the original network communities more closely than the clusters obtained via Euclidean kmeans on the basis of well-known measures normalised mutual information and adjusted mutual information. Through this, we are able to clearly distinguish between periods of market stability and volatility by applying non-parametric statistical tests with a significance level of 0.05 to geometric measures namely hyperbolic distance and hyperbolic shortest path distance. After that, we are able to spot significant market change early by leveraging the Bollinger Band analysis on the time series of modularity in the embedded networks of each window. Finally, the radial distance and the Equidistance Angular coordinates help in visualizing the embedded network in the Poincare disc and it is seen that specific market sectors cluster together.

physics.soc-ph↗

arXiv2023

Helson-Lowdenslager and de Branges type theorems in the setting of continuous rotationally symmetric norms

A Helson-Lowdenslager type result has been proved by Chen in the context of Lebesgue spaces of the unit circle equipped with a continuous rotationally symmetric norm by studying the simply invariant subspaces of the operator of multiplication by the coordinate function $z$. In this paper, we generalize Chen's result by obtaining a description of simply invariant subspaces for multiplication by $z^n$. A de Branges type result is also proved for Hardy spaces equipped with continuous rotationally symmetric norms.

math.FA↗

arXiv2022

Multiplication by finite Blaschke factors on a general class of Hardy spaces

A broader class of Hardy spaces and Lebesgue spaces have been introduced recently on the unit circle by considering continuous $\|.\|_1$-dominating normalized gauge norms instead of the classical norms on measurable functions and a Beurling type result has been proved for the operator of multiplication by the coordinate function. In this paper, we generalize the above Beurling type result to the context of multiplication by a finite Blaschke factor $B(z)$ and also derive the common invariant subspaces of $B^2(z)$ and $B^3(z)$. These results lead to a factorization result for all functions in the Hardy space equipped with a continuous rotationally symmetric norm.

math.FA↗

arXiv2020

Network Learning Approaches to study World Happiness

The United Nations in its 2011 resolution declared the pursuit of happiness a fundamental human goal and proposed public and economic policies centered around happiness. In this paper we used 2 types of computational strategies viz. Predictive Modelling and Bayesian Networks (BNs) to model the processed historical happiness index data of 156 nations published by UN since 2012. We attacked the problem of prediction using General Regression Neural Networks (GRNNs) and show that it out performs other state of the art predictive models. To understand causal links amongst key features that have been proven to have a significant impact on world happiness, we first used a manual discretization scheme to discretize continuous variables into 3 levels viz. Low, Medium and High. A consensus World Happiness BN structure was then fixed after amalgamating information by learning 10000 different BNs using bootstrapping. Lastly, exact inference through conditional probability queries was used on this BN to unravel interesting relationships among the important features affecting happiness which would be useful in policy making.

cs.CY↗

arXiv2016

Invariance Under Bounded Analytic Functions

In a recent paper, M. Raghupathi has extended the famous theorem of Beurling to the context of subspaces that are invariant under the class of subalgebras of $H^\infty$ of the form $IH^\infty$, where $I$ is an inner function. In this paper, we provide analouges of the above mentioned $IH^\infty$ related extension of Beurling's theorem to the context of uniform algebras, on compact abelian groups with ordered duals, the Lebesgue space on the real line and in the setting of the space $BMOA$. We also provide a significant simplification of the proof of the Beurling's theorem in the setting of uniform algebras and a new proof of the Helson-Lowdenslager theorem that generalizes Beurling's theorem in the context of compact abelian groups with ordered duals.

math.FA↗

arXiv2013

Sub-Hardy Hilbert spaces on the circle and torus

Sahni and Singh settled a problem posed by Yousefi & Hesameddini by generalizing their main result with a simple proof in the setting of sub-Hilbert spaces in $H^2(T)$. In this paper, we extend the main result of Sahni and Singh to the setting of the Banach spaces $H^p$, $1\le p\le\infty$ on the circle and the torus.

math.FA↗

arXiv2012

Lax-Halmos Type Theorems in H^p Spaces

In this paper we characterize for 0 < p \leq \infty, the closed subspaces of Hp that are invariant under multiplication by all powers of a finite Blaschke factor B, except the first power. Our result clearly generalizes the invariant subspace theorem obtained by Paulsen and Singh [9] which has proved to be the starting point of important work on constrained Nevanlinna-Pick interpolation. Our method of proof can also be readily adapted to the case where the subspace is invariant under all positive powers of B (z). The two results are in the mould of the classical Lax-Halmos Theorem and can be said to be Lax-Halmos type results in the finitre multiplicity case for two commuting shifts and for a single shift respectively.

math.FA↗