arXiv subjects

Akrati Saxena

Publications and source records attributed to Akrati Saxena.

At least 19 recordsLinked to original sources

arXiv2026

LLMs for Social Network Modeling: From Network Generation to Dynamic Processes

Large language models (LLMs) are rapidly emerging as a new paradigm for modeling social networks by representing users and their relationships and interactions through natural language. Unlike classical network models or deep learning approaches, LLMs can simulate context-aware social behavior and language-driven interactions, enabling more realistic modeling of network formation and dynamic social processes. However, existing studies are scattered across different research communities and lack a unified perspective. This survey presents the first comprehensive review of LLMs for social network modeling by organizing the literature into two broad categories: network generative models and dynamic process models. Network generative models are further classified into selection-based and interaction-based approaches, while dynamic process models are categorized into opinion dynamics, information diffusion, and rumor propagation, each with their underlying modeling mechanisms. LLMs enable rich textual social interactions and decision-making, but they also exhibit many limitations, including inherent social biases and prompt sensitivity. We outline these open research challenges and discuss future directions in LLM-based social network modeling.

cs.SI↗

arXiv2026

Fairness-Aware Network Embeddings: Methods, Applications, and Challenges

Network embedding methods learn low-dimensional representations of graph-structured data to support downstream tasks such as node classification, link prediction, and influence maximization. However, real-world networks often reflect structural inequalities arising from demographic imbalances, homophily, and other societal biases, which fairness-agnostic embedding methods can encode and amplify. To address this issue, numerous fairness-aware network embedding methods have been proposed to mitigate bias while preserving embedding utility. This survey presents a comprehensive overview of fairness-aware network embeddings for complex networks. We propose a taxonomy that categorizes existing methods along three main complementary dimensions: underlying embedding approach (spectral, random walk, graph neural network, Bayesian, and method-agnostic), fairness intervention strategy (pre-processing, in-processing, and post-processing), and fairness objective criterion (embedding- or task-level). We further compare methods with respect to group versus individual fairness and assumptions regarding sensitive attributes. Finally, we discuss current limitations and highlight promising future research directions. This survey provides a unified perspective on fairness-aware network embedding and serves as a reference for developing fair and trustworthy network representation learning methods.

cs.SI↗

arXiv2026

BetXplain: An Explanation-Annotated Dataset for Detecting Manipulative Betting Advertisements on Social Media

The promotion of betting applications on social media platforms has increased significantly in recent years. Many of these advertisements use persuasive techniques that may mislead users, encourage risky behavior, and potentially influence users' mental well-being. However, research on the automated detection of manipulative and deceptive betting advertisements remains limited due to the lack of publicly available annotated datasets. In this work, we introduce a new dataset of betting-related advertisements collected from two widely used social media platforms, Instagram and Reddit. The advertisements were manually annotated for manipulative and deceptive advertising practices. In addition to classification labels, the dataset includes human-provided explanations that describe the reasoning behind each annotation, enabling research into explainable approaches to detecting manipulative advertising. Furthermore, we analyze the strategies commonly used in betting advertisements and examine how these persuasive tactics may impact users' mental health. The proposed framework can also enable practical applications such as browser plugins that warn users about manipulative betting advertisements and automated web crawlers that help regulatory authorities monitor and detect such promotions online.

cs.LG↗

arXiv2026

Persistent geographical biases in global scientific collaboration and citations

Scientific knowledge flows enable cumulative progress by connecting researchers across disciplines, institutions, and countries. Yet it remains unclear how geography and national structures continue to shape these exchanges in an increasingly connected world. Using a large-scale bibliometric dataset from OpenAlex, which covers 39.35 million publications across 95 countries and 3,794 cities between 2000 and 2022, we examine global knowledge diffusion through two complementary channels: co-authorship and citation. We find that the constraining effect of geographic distance on collaboration has not diminished over time but has instead intensified, suggesting persistent structural or institutional barriers. Citation flows, by contrast, are less sensitive to spatial proximity, indicating that intellectual influence may diffuse more freely across borders. At the country level, research networks exhibit strong domestic preferences and a shared citation orientation toward the United States. China, while increasingly favored as a collaboration partner by other countries, continues to be systematically undercited within global citation flows. International mobility increases researchers' collaboration with scholars in their host country but has limited effects on citation flows. These results highlight the structural persistence of spatial and country biases in global science, with implications for equitable participation and recognition across regions.

econ.GN↗

arXiv2026

Individual Fairness in Community Detection: Quantitative Measure and Comparative Evaluation

Community detection is a fundamental task in complex network analysis. Fairness-aware community detection seeks to prevent biased node partitions, typically framed in terms of individual fairness, which requires similar nodes to be treated similarly, and group fairness, which aims to avoid disadvantaging specific groups of nodes. While existing literature on fair community detection has primarily focused on group fairness, we introduce a novel measure to quantify individual fairness in community detection methods. The proposed measure captures unfairness as the vectorial distance between a node's true and predicted community representations, computed using the community co-occurrence matrix. We provide a comprehensive empirical investigation of a broad set of community detection algorithms from the literature on both synthetic networks, with varying levels of community explicitness, and real-world networks. We particularly investigate the fairness-performance trade-off using standard quality metrics and compare individual fairness outcomes with existing group fairness measures. The results show that individual unfairness can occur even when group fairness or clustering accuracy is high, underscoring that individual and group fairness are not interchangeable. Moreover, fairness depends critically on the detectability of community structure. However, we find that Significance and Surprise for denser graphs, and Combo, Leiden, and SBMDL for sparser graphs result in a better trade-off between individual fairness and community quality. Overall, our findings, together with the fact that community detection is an important step in many network analysis downstream tasks, highlight the necessity of developing fairness-aware community detection methods.

cs.SI↗

arXiv2026

Fairness-Sensitive PageRank Approximation

Real-world social networks have structural inequalities, including the majority and minorities, and fairness-agnostic centrality measures often amplify these inequalities by disproportionately favoring majority nodes. Fairness-Sensitive PageRank aims to balance algorithmic influence across structurally and demographically diverse groups while preserving the link-based relevance of classical PageRank. However, existing formulations require solving constrained matrix inversions that scale poorly with network size. In this work, we develop an efficient mean-field approximation for Fairness-Sensitive PageRank (FSPR) that enforces group-level fairness through an estimated teleportation (jump) vector, thereby avoiding the costly matrix inversion and iterative optimization. We derive a closed-form approximation of FSPR using the in-degree and group label of nodes, along with the global group proportion. We further analyze intra-class fluctuations by deriving expressions for the variance of approximated FSPR scores. Empirical results on real-world networks demonstrate that the proposed approximation efficiently estimates the FSPR while reducing runtime by an order of magnitude, enabling fairness-constrained ranking at scale.

cs.SI↗

arXiv2026

Fairness in Opinion Dynamics

Ways in which people's opinions change are, without a doubt, subject to a rich tapestry of differing influences. Factors that affect how one arrives at an opinion reflect how they have been shaped by their environment throughout their lives, education, material status, what belief systems are they subscribed to, and what socio-economic minorities are they a part of. This already complex system is further expanded by the ever-changing nature of one's social network. It is therefore no surprise that many models have a tendency to perform best for the majority of the population and discriminating those people who are members of various marginalized groups . This bias and the study of how to counter it are subject to a rapidly developing field of Fairness in Social Network Analysis (SNA). The focus of this work is to look into how a state-of-the-art model discriminates certain minority groups and whether it is possible to reliably predict for whom it will perform worse. Moreover, is such prediction possible based solely on one's demographic or topological features? To this end, the NetSense dataset, together with a state-of-the-art CoDiNG model for opinion prediction have been employed. Our work explores how three classifier models (Demography-Based, Topology-Based, and Hybrid) perform when assessing for whom this algorithm will provide inaccurate predictions. Finally, through a comprehensive analysis of these experimental results, we identify four key patterns of algorithmic bias. Our findings suggest that no single paradigm provides the best results and that there is a real need for context-aware strategies in fairness-oriented social network analysis. We conclude that a multi-faceted approach, incorporating both individual attributes and network structures, is essential for reducing algorithmic bias and promoting inclusive decision-making.

cs.SI↗

arXiv2025

DQ4FairIM: Fairness-aware Influence Maximization using Deep Reinforcement Learning

The Influence Maximization (IM) problem aims to select a set of seed nodes within a given budget to maximize the spread of influence in a social network. However, real-world social networks have several structural inequalities, such as dominant majority groups and underrepresented minority groups. If these inequalities are not considered while designing IM algorithms, the outcomes might be biased, disproportionately benefiting majority groups while marginalizing minorities. In this work, we address this gap by designing a fairness-aware IM method using Reinforcement Learning (RL) that ensures equitable influence outreach across all communities, regardless of protected attributes. Fairness is incorporated using a maximin fairness objective, which prioritizes improving the outreach of the least-influenced group, pushing the solution toward an equitable influence distribution. We propose a novel fairness-aware deep RL method, called DQ4FairIM, that maximizes the expected number of influenced nodes by learning an RL policy. The learnt policy ensures that minority groups formulate the IM problem as a Markov Decision Process (MDP) and use deep Q-learning, combined with the Structure2Vec network embedding, earning together with Structure2Vec network embedding to solve the MDP. We perform extensive experiments on synthetic benchmarks and real-world networks to compare our method with fairness-agnostic and fairness-aware baselines. The results show that our method achieves a higher level of fairness while maintaining a better fairness-performance trade-off than baselines. Additionally, our approach learns effective seeding policies that generalize across problem instances without retraining, such as varying the network size or the number of seed nodes.

cs.LG↗

arXiv2025

Perplexity-Homophily Index: Homophily through Diversity in Hypergraphs

Real-world complex systems are often better modeled as hypergraphs, where edges represent group interactions involving multiple entities. Understanding and quantifying homophily (similarity-driven association) in such networks is essential for analyzing community formation and information flow. We propose a hyperedge-centric framework to quantify homophily in hypergraphs. Each interaction is represented as a hyperedge, and its interaction perplexity measures the effective number of distinct attributes it contains. Comparing this observed perplexity with a degree-preserving random baseline defines the diversity gap, which quantifies how diverse an interaction is than expected by chance. The global homophily score for a network, called Perplexity-Homophily Index, is computed by averaging the normalized diversity gap across all hyperedges. Experiments on synthetic and real-world datasets show that the proposed index captures the full distribution of homophily and reveals how homophilic and heterophilic tendencies vary with interaction size in hypergraphs.

cs.SI↗

arXiv2025

Homophily in Complex Networks: Measures, Models, and Applications

Homophily, the tendency of individuals to connect with others who share similar attributes, is a defining feature of social networks. Understanding how groups interact, both within and across, is crucial for uncovering the dynamics of network evolution and the emergence of structural inequalities in these network. This tutorial offers a comprehensive overview of homophily, covering its various definitions, key properties, and the limitations of widely used metrics. Extending beyond traditional pairwise interactions, we will discuss homophily in higher-order network structures such as hypergraphs and simplicial complexes. We will further discuss network generating models capable of producing different types of homophilic networks with tunable levels of homophily and highlight their relevance in real-world contexts. The tutorial concludes with a discussion of open challenges, emerging directions, and opportunities for further research in this area.

cs.SI↗

arXiv2025

Dynamics of temporal influence in polarised networks

In social networks, it is often of interest to identify the most influential users who can successfully spread information to others. This is particularly important for marketing (e.g., targeting influencers for a marketing campaign) and to understand the dynamics of information diffusion (e.g., who is the most central user in the spreading of a certain type of information). However, different opinions often split the audience and make the network polarised. In polarised networks, information becomes soiled within communities in the network, and the most influential user within a network might not be the most influential across all communities. Additionally, influential users and their influence may change over time as users may change their opinion or choose to decrease or halt their engagement on the subject. In this work, we aim to study the temporal dynamics of users' influence in a polarised social network. We compare the stability of influence ranking using temporal centrality measures, while extending them to account for community structure across a number of network evolution behaviours. We show that we can successfully aggregate nodes into influence bands, and how to aggregate centrality scores to analyse the influence of communities over time. A modified version of the temporal independent cascade model and the temporal degree centrality perform the best in this setting, as they are able to reliably isolate nodes into their bands.

cs.SI↗

arXiv2025

Social Media and Academia: How Gender Influences Online Scholarly Discourse

This study investigates gender-based differences in online communication patterns of academics, focusing on how male and female academics represent themselves and how users interact with them on the social media platform X (formerly Twitter). We collect historical Twitter data of academics in computer science at the top 20 USA universities and analyze their tweets, retweets, and replies to uncover systematic patterns such as discussed topics, engagement disparities, and the prevalence of negative language or harassment. The findings indicate that while both genders discuss similar topics, men tend to post more tweets about AI innovation, current USA society, machine learning, and personal perspectives, whereas women post slightly more on engaging AI events and workshops. Women express stronger positive and negative sentiments about various events compared to men. However, the average emotional expression remains consistent across genders, with certain emotions being more strongly associated with specific topics. Writing-style analysis reveals that female academics show more empathy and are more likely to discuss personal problems and experiences, with no notable differences in other factors, such as self-praise, politeness, and stereotypical comments. Analyzing audience responses indicates that female academics are more frequently subjected to severe toxic and threatening replies. Our findings highlight the impact of gender in shaping the online communication of academics and emphasize the need for a more inclusive environment for scholarly engagement.

cs.SI↗

arXiv2025

Quantifying Group Fairness in Community Detection

Understanding community structures is crucial for analyzing networks, as nodes join communities that collectively shape large-scale networks. In real-world settings, the formation of communities is often impacted by several social factors, such as ethnicity, gender, wealth, or other attributes. These factors may introduce structural inequalities; for instance, real-world networks can have a few majority groups and many minority groups. Community detection algorithms, which identify communities based on network topology, may generate unfair outcomes if they fail to account for existing structural inequalities, particularly affecting underrepresented groups. In this work, we propose a set of novel group fairness metrics to assess the fairness of community detection methods. Additionally, we conduct a comparative evaluation of the most common community detection methods, analyzing the trade-off between performance and fairness. Experiments are performed on synthetic networks generated using LFR, ABCD, and HICH-BA benchmark models, as well as on real-world networks. Our results demonstrate that the fairness-performance trade-off varies widely across methods, with no single class of approaches consistently excelling in both aspects. We observe that Infomap and Significance methods are high-performing and fair with respect to different types of communities across most networks. The proposed metrics and findings provide valuable insights for designing fair and effective community detection algorithms.

cs.SI↗

arXiv2024

Group Fairness Metrics for Community Detection Methods in Social Networks

Understanding community structure has played an essential role in explaining network evolution, as nodes join communities which connect further to form large-scale complex networks. In real-world networks, nodes are often organized into communities based on ethnicity, gender, race, or wealth, leading to structural biases and inequalities. Community detection (CD) methods use network structure and nodes' attributes to identify communities, and can produce biased outcomes if they fail to account for structural inequalities, especially affecting minority groups. In this work, we propose group fairness metrics ($\Phi^{F*}_{p}$) to evaluate CD methods from a fairness perspective. We also conduct a comparative analysis of existing CD methods, focusing on the performance-fairness trade-off, to determine whether certain methods favor specific types of communities based on their size, density, or conductance. Our findings reveal that the trade-off varies significantly across methods, with no specific type of method consistently outperforming others. The proposed metrics and insights will help develop and evaluate fair and high performing CD methods.

cs.SI↗

arXiv2024

Gender differences in online communication: A case study of Soccer

Social media and digital platforms allow us to express our opinions freely and easily to a vast number of people. In this study, we examine whether there are gender-based differences in how communication happens via Twitter in regard to soccer. Soccer is one of the most popular sports, and therefore, on social media, it engages a diverse audience regardless of their technical knowledge. We collected Twitter data for three months (March-June) for English and Portuguese that contains 9.5 million Tweets related to soccer, and only 18.38% tweets were identified as belonging to women, highlighting a possible gender gap already in the number of people who participated actively in this topic. We then conduct a fine-grained text-level and network-level analysis to identify the gender differences that might exist while communicating on Twitter. Our results show that women express their emotions more intensely than men, regardless of the differences in volume. The network generated from Portuguese has lower homophily than English. However, this difference in homophily does not impact how females express their emotions and sentiments, suggesting that these aspects are inherent norms or characteristics of genders. Our study unveils more gaps through qualitative and quantitative analyses, highlighting the importance of examining and reporting gender gaps in online communication to create a more inclusive space where people can openly share their opinions.

cs.SI↗

arXiv2024

Fast degree-preserving rewiring of complex networks

In this paper we introduce a new, fast, degree-preserving rewiring algorithm for altering the assortativity of complex networks, which we call \textit{Fast total link (FTL) rewiring} algorithm. Commonly used existing algorithms require a large number of iterations, in particular in the case of large dense networks. This can especially be problematic when we wish to study ensembles of networks. In this work we aim to overcome aforementioned scalability problems by performing a rewiring of all edges at once to achieve a very high assortativity value before rewiring samples of edges at once to reduce this high assortativity value to the target value. The proposed method performs better than existing methods by several orders of magnitude for a range of structurally diverse complex networks, both in terms of the number of iterations taken, and time taken to reach a given assortativity value. Here we test our proposed algorithm on networks with up to $100,000$ nodes and around $750,000$ edges and find that the relative improvements in speed remain, showing that the algorithm is both efficient and scalable.

physics.soc-ph↗

arXiv2023

Heterophily-Based Graph Neural Network for Imbalanced Classification

Graph neural networks (GNNs) have shown promise in addressing graph-related problems, including node classification. However, conventional GNNs assume an even distribution of data across classes, which is often not the case in real-world scenarios, where certain classes are severely underrepresented. This leads to suboptimal performance of standard GNNs on imbalanced graphs. In this paper, we introduce a unique approach that tackles imbalanced classification on graphs by considering graph heterophily. We investigate the intricate relationship between class imbalance and graph heterophily, revealing that minority classes not only exhibit a scarcity of samples but also manifest lower levels of homophily, facilitating the propagation of erroneous information among neighboring nodes. Drawing upon this insight, we propose an efficient method, called Fast Im-GBK, which integrates an imbalance classification strategy with heterophily-aware GNNs to effectively address the class imbalance problem while significantly reducing training time. Our experiments on real-world graphs demonstrate our model's superiority in classification performance and efficiency for node classification tasks compared to existing baselines.

cs.LG↗

arXiv2022

DCC: A Cascade based Approach to Detect Communities in Social Networks

Community detection in Social Networks is associated with finding and grouping the most similar nodes inherent in the network. These similar nodes are identified by computing tie strength. Stronger ties indicates higher proximity shared by connected node pairs. This work is motivated by Granovetter's argument that suggests that strong ties lies within densely connected nodes and the theory that community cores in real-world networks are densely connected. In this paper, we have introduced a novel method called \emph{Disjoint Community detection using Cascades (DCC)} which demonstrates the effectiveness of a new local density based tie strength measure on detecting communities. Here, tie strength is utilized to decide the paths followed for propagating information. The idea is to crawl through the tuple information of cascades towards the community core guided by increasing tie strength. Considering the cascade generation step, a novel preferential membership method has been developed to assign community labels to unassigned nodes. The efficacy of $DCC$ has been analyzed based on quality and accuracy on several real-world datasets and baseline community detection algorithms.

cs.SI↗