pwc-archive/papers-with-abstracts huggingface dataset:
Simulating Rumor Spreading in Social Networks using LLM Agents
Authors: Tianrui Hu, Dimitrios Liakopoulos, Xiwen Wei, Radu Marculescu, Neeraja J. Yadwadkar
Date: 2025-02-03 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Misinformation

With the rise of social media, misinformation has become increasingly prevalent, fueled largely by the spread of rumors. This study explores the use of Large Language Model (LLM) agents within a novel framework to simulate and analyze the dynamics of rumor propagation across social networks. To this end, we design a variety of LLM-based agent types and construct four distinct network structures to conduct these simulations. Our framework assesses the effectiveness of different network constructions and agent behaviors in influencing the spread of rumors. Our results demonstrate that the framework can simulate rumor spreading across more than one hundred agents in various networks with thousands of edges. The evaluations indicate that network structure, personas, and spreading schemes can significantly influence rumor dissemination, ranging from no spread to affecting 83\% of agents in iterations, thereby offering a realistic simulation of rumor spread in social networks.
DiffIM: Differentiable Influence Minimization with Surrogate Modeling and Continuous Relaxation
Authors: Junghun Lee, Hyunju Kim, Fanchen Bu, Jihoon Ko, Kijung Shin
Date: 2025-02-03 00:00:00
Tasks: Blocking

In social networks, people influence each other through social links, which can be represented as propagation among nodes in graphs. Influence minimization (IMIN) is the problem of manipulating the structures of an input graph (e.g., removing edges) to reduce the propagation among nodes. IMIN can represent time-critical real-world applications, such as rumor blocking, but IMIN is theoretically difficult and computationally expensive. Moreover, the discrete nature of IMIN hinders the usage of powerful machine learning techniques, which requires differentiable computation. In this work, we propose DiffIM, a novel method for IMIN with two differentiable schemes for acceleration: (1) surrogate modeling for efficient influence estimation, which avoids time-consuming simulations (e.g., Monte Carlo), and (2) the continuous relaxation of decisions, which avoids the evaluation of individual discrete decisions (e.g., removing an edge). We further propose a third accelerating scheme, gradient-driven selection, that chooses edges instantly based on gradients without optimization (spec., gradient descent iterations) on each test instance. Through extensive experiments on real-world graphs, we show that each proposed scheme significantly improves speed with little (or even no) IMIN performance degradation. Our method is Pareto-optimal (i.e., no baseline is faster and more effective than it) and typically several orders of magnitude (spec., up to 15,160X) faster than the most effective baseline while being more effective.
When Do LLMs Help With Node Classification? A Comprehensive Analysis
Authors: Xixi Wu, Yifei Shen, Fangzhou Ge, Caihua Shan, Yizhu Jiao, Xiangguo Sun, Hong Cheng
Date: 2025-02-02 00:00:00
Tasks: Node Classification

Node classification is a fundamental task in graph analysis, with broad applications across various fields. Recent breakthroughs in Large Language Models (LLMs) have enabled LLM-based approaches for this task. Although many studies demonstrate the impressive performance of LLM-based methods, the lack of clear design guidelines may hinder their practical application. In this work, we aim to establish such guidelines through a fair and systematic comparison of these algorithms. As a first step, we developed LLMNodeBed, a comprehensive codebase and testbed for node classification using LLMs. It includes 10 homophilic datasets, 4 heterophilic datasets, 8 LLM-based algorithms, 8 classic baselines, and 3 learning paradigms. Subsequently, we conducted extensive experiments, training and evaluating over 2,700 models, to determine the key settings (e.g., learning paradigms and homophily) and components (e.g., model size and prompt) that affect performance. Our findings uncover 8 insights, e.g., (1) LLM-based methods can significantly outperform traditional methods in a semi-supervised setting, while the advantage is marginal in a supervised setting; (2) Graph Foundation Models can beat open-source LLMs but still fall short of strong LLMs like GPT-4o in a zero-shot setting. We hope that the release of LLMNodeBed, along with our insights, will facilitate reproducible research and inspire future studies in this field. Codes and datasets are released at \href{https://llmnodebed.github.io/}{\texttt{https://llmnodebed.github.io/}}.
Generic Multimodal Spatially Graph Network for Spatially Embedded Network Representation Learning
Authors: Xudong Fan, Jürgen Hackl
Date: 2025-02-01 00:00:00
Tasks: Representation Learning

Spatially embedded networks (SENs) represent a special type of complex graph, whose topologies are constrained by the networks' embedded spatial environments. The graph representation of such networks is thereby influenced by the embedded spatial features of both nodes and edges. Accurate network representation of the graph structure and graph features is a fundamental task for various graph-related tasks. In this study, a Generic Multimodal Spatially Graph Convolutional Network (GMu-SGCN) is developed for efficient representation of spatially embedded networks. The developed GMu-SGCN model has the ability to learn the node connection pattern via multimodal node and edge features. In order to evaluate the developed model, a river network dataset and a power network dataset have been used as test beds. The river network represents the naturally developed SENs, whereas the power network represents a man-made network. Both types of networks are heavily constrained by the spatial environments and uncertainties from nature. Comprehensive evaluation analysis shows the developed GMu-SGCN can improve accuracy of the edge existence prediction task by 37.1\% compared to a GraphSAGE model which only considers the node's position feature in a power network test bed. Our model demonstrates the importance of considering the multidimensional spatial feature for spatially embedded network representation.
Multi-Order Hyperbolic Graph Convolution and Aggregated Attention for Social Event Detection
Authors: Yao Liu, Zhilan Liu, Tien Ping Tan, Yuxin Li
Date: 2025-02-01 00:00:00
Tasks: Event Detection

Social event detection (SED) is a task focused on identifying specific real-world events and has broad applications across various domains. It is integral to many mobile applications with social features, including major platforms like Twitter, Weibo, and Facebook. By enabling the analysis of social events, SED provides valuable insights for businesses to understand consumer preferences and supports public services in handling emergencies and disaster management. Due to the hierarchical structure of event detection data, traditional approaches in Euclidean space often fall short in capturing the complexity of such relationships. While existing methods in both Euclidean and hyperbolic spaces have shown promising results, they tend to overlook multi-order relationships between events. To address these limitations, this paper introduces a novel framework, Multi-Order Hyperbolic Graph Convolution with Aggregated Attention (MOHGCAA), designed to enhance the performance of SED. Experimental results demonstrate significant improvements under both supervised and unsupervised settings. To further validate the effectiveness and robustness of the proposed framework, we conducted extensive evaluations across multiple datasets, confirming its superiority in tackling common challenges in social event detection.
Demystifying MPNNs: Message Passing as Merely Efficient Matrix Multiplication
Authors: Qin Jiang, Chengjia Wang, Michael Lones, Wei Pang
Date: 2025-01-31 00:00:00

While Graph Neural Networks (GNNs) have achieved remarkable success, their design largely relies on empirical intuition rather than theoretical understanding. In this paper, we present a comprehensive analysis of GNN behavior through three fundamental aspects: (1) we establish that \textbf{$k$-layer} Message Passing Neural Networks efficiently aggregate \textbf{$k$-hop} neighborhood information through iterative computation, (2) analyze how different loop structures influence neighborhood computation, and (3) examine behavior across structure-feature hybrid and structure-only tasks. For deeper GNNs, we demonstrate that gradient-related issues, rather than just over-smoothing, can significantly impact performance in sparse graphs. We also analyze how different normalization schemes affect model performance and how GNNs make predictions with uniform node features, providing a theoretical framework that bridges the gap between empirical success and theoretical understanding.
Israel-Hamas war through Telegram, Reddit and Twitter
Authors: Despoina Antonakaki, Sotiris Ioannidis
Date: 2025-01-30 00:00:00
Tasks: Sentiment Analysis

The Israeli-Palestinian conflict started on 7 October 2023, have resulted thus far to over 48,000 people killed including more than 17,000 children with a majority from Gaza, more than 30,000 people injured, over 10,000 missing, and over 1 million people displaced, fleeing conflict zones. The infrastructure damage includes the 87\% of housing units, 80\% of public buildings and 60\% of cropland 17 out of 36 hospitals, 68\% of road networks and 87\% of school buildings damaged. This conflict has as well launched an online discussion across various social media platforms. Telegram was no exception due to its encrypted communication and highly involved audience. The current study will cover an analysis of the related discussion in relation to different participants of the conflict and sentiment represented in those discussion. To this end, we prepared a dataset of 125K messages shared on channels in Telegram spanning from 23 October 2025 until today. Additionally, we apply the same analysis in two publicly available datasets from Twitter containing 2001 tweets and from Reddit containing 2M opinions. We apply a volume analysis across the three datasets, entity extraction and then proceed to BERT topic analysis in order to extract common themes or topics. Next, we apply sentiment analysis to analyze the emotional tone of the discussions. Our findings hint at polarized narratives as the hallmark of how political factions and outsiders mold public opinion. We also analyze the sentiment-topic prevalence relationship, detailing the trends that may show manipulation and attempts of propaganda by the involved parties. This will give a better understanding of the online discourse on the Israel-Palestine conflict and contribute to the knowledge on the dynamics of social media communication during geopolitical crises.
The Best Soules Basis for the Estimation of a Spectral Barycentre Network
Authors: François G. Meyer
Date: 2025-01-26 00:00:00

The main contribution of this work is a fast algorithm to compute the barycentre of a set of networks based on a Laplacian spectral pseudo-distance. The core engine for the reconstruction of the barycentre is an algorithm that explores the large library of Soules bases, and returns a basis that yields a sparse approximation of the sample mean adjacency matrix. We prove that when the networks are random realizations of stochastic block models, then our algorithm reconstructs the population mean adjacency matrix. In addition to the theoretical analysis of the estimator of the barycentre network, we perform Monte Carlo simulations to validate the theoretical properties of the estimator. This work is significant because it opens the door to the design of new spectral-based network synthesis that have theoretical guarantees.
Reachability-Based Contingency Planning against Multi-Modal Predictions with Branch MPC
Authors: Mohamed-Khalil Bouzidi, Bojan Derajic, Daniel Goehring, Joerg Reichardt
Date: 2025-02-04 00:00:00
Tasks: Model Predictive Control

This paper presents a novel contingency planning framework that integrates learning-based multi-modal predictions of traffic participants into Branch Model Predictive Control (MPC). Leveraging reachability analysis, we address the computational challenges associated with Branch MPC by organizing the multitude of predictions into driving corridors. Analyzing the overlap between these corridors, their number can be reduced through pruning and clustering while ensuring safety since all prediction modes are preserved. These processed corridors directly correspond to the distinct branches of the scenario tree and provide an efficient constraint representation for the Branch MPC. We further utilize the reachability for determining maximum feasible decision postponing times, ensuring that branching decisions remain executable. Qualitative and quantitative evaluations demonstrate significantly reduced computational complexity and enhanced safety and comfort.
System Integrity Protection Schemes in the Nordics -- a comparative analysis
Authors: Gabriel Malmer, Arvid Rolander, Emil Hillberg, Olof Samuelsson, Susanne Ackeby, Lars Nordström
Date: 2025-02-04 00:00:00
Tasks: Relation

To increase the utilisation rate of the power system and accelerate electrification while providing a high degree of security and reliability, System Integrity Protection Schemes (SIPS) are of great importance. SIPS functions are automatic remedial actions, detecting abnormal conditions or contingencies in the system and taking control action to mitigate these conditions. Design, implementation, maintenance and coordination of SIPS are all important aspects for desired operation. However, different actors have chosen different approaches to using SIPS for capacity enhancement, and there are discrepancies in how capacity is valued in relation to for example complexity, reliability and risk. Additionally, definitions often vary between countries. This paper reports on a joint survey and interview study on SIPS with stakeholders and experts in the Nordic countries - including TSOs, DSOs and industry. Combined with a literature review, a comparison and analysis of how SIPS are used in the Nordics is performed, particularly in relation to ENTSO-E capacity allocation.
Robust contraction-based model predictive control for nonlinear systems
Authors: Marco Polver, Daniel Limon, Fabio Previdi, Antonio Ferramosca
Date: 2025-02-04 00:00:00
Tasks: Model Predictive Control

Model Predictive Control (MPC) is a widely known control method that has proved to be particularly effective in multivariable and constrained control. Closed-loop stability and recursive feasibility can be guaranteed by employing accurate models in prediction and suitable terminal ingredients, i.e. the terminal cost function and the terminal constraint. Issues might arise in case of model mismatches or perturbed systems, as the state predictions could be inaccurate, and nonlinear systems for which the computation of the terminal ingredients can result challenging. In this manuscript, we exploit the properties of component-wise uniformly continuous and stabilizable systems to introduce a robust contraction-based MPC for the regulation of nonlinear perturbed systems, that employs an easy-to-design terminal cost function, does not make use of terminal constraints, and selects the shortest prediction horizon that guarantees the stability of the closed-loop system.
Secure Data Reconstruction: A Direct Data-Driven Approach
Authors: Jiaqi Yan, Ivan Markovsky, John Lygeros
Date: 2025-02-01 00:00:00
Tasks: State Space Models, Trajectory Recovery

This paper addresses the problem of secure data reconstruction for unknown systems, where data collected from the system are susceptible to malicious manipulation. We aim to recover the real trajectory without prior knowledge of the system model. To achieve this, a behavioral language is used to represent the system, describing it using input/output trajectories instead of state-space models. We consider two attack scenarios. In the first scenario, up to $k$ entries of the collected data are malicious. On the other hand, the second scenario assumes that at most $k$ channels from sensors or actuators can be compromised, implying that any data collected from these channels might be falsified. For both scenarios, we formulate the trajectory recovery problem as an optimization problem and introduce sufficient conditions to ensure successful recovery of the true data. Since finding exact solutions to these problems can be computationally inefficient, we further approximate them using an $\ell_1$-norm and group Least Absolute Shrinkage and Selection Operator (LASSO). We demonstrate that under certain conditions, these approximation problems also find the true trajectory while maintaining low computation complexity. Finally, we extend the proposed algorithms to noisy data. By reconstructing the secure trajectory, this work serves as a safeguard mechanism for subsequent data-driven control methods.
Graph-based Impact Analysis of Cyber-Attacks on Behind-the-Meter Infrastructure
Authors: Immanuel Hacker, Ömer Sen, Florian Klein-Helmkamp, Andreas Ulbig
Date: 2025-02-04 00:00:00

Behind-the-Meter assets are getting more interconnected to realise new applications like flexible tariffs. Cyber-attacks on the resulting control infrastructure may impact a large number of devices, which can result in severe impact on the power system. To analyse the possible impact of such attacks we developed a graph model of the cyber-physical energy system, representing interdependencies between the control infrastructure and the power system. This model is than used for an impact analysis of cyber-attacks with different attack vectors.
Exponentially Stable Combined Adaptive Control under Finite Excitation Condition
Authors: Manish Patel, Arnab Maity
Date: 2025-02-04 00:00:00

The parameter convergence relies on a stringent persistent excitation (PE) condition in adaptive control. Several works have proposed a memory term in the last decade to translate the PE condition to a feasible finite excitation (FE) condition. This work proposes a combined model reference adaptive control for a class of uncertain nonlinear systems with an unknown control effectiveness vector. The closed-loop system is exponentially stable under the FE condition. The exponential rate of convergence is independent of the excitation level of the regressor vector and is lower-bounded in terms of the system parameters and user-designed gains. Numerical simulation is illustrated, validating the results obtained with the proposed adaptive control.
Identifying Large-Scale Linear Parameter Varying Systems with Dynamic Mode Decomposition Methods
Authors: Jean Panaioti Jordanou, Eduardo Camponogara, Eduardo Gildin
Date: 2025-02-04 00:00:00

Linear Parameter Varying (LPV) Systems are a well-established class of nonlinear systems with a rich theory for stability analysis, control, and analytical response finding, among other aspects. Although there are works on data-driven identification of such systems, the literature is quite scarce in terms of works that tackle the identification of LPV models for large-scale systems. Since large-scale systems are ubiquitous in practice, this work develops a methodology for the local and global identification of large-scale LPV systems based on nonintrusive reduced-order modeling. The developed method is coined as DMD-LPV for being inspired in the Dynamic Mode Decomposition (DMD). To validate the proposed identification method, we identify a system described by a discretized linear diffusion equation, with the diffusion gain defined by a polynomial over a parameter. The experiments show that the proposed method can easily identify a reduced-order LPV model of a given large-scale system without the need to perform identification in the full-order dimension, and with almost no performance decay over performing a reduction, given that the model structure is well-established.
Hybrid Resolver Model Generalization for Fault Condition Modeling: A Promising Tool for Reliability Study
Authors: MohammadSadegh KhajueeZadeh, Farid Tootoonchian, Ali Pourghoraba
Date: 2025-02-04 00:00:00
Tasks: Fault Diagnosis

Resolvers, like all electromagnetic devices, are constantly under investigation, both operationally and structurally. In this regard, proposing a modeling methodology that can save significant time without compromising accuracy is a big honor. In this study, a generalized hybrid model is suggested that, in addition to the above benefits, has sufficient capability to ease reliability study in the field of resolvers, where a large number of faulty conditions must be investigated under different operating conditions, including changes in angular velocity, voltage, and frequency of excitation; all of which are highlighted in the context of fault coverage. This model also serves as a promising tool for generating large datasets, which is advantageous for fault diagnosis. A resolver with a non-uniform air gap is chosen as a case study to challenge the suggested model, particularly in relation to eccentricity faults. We generalize the suggested model to account for the most common faulty conditions of resolvers: in-turn short circuits in signal and excitation windings, as well as static and dynamic eccentricity faults. The close agreement between the results of the suggested model and those from Time-Stepping Finite Element Analysis (TS-FEA), along with significant time savings in both healthy and faulty conditions, highlights the generality and proficiency of the suggested model. Finally, the case study is prototyped, and we verify the accuracy of the suggested model experimentally.
Gaussian processes for dynamics learning in model predictive control
Authors: Anna Scampicchio, Elena Arcari, Amon Lahr, Melanie N. Zeilinger
Date: 2025-02-04 00:00:00
Tasks: Gaussian Processes, Model Predictive Control, regression, Uncertainty Quantification

Due to its state-of-the-art estimation performance complemented by rigorous and non-conservative uncertainty bounds, Gaussian process regression is a popular tool for enhancing dynamical system models and coping with their inaccuracies. This has enabled a plethora of successful implementations of Gaussian process-based model predictive control in a variety of applications over the last years. However, despite its evident practical effectiveness, there are still many open questions when attempting to analyze the associated optimal control problem theoretically and to exploit the full potential of Gaussian process regression in view of safe learning-based control. The contribution of this review is twofold. The first is to survey the available literature on the topic, highlighting the major theoretical challenges such as (i) addressing scalability issues of Gaussian process regression; (ii) taking into account the necessary approximations to obtain a tractable MPC formulation; (iii) including online model updates to refine the dynamics description, exploiting data collected during operation. The second is to provide an extensive discussion of future research directions, collecting results on uncertainty quantification that are related to (but yet unexploited in) optimal control, among others. Ultimately, this paper provides a toolkit to study and advance Gaussian process-based model predictive control.