market network analysis

This paper addresses the challenges of identifying functional roles and ensuring interpretability for financial institutions within multilayer financial networks. We propose an interpretable node embedding method grounded in egonet-based structural features, integrating transaction proximity metrics, robust clustering, and role evaluation criteria to perform cross-market-layer functional role partitioning on ECB MMSR transaction-level data. Unlike conventional black-box embedding approaches, our method explicitly models local topological structures, enabling precise identification and semantic interpretation of heterogeneous functional roles—including intermediaries, cross-segment connectors, and peripheral lenders. Empirical results demonstrate that the framework substantially improves role classification accuracy and regulatory traceability, offering an interpretable and operationally deployable analytical tool for systemic risk monitoring and differentiated supervision.

Static centrality measures fail to capture perturbation propagation dynamics and resilience bottlenecks in complex network analysis. Method: This paper proposes a unified modeling framework integrating node centrality, graph Laplacian spectral features, and continuous-time diffusion dynamics. It enables the first synergistic modeling and multi-dimensional joint optimization of these three indicator classes, overcoming limitations of unidimensional assessment. Leveraging spectral analysis and stochastic diffusion processes, we develop an interpretable method for critical node identification and vulnerability localization. Results: Validation on synthetic networks demonstrates a 23.6% improvement in critical node identification accuracy, significantly enhancing detection of propagation bottlenecks and robustness weaknesses. The framework provides theoretical foundations and decision-support tools for epidemic control and cybersecurity hardening.

This study investigates how economic transactions in Web3 foster social relationships, shared narratives, and collective identities rather than merely functioning as digital asset markets. By integrating on-chain transaction data with off-chain social media activity and combining network analysis with discourse analysis, the research empirically examines over one hundred NFT communities, establishing a novel linkage between their structural characteristics and cultural dynamics. Findings reveal that communities centered on holding behaviors exhibit high cohesion and sustained cultural engagement, whereas those driven primarily by speculation tend toward fragmentation and lack social embeddedness. The work proposes a scalable socio-technical analytical framework to identify patterns of inclusion, exclusion, and representational imbalance within Web3 ecosystems, offering a new paradigm for understanding the evolution of decentralized communities.

Conventional network connectivity measures fail to capture dynamic spillovers both across and within markets under clustered structures. Method: We propose a cluster-based network connectivity framework that partitions nodes by asset class, industry, or region; assumes orthogonal shocks across clusters but correlated shocks within clusters—relaxing the standard VAR orthogonalization assumption. Our approach is the first to embed cluster structure into the variance decomposition framework, integrating Sims orthogonalization with Koop-Pesaran-Potter-Shin generalized impulse responses to jointly achieve cross-cluster orthogonal identification and intra-cluster generalized identification. Node ranking depends solely on inter-cluster relationships, rendering intra-cluster ordering irrelevant. Results: Applied to equity market data from sixteen countries, the framework uncovers heterogeneous connectivity patterns under regional and industry clustering, substantially enhancing the explanatory power and predictive accuracy of systemic risk transmission pathways.

Traditional measures of risk spillovers struggle to capture the local interaction structures that drive systemic risk. This study proposes a novel analytical framework based on directed triadic motifs, integrating quantile-based connectivity networks with asset sector labels to construct multiscale backbone networks. By introducing colored motifs and a diversity metric based on orbit positions, the approach uniquely bridges local topological features with tail systemic impact and portfolio construction. Empirical results demonstrate that motif-driven portfolios significantly outperform minimum-correlation and minimum-connectivity benchmarks in terms of risk-adjusted returns. Moreover, assets exhibiting high orbit diversity within tail-risk networks are more likely to act as net risk transmitters.

This study addresses the limitations of traditional node similarity measures, which often assume a uniform and continuous feature space and thus fail to capture the true structural equivalence among nodes in attributed networks. By integrating neighborhood attribute profiling, dimensionality reduction, and visualization techniques, the authors uncover complex nonlinear manifold structures and density biases inherent in high-dimensional feature spaces. Empirical analysis on an enterprise transaction network reveals that semantically identical industry labels can correspond to multiple disconnected regions of structural roles, and that supply chain tiers exhibit continuous transitions rather than discrete partitions. These findings motivate the proposal of a new similarity metric grounded in manifold topology to more accurately reflect structural equivalence among nodes.

Traditional volume-based metrics struggle to capture structural stress in decentralized transaction networks. This work proposes a novel network-topology-based inefficiency metric that, for the first time, treats effective diameter and closeness centrality as distinct structural dimensions. Leveraging six years of Hedera transaction data, the authors construct a physically interpretable measurement framework. Through principal component analysis, Pearson correlation matrices, and a seven-dimensional isolation forest for anomaly detection, the proposed metric successfully identifies topological disturbances caused by intermediary fragmentation or surges in smart contract activity. Moreover, it reveals network contraction during periods of market stress, effectively capturing multidimensional systemic anomalies.

This study addresses a central challenge in modeling the evolution of complex networks: selecting the optimal network generative model from a set of candidates. It presents the first systematic review and classification of existing model selection methods, organizing them into four categories based on their underlying principles. The work provides a comprehensive analysis of each approach’s theoretical foundations, technical implementation, and available software tools. By offering a panoramic overview of the current landscape, this research not only clarifies key methodological distinctions but also identifies promising directions for future work. Ultimately, it lays the groundwork for developing a unified and efficient framework for network model selection, serving as an essential reference for researchers in the field.

This study investigates the transmission mechanisms of financial systemic risk through multiple channels and identifies key sources of risk propagation. To this end, the authors propose a multiplex network Hawkes model that explicitly disentangles three distinct contagion channels—asset similarity, solvency, and profitability—within a unified framework by incorporating excitation weights dependent on node and edge covariates, thereby overcoming the limitations of traditional single-layer homogeneous excitation assumptions. Employing a Bayesian inference approach based on Markov chain Monte Carlo (MCMC) and leveraging large-scale credit default swap (CDS) data, the empirical analysis covers 99 European and U.S. financial institutions from 2004 to 2022. The findings reveal that systemic risk is predominantly driven by a small subset of institutions, that industry similarity constitutes the most robust asset-related linkage, and that all three channels significantly contribute to risk contagion.

This study presents the first empirical examination of the competitive linking hypothesis within the Barabási-Albert network growth model. We propose a robust statistical framework to quantify node competitiveness and, integrated with complex network modeling, conduct systematic analyses of news comment and movie datasets. Our results demonstrate that perfect competition is absent in practice; instead, newly arriving nodes exhibit heterogeneous influence patterns ranging from elastic to moderate regimes. By addressing the longstanding gap in empirical validation of this competitive assumption, this work reveals the inherent heterogeneity of node influence in real-world growing networks. These findings provide critical theoretical and empirical foundations for the precise modeling of complex systems and for practical applications such as recommendation algorithms in e-commerce platforms.