A Data-driven Approach for Constructing Multilayer Network-based Service Ecosystem Models

• URL: http://arxiv.org/abs/2004.10383v1
• Date: Wed, 22 Apr 2020 03:21:38 GMT
• Title: A Data-driven Approach for Constructing Multilayer Network-based Service Ecosystem Models
• Abstract summary: Service ecosystems have become a focus in both research and practice. "Events" are introduced to describe the triggers of service ecosystem evolution. Our approach can construct large-scale models for real-world service ecosystems with lower cost and higher efficiency.
• Score: 5.211872784262557
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Services are flourishing drastically both on the Internet and in the real world. Additionally, services have become much more interconnected to facilitate transboundary business collaboration to create and deliver distinct new values to customers. Various service ecosystems have become a focus in both research and practice. However, due to the lack of widely recognized service ecosystem models and sufficient data for constructing such models, existing studies on service ecosystems are limited to very narrow scope and cannot effectively guide the design, optimization, and evolution of service ecosystems. We propose a Multilayer network-based Service Ecosystem Model, which covers a variety of service-related elements, including stakeholders, channels, functional and nonfunctional features, and domains, and especially, structural and evolutionary relations between them. "Events" are introduced to describe the triggers of service ecosystem evolution. We propose a data-driven approach for constructing MSEM from public media news and external data sources. Qualitative comparison with state-of-the-art models shows that MSEM has a higher coverage degree of fine-grained elements/relations in service ecosystems and richer semantics for higher interpretability. Experiments conducted on real news corpora show that compared with other approaches, our approach can construct large-scale models for real-world service ecosystems with lower cost and higher efficiency.

Related papers

• Service Ecosystem Evolution: A Comprehensive Survey from Complex Network Perspectives [39.34068212247308]
  Digital society increasingly relies on complex service ecosystems, formed by interconnected services from technology giants.<n>We present a comprehensive survey focused on the study of service ecosystem evolution, employing a novel three-stage analytical framework.<n>We pioneer the application of complex network theory to analyze service ecosystem evolution, providing a novel perspective to capture the inherent connectivity, dynamics, and emergent properties often missed by traditional approaches.
  arXiv  Detail & Related papers  (2026-07-22T08:36:12Z)
• Retrieval-as-a-Service:A System-Oriented Analysis of Industrial Retrieval Pipelines in Web Systems [5.990455845813845]
  Retrieval systems have become a foundational infrastructure component in modern Web services.<n>In large-scale industrial environments, retrieval is increasingly deployed as an independent service layer.<n>This paper focuses on architectural design and deployment trade-offs under real-world constraints.
  arXiv  Detail & Related papers  (2026-06-12T20:10:02Z)
• Heterogeneous Multi-Agent Modeling for Measurement and Network Analysis of the Data Service Market [50.00393065059756]
  This paper proposes a data service market measurement and network analysis method based on heterogeneous multi-agent modeling.<n>By introducing the service ecosystem theory, we clarify the participants and external factors of the data service market.<n>Furthermore, an analytical methodology is devised to precisely assess the influence of heterogeneous networks on utility.
  arXiv  Detail & Related papers  (2026-05-22T08:44:56Z)
• Modeling Dependency-Propagated Ecosystem Impact of Changes in Maintenance Activities: Evaluating Support Strategies in the PyPI Network [83.16077040470975]
  We introduce a dependency-aware model of ecosystem impact that captures how changes in maintenance activities propagate through the Python Package Index (PyPI) ecosystem.<n>Our results show that a large share of the modeled ecosystem impact can be attributed to just 0.1% of all PyPI packages when prioritized based on dependency-propagated impact.
  arXiv  Detail & Related papers  (2026-05-07T12:51:00Z)
• When Model Editing Meets Service Evolution: A Knowledge-Update Perspective for Service Recommendation [28.779676579427967]
  EVOREC is an evolution-aware framework for service recommendation that incorporates updated service facts without costly retraining efficiently.<n>We introduce a Finite Automata-based constrained decoding mechanism with deduplication, which enforces structural and semantic validity while eliminating repeated services.<n>Our approach outperforms model fine-tuning approaches by 22.3%, demonstrating strong adaptability to service evolution.
  arXiv  Detail & Related papers  (2026-04-29T13:51:56Z)
• Adaptive Dual-Weighting Framework for Federated Learning via Out-of-Distribution Detection [53.45696787935487]
  Federated Learning (FL) enables collaborative model training across large-scale distributed service nodes.<n>In real-world service-oriented deployments, data generated by heterogeneous users, devices, and application scenarios are inherently non-IID.<n>We propose FLood, a novel FL framework inspired by out-of-distribution (OOD) detection.
  arXiv  Detail & Related papers  (2026-02-01T05:54:59Z)
• QoSGMAA: A Robust Multi-Order Graph Attention and Adversarial Framework for Sparse QoS Prediction [12.037416164995605]
  We propose a novel architecture,MGAA, specifically designed to enhance prediction accuracy in complex and noisy network service environments.<n>To capture complex, higher-order interactions among users and services, we employ a discrete sampling technique.<n>Our proposed model significantly outperforms existing baseline methods, highlighting its strong potential for practical deployment in service selection and recommendation scenarios.
  arXiv  Detail & Related papers  (2025-10-27T04:03:28Z)
• Beyond the model: Key differentiators in large language models and multi-agent services [0.0]
  With the launch of foundation models like DeepSeek, Manus AI, and Llama 4, it has become evident that large language models (LLMs) are no longer the sole defining factor in generative AI.<n>This review article delves into these critical differentiators that ensure modern AI services are efficient and profitable.
  arXiv  Detail & Related papers  (2025-05-05T09:15:31Z)
• Data-Juicer 2.0: Cloud-Scale Adaptive Data Processing for and with Foundation Models [64.28420991770382]
  Data-Juicer 2.0 is a data processing system backed by data processing operators spanning text, image, video, and audio modalities.<n>It supports more critical tasks including data analysis, annotation, and foundation model post-training.<n>It has been widely adopted in diverse research fields and real-world products such as Alibaba Cloud PAI.
  arXiv  Detail & Related papers  (2024-12-23T08:29:57Z)
• MetaTrading: An Immersion-Aware Model Trading Framework for Vehicular Metaverse Services [94.61039892220037]
  We present a novel immersion-aware model trading framework that incentivizes metaverse users (MUs) to contribute learning models for augmented reality (AR) services in the vehicular metaverse. Considering dynamic network conditions and privacy concerns, we formulate the reward decisions of MSPs as a multi-agent Markov decision process. Experimental results demonstrate that the proposed framework can effectively provide higher-value models for object detection and classification in AR services on real AR-related vehicle datasets.
  arXiv  Detail & Related papers  (2024-10-25T16:20:46Z)
• Data-Juicer Sandbox: A Feedback-Driven Suite for Multimodal Data-Model Co-development [67.55944651679864]
  We present a new sandbox suite tailored for integrated data-model co-development. This sandbox provides a feedback-driven experimental platform, enabling cost-effective and guided refinement of both data and models.
  arXiv  Detail & Related papers  (2024-07-16T14:40:07Z)
• Tree-based variational inference for Poisson log-normal models [47.82745603191512]
  hierarchical trees are often used to organize entities based on proximity criteria. Current count-data models do not leverage this structured information. We introduce the PLN-Tree model as an extension of the PLN model for modeling hierarchical count data.
  arXiv  Detail & Related papers  (2024-06-25T08:24:35Z)
• Quantifying Heterogeneous Ecosystem Services With Multi-Label Soft Classification [2.4213557151485396]
  quantifying ecosystem services are crucial for sustainable environmental management, conservation efforts, and policy-making. Ground truth labels, such as biodiversity, are very difficult and expensive to measure. proxy labels, such as land use, often fail to capture the complex heterogeneity of the ecosystem.
  arXiv  Detail & Related papers  (2024-06-24T21:38:13Z)
• Balancing Similarity and Complementarity for Federated Learning [91.65503655796603]
  Federated Learning (FL) is increasingly important in mobile and IoT systems. One key challenge in FL is managing statistical heterogeneity, such as non-i.i.d. data. We introduce a novel framework, textttFedSaC, which balances similarity and complementarity in FL cooperation.
  arXiv  Detail & Related papers  (2024-05-16T08:16:19Z)
• A Microservices Identification Method Based on Spectral Clustering for Industrial Legacy Systems [5.255685751491305]
  We propose an automated microservice decomposition method for extracting microservice candidates based on spectral graph theory. We show that our method can yield favorable results even without the involvement of domain experts.
  arXiv  Detail & Related papers  (2023-12-20T07:47:01Z)
• Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective [64.04617968947697]
  We introduce a novel data-model co-design perspective: to promote superior weight sparsity. Specifically, customized Visual Prompts are mounted to upgrade neural Network sparsification in our proposed VPNs framework.
  arXiv  Detail & Related papers  (2023-12-03T13:50:24Z)
• FREE: The Foundational Semantic Recognition for Modeling Environmental Ecosystems [56.0640340392818]
  We introduce a framework, FREE, that enables the use of varying features and available information to train a universal model.<n>The core idea is to map available environmental data into a text space and then convert the traditional predictive modeling task in environmental science to a semantic recognition problem.<n>Our evaluation on two societally important real-world applications, stream water temperature prediction and crop yield prediction, demonstrates the superiority of FREE over multiple baselines.
  arXiv  Detail & Related papers  (2023-11-17T00:53:09Z)
• Simulating News Recommendation Ecosystem for Fun and Profit [13.980779134063853]
  SimuLine is a simulation platform to dissect the evolution of news recommendation ecosystems. We analyze the characteristics of each evolutionary phase from the perspective of life-cycle theory.
  arXiv  Detail & Related papers  (2023-05-23T14:25:37Z)
• Ecosystem Graphs: The Social Footprint of Foundation Models [64.02855828418608]
  We propose Ecosystem Graphs as a documentation framework to transparently centralize knowledge of this ecosystem. Ecosystem Graphs is composed of assets (datasets, models, applications) linked together by dependencies that indicate technical (e.g. how Bing relies on GPT-4) and social (e.g. how Microsoft relies on OpenAI) relationships.
  arXiv  Detail & Related papers  (2023-03-28T07:18:29Z)
• A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning [54.55119659523629]
  Multi-agent reinforcement learning has recently shown great promise as an approach to networked system control. Common-pool resources include arable land, fresh water, wetlands, wildlife, fish stock, forests and the atmosphere.
  arXiv  Detail & Related papers  (2020-10-15T14:12:26Z)
• Modeling Stakeholder-centric Value Chain of Data to Understand Data Exchange Ecosystem [0.12891210250935145]
  We propose a model describing the stakeholder-centric value chain (SVC) of data by focusing on the relationships among stakeholders in data businesses. The SVC model enables the analysis and understanding of the structural characteristics of the data exchange ecosystem.
  arXiv  Detail & Related papers  (2020-05-22T05:04:08Z)
• Community-Based Service Ecosystem Evolution Analysis [4.770906657995415]
  Service ecosystem is a complex dynamic system with continuous evolution. Existing studies on service ecosystem evolution are more about facilitating programmers to use services. We present a method for analyzing service ecosystem evolution patterns from the perspective of the service community.
  arXiv  Detail & Related papers  (2020-05-06T11:00:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.