GRAPHNET: Network Science and Graph Analytics Platform

Short Summary

The GRAPHNET Network Science and Graph Analytics Platform provides dedicated computational resources and professional-grade software tools for modeling, analyzing, and visualizing complex networked systems. Built on high-performance multi-core CPU and GPU infrastructure, and equipped with a comprehensive suite of graph analytics libraries and graph database technologies, this testbed enables researchers and industry partners to study the structure, dynamics, and emergent properties of large-scale networks. Application domains include social network analysis, cybersecurity threat detection, biological network modeling, transportation systems, financial fraud detection, and knowledge graph construction. Mathematical graph theory and network science form foundational pillars of modern Artificial Intelligence, underpinning recommendation systems, graph neural networks (GNNs), and relational reasoning pipelines. By bridging classical network analysis with cutting-edge AI workflows, GRAPHNET supports the full analytics lifecycle from data ingestion and graph construction through to community detection, pathfinding, centrality analysis, and machine learning on graphs.

Keywords: Graph Analytics; Network Science; Graph Neural Networks; Complex Networks; Mathematical graph theory

Deeptech Area

Artificial Intelligence

Hosting Institution and PI Info

Name of Host Organization	NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa
Department or Lab	MagIC (Information Management Research Center) - the NOVA IMS research and development center
Name of Building	Manuel Vilares Building
Physical Address	Campus de Campolide, 1070-312 Lisboa
Website Links	https://www.novaims.unl.pt/
Institutional contact name	Cristina Oliveira
Institutional contact email	magic@novaims.unl.pt
Principal Investigator Name	Professor Ian James Scott
Position / institutional role	Assistant Professor
ORCID	0000-0001-9699-4473
Email	iscott@novaims.unl.pt
TestBed Responsible Name (if different from PI)
Funding source(s) for TestBed’s acquisition	This testbed benefits from the resources of the NOVA Data & Analytics Hub (NOVA DAH), hosted at NOVA Information Management School (NOVA IMS) of Universidade NOVA de Lisboa. The work is supported by national funds through FCT (Fundação para a Ciência e a Tecnologia) under project UID/04152/2025 (https://doi.org/10.54499/UID/04152/2025) (Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS); by the Plano de Recuperação e Resiliência (PRR) under projects UID/PRR/04152/2025 (https://doi.org/10.54499/UID/PRR/04152/2025) and EQUIPAR+2: UID/PRR2/04152/2025 (https://doi.org/10.54499/UID/PRR2/04152/2025); and by LISBOA2030 under project LISBOA2030-FEDER-01317500.

Application Domain

Manufacturing
Healthcare
Logistics
Agriculture
Maintenance & Inspection

Application Cases

Application case:	Short description:
Social Network Analysis and Community Detection	Apply centrality measures, clustering algorithms, and community detection techniques to map influence patterns, identify key actors, and detect echo chambers within large-scale social networks. This can support sociological research, political communication studies, and online misinformation monitoring.
Knowledge Graph Construction and Reasoning	Build and query domain-specific knowledge graphs connecting entities such as organizations, people, products, and events. Applications include intelligent search engines, semantic question answering systems, and AI-powered recommendation engines that leverage structured relational knowledge.
Financial Fraud Detection	Represent financial transactions as graph networks to identify suspicious patterns, money laundering rings, and fraudulent account clusters. Graph-based AI models can detect complex multi-hop fraud schemes invisible to conventional rule-based detection systems.
Transportation and Infrastructure Network Resilience	Analyze road networks, public transport systems, and critical infrastructure as graphs to identify bottlenecks, optimize routing, and assess resilience to disruptions. This supports urban planning, smart city initiatives, and emergency response optimization.

Potencial Stakeholders

Non-academic stakeholders	Industrial partners, SMEs, Startups, Government bodies, Professional associations, Public agencies and municipalities
Academic stakeholders	MSc students, PhD students, Researchers, Visiting researchers, Seconded researchers
Other types of stakeholders	R&I support professionals, R&I infrastructure operators, Innovation intermediaries, Technology transfer actors

Possible TRL and Exploitation Scenarios

TRL application range	4
Internal academic research	Yes
Collaborative research with external academic partners	Yes
Contract research / Proof-of-Concept for industry	Yes
Pilot / DeepTech Deployment in operational environment	No
Training services (courses, workshops, certification)	Yes
Service provision (testing, benchmarking, validation)	Yes
Open access for walk-in users (e.g. open days / hackathons)	No
Other (Secondments / sponsored access for visiting researchers under project-based or institutionally approved arrangements)	Yes

Formal access conditions and prerequisites

Type of contractual relationship	Academic partner	Industrial partner
No contract (direct access)	No	No
Direct contract between parties (e.g., research agreement)	Yes (See Note 1)	Yes (See Note 1)
Indirect contract between parties (e.g., project framework)	Yes (See Note 1)	Yes (See Note 1)

Note 1: All access is subject to terms and conditions.

Type of prerequisites	Description of prerequisites
Agreements	Confidentiality agreement for proprietary algorithms	In some cases (See Note 2)
	Data sharing agreement for datasets generated	In some cases (See Note 2)
	IP agreements	In some cases (See Note 2)
	Other	In some cases (See Note 3)
Insurance	Users must have appropriate liability coverage through their home institution	Yes

Note 2: Intellectual property, confidentiality, and exploitation conditions are governed by the applicable NOVA regulations, the CITADELS consortium framework, and any project- or service-specific agreements. Background IP remains with the original rightsholders. Foreground generated through collaborative or service activities will be managed according to the applicable contractual framework, including provisions on ownership, access rights, confidentiality, dissemination, and exploitation. Additional NDAs, data-processing agreements, or specific IP clauses may apply depending on the nature of the data, software, models, or other assets involved.

Note 3: Access is granted on a project-based or institutionally approved basis, subject to feasibility assessment, resource availability, compliance with data protection and security requirements, and acceptance of the applicable terms and conditions. Special arrangements may apply for CITADELS secondments and other approved visiting researcher schemes. Where sensitive, proprietary, or regulated assets are involved, additional safeguards may be required before access is enabled.

Training and Safety

Mandatory technical training	N/A
Recommended technical training	Recommended training on cluster operation, job submission, queuing in SLURM.
Mandatory safety requirements	N/A

Technical Components for the Testbed

Components:

Description:

Hardware

(physical equipment available in this TestBed)

1) NOVA DAH01 System Specifications:

a) CPU: 32-Core CPU - This processor provides a significant amount of processing power, enabling users to run multiple demanding tasks simultaneously, such as simulations, data processing, and other compute-intensive workloads.

b) GPU: 2 x Nvidia RTX 6000 ADA - These high-performance GPUs are designed to accelerate AI, HPC, and other GPU-accelerated workloads. With two RTX 6000 ADA GPUs, users can leverage massive parallel processing capabilities, handling large amounts of data and providing a substantial boost to performance.

c) Storage: 7TB NVMe Storage - This high-capacity storage solution provides rapid data access and transfer speeds, ideal for applications that require high-performance storage, such as data analytics, scientific simulations.

2) NOVA DAH02 System Specifications:

a) CPU: 112 CPU cores, providing a substantial amount of processing power for compute-intensive tasks. This will enable users to run multiple simulations, data processing, and other tasks concurrently.

b) GPU: 2 x Nvidia H100 NVL (Next-Generation High-Performance Computing) GPUs, which offer significant performance boosts for AI, HPC, and other GPU-accelerated workloads. The H100 NVL GPUs are designed to handle massive amounts of data and provide high-performance computing capabilities.

3) NOVA DAH WS includes:

a) 16 Lenovo Thinkstations P5 units, each equipped with an Intel(R) Xeon(R) W3-2423 processor, 32 GB DDRS-4800 MHz ECC memory, NVIDIA RTX(R) 2000 GPU with 16 GB GDDR6 (Ada Generation), and 1 TB PCIe Neg4 SSD.

b) Operating system Windows 11 Education,

c) Broad range of licensed and open-source software for data science, analytics, modelling, and visualisation, including but not limited to a broad range of licensed and open-source software for data science, analytics, modelling, and visualization, including but not limited to Python, R, Power BI, Tableau, SPSS, SAS, QGIS, ArcGIS, Docker, Anaconda, Visual Studio Code, and Zotero.

c) Storage: 500 TB of storage, providing ample space for storing large datasets, applications, and other data. This storage capacity will enable users to work with big data and store the results of their computations.

4) Others

Software

(needed to run
the TestBed)

1) SSH client

2) File transfer tools recommended

3) Apptainer runtime to test locally

Standards and regulations
(relevant for the safe and compliant operation of this TestBed)

N/A

Testbed Documentation

Type	Short description:
Tutorial	How to connect to NOVA DAH (Tutorial NOVA DAH01)
Tutorial	How to connect to NOVA DAH (Tutorial NOVA DAH02)

Ethical and Societal Aspects

Ethical and societal aspect:	Short description:
Combating Misinformation and Protecting Democratic Processes	Graph analytics can map the spread of misinformation across social networks, identifying coordinated inauthentic behavior, bot networks, and influence manipulation campaigns. For non-technical audiences, this means better tools to protect democratic elections, public health communication, and informed citizen participation from the distorting effects of algorithmically amplified false information.
Supporting Epidemiological Research and Public Health	Network science plays a critical role in modeling disease transmission through contact networks, enabling public health authorities to identify super-spreader nodes, optimize vaccination strategies, and predict outbreak trajectories. This directly contributes to faster and more effective responses to public health emergencies, protecting vulnerable populations.
Strengthening Critical Infrastructure Resilience	Graph-based resilience analysis of power grids, water distribution networks, and transportation systems helps identify vulnerabilities before they lead to catastrophic failures. For citizens, this means more reliable public services, reduced risk of large-scale infrastructure disruptions, and better-prepared emergency response systems.
Risk of Re-identification in Graph-Structured Data	Unlike tabular data, graph-structured data carries heightened re-identification risks, as the combination of network position, connection patterns, and node attributes can uniquely identify individuals even after anonymization. Users of GRAPHNET are encouraged to apply graph-specific privacy-preserving techniques such as differential privacy and graph perturbation methods to mitigate re-identification risks in compliance with GDPR principles of data minimization and purpose limitation.

Funding Source

This testbed benefits from the resources of the NOVA Data & Analytics Hub (NOVA DAH), hosted at NOVA Information Management School (NOVA IMS) of Universidade NOVA de Lisboa. The work is supported by national funds through FCT (Fundação para a Ciência e a Tecnologia) under project UID/04152/2025 (https://doi.org/10.54499/UID/04152/2025) (Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS); by the Plano de Recuperação e Resiliência (PRR) under projects UID/PRR/04152/2025 (https://doi.org/10.54499/UID/PRR/04152/2025) and EQUIPAR+2: UID/PRR2/04152/2025 (https://doi.org/10.54499/UID/PRR2/04152/2025); and by LISBOA2030 under project LISBOA2030-FEDER-01317500.

More info

(TBD)