Text2Net: Transforming Plain-text To A Dynamic Interactive Network Simulation Environment

• URL: http://arxiv.org/abs/2502.15754v1
• Date: Mon, 10 Feb 2025 23:45:57 GMT
• Title: Text2Net: Transforming Plain-text To A Dynamic Interactive Network Simulation Environment
• Authors: Alireza Marefat, Abbaas Alif Mohamed Nishar, Ashwin Ashok,
• Abstract summary: Text2Net is a text-based network simulation engine that transforms plain-text descriptions of network topologies into dynamic, interactive simulations.<n>By automating repetitive tasks and enabling intuitive interaction, Text2Net enhances accessibility for students, educators, and professionals.
• Score: 1.357291726431012
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces Text2Net, an innovative text-based network simulation engine that leverages natural language processing (NLP) and large language models (LLMs) to transform plain-text descriptions of network topologies into dynamic, interactive simulations. Text2Net simplifies the process of configuring network simulations, eliminating the need for users to master vendor-specific syntaxes or navigate complex graphical interfaces. Through qualitative and quantitative evaluations, we demonstrate Text2Net's ability to significantly reduce the time and effort required to deploy network scenarios compared to traditional simulators like EVE-NG. By automating repetitive tasks and enabling intuitive interaction, Text2Net enhances accessibility for students, educators, and professionals. The system facilitates hands-on learning experiences for students that bridge the gap between theoretical knowledge and practical application. The results showcase its scalability across various network complexities, marking a significant step toward revolutionizing network education and professional use cases, such as proof-of-concept testing.

Related papers

• Dynamic Bi-Elman Attention Networks: A Dual-Directional Context-Aware Test-Time Learning for Text Classification [17.33216148544084]
  This paper proposes the Dynamic Bidirectional Elman with Attention Network (DBEAN) DBEAN integrates bidirectional temporal modeling with self-attention mechanisms. It dynamically assigns weights to critical segments of input, improving contextual representation while maintaining computational efficiency.
  arXiv  Detail & Related papers  (2025-03-19T17:45:13Z)
• A Multimodal Framework for Topic Propagation Classification in Social Networks [2.189314262079322]
  This paper proposes a predictive model for topic dissemination in social networks. We introduce two novel indicators, user relationship breadth and user authority, into the PageRank algorithm. We refine the measurement of user interaction traces with topics, replacing traditional topic view metrics with a more precise communication characteristics measure.
  arXiv  Detail & Related papers  (2025-03-05T02:12:23Z)
• GeNet: A Multimodal LLM-Based Co-Pilot for Network Topology and Configuration [21.224554993149184]
  GeNet is a novel framework that leverages a large language model (LLM) to streamline network design. It uses visual and textual modalities to interpret and update network topologies and device configurations based on user intents.
  arXiv  Detail & Related papers  (2024-07-11T07:51:57Z)
• Leveraging advances in machine learning for the robust classification and interpretation of networks [0.0]
  Simulation approaches involve selecting a suitable network generative model such as Erd"os-R'enyi or small-world. We utilize advances in interpretable machine learning to classify simulated networks by our generative models based on various network attributes.
  arXiv  Detail & Related papers  (2024-03-20T00:24:23Z)
• Text2Data: Low-Resource Data Generation with Textual Control [100.5970757736845]
  Text2Data is a novel approach that utilizes unlabeled data to understand the underlying data distribution.<n>It undergoes finetuning via a novel constraint optimization-based learning objective that ensures controllability and effectively counteracts catastrophic forgetting.
  arXiv  Detail & Related papers  (2024-02-08T03:41:39Z)
• ERNetCL: A novel emotion recognition network in textual conversation based on curriculum learning strategy [37.41082775317849]
  We propose a novel emotion recognition network based on curriculum learning strategy (ERNetCL) The proposed ERNetCL primarily consists of temporal encoder (TE), spatial encoder (SE), and curriculum learning (CL) loss. Our proposed method is effective and dramatically beats other baseline models.
  arXiv  Detail & Related papers  (2023-08-12T03:05:44Z)
• The Whole Truth and Nothing But the Truth: Faithful and Controllable Dialogue Response Generation with Dataflow Transduction and Constrained Decoding [65.34601470417967]
  We describe a hybrid architecture for dialogue response generation that combines the strengths of neural language modeling and rule-based generation. Our experiments show that this system outperforms both rule-based and learned approaches in human evaluations of fluency, relevance, and truthfulness.
  arXiv  Detail & Related papers  (2022-09-16T09:00:49Z)
• Continual Learning, Fast and Slow [75.53144246169346]
  According to the Complementary Learning Systems theory, humans do effective emphcontinual learning through two complementary systems. We propose emphDualNets (for Dual Networks), a general continual learning framework comprising a fast learning system for supervised learning of specific tasks and a slow learning system for representation learning of task-agnostic general representation via Self-Supervised Learning (SSL) We demonstrate the promising results of DualNets on a wide range of continual learning protocols, ranging from the standard offline, task-aware setting to the challenging online, task-free scenario.
  arXiv  Detail & Related papers  (2022-09-06T10:48:45Z)
• TeKo: Text-Rich Graph Neural Networks with External Knowledge [75.91477450060808]
  We propose a novel text-rich graph neural network with external knowledge (TeKo) We first present a flexible heterogeneous semantic network that incorporates high-quality entities. We then introduce two types of external knowledge, that is, structured triplets and unstructured entity description.
  arXiv  Detail & Related papers  (2022-06-15T02:33:10Z)
• Vision-Language Pre-Training for Boosting Scene Text Detectors [57.08046351495244]
  We specifically adapt vision-language joint learning for scene text detection. We propose to learn contextualized, joint representations through vision-language pre-training. The pre-trained model is able to produce more informative representations with richer semantics.
  arXiv  Detail & Related papers  (2022-04-29T03:53:54Z)
• NetReAct: Interactive Learning for Network Summarization [60.18513812680714]
  We present NetReAct, a novel interactive network summarization algorithm which supports the visualization of networks induced by text corpora to perform sensemaking. We show how NetReAct is successful in generating high-quality summaries and visualizations that reveal hidden patterns better than other non-trivial baselines.
  arXiv  Detail & Related papers  (2020-12-22T03:56:26Z)

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.