The NOESIS Network-Oriented Exploration, Simulation, and Induction System
Network data mining has become an important area of study due to the large number of problems it can be applied to. This paper presents NOESIS, an open source framework for network data mining that provides a large collection of network analysis techniques, including the analysis of network structural properties, community detection methods, link scoring, and link prediction, as well as network visualization algorithms. It also features a complete stand-alone graphical user interface that facilitates the use of all these techniques. The NOESIS framework has been designed using solid object-oriented design principles and structured parallel programming. As a lightweight library with minimal external dependencies and a permissive software license, NOESIS can be incorporated into other software projects. Released under a BSD license, it is available from http://noesis.ikor.org.
Code (0)
등록된 구현이 없습니다.
Tasks
Community DetectionLink PredictionSimilar Papers 제목 키워드 기반
Automatic Knowledge Acquisition for Object-Oriented Expert Systems
We describe an Object Oriented Model for building Expert Systems. This model and the detection of similarities allow to implement reasoning modes as induction, deduction and simulation. We specially focus on similarity a…
ObjectPre-Trained and Attention-Based Neural Networks for Building Noetic Task-Oriented Dialogue Systems
The NOESIS II challenge, as the Track 2 of the 8th Dialogue System Technology Challenges (DSTC 8), is the extension of DSTC 7. This track incorporates new elements that are vital for the creation of a deployed task-orien…
Conversation DisentanglementTask-Oriented Dialogue SystemsNoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation
Large Language Models (LLM) are typically trained on vast amounts of data from various sources. Even when designed modularly (e.g., Mixture-of-Experts), LLMs can leak privacy on their sources. Conversely, training such m…
Code CompletionMixture-of-Expertsparameter-efficient fine-tuningTransfer LearningImprovement of Sliding Mode Control Strategy Founded on Cascaded Doubly Fed Induction Generator Powered by a Matrix Converter
The current paper presents a Sliding Mode Controller (SMC) for indirect field-oriented Cascaded Doubly Fed Induction Generator (CDFIG) powered through a Matrix Converter (MC). The proposed SMC employs a continuous contro…
continuous-controlContinuous ControlPoint TrackingDistributionally Robust Multi-Agent Reinforcement Learning for Dynamic Chute Mapping
In Amazon robotic warehouses, the destination-to-chute mapping problem is crucial for efficient package sorting. Often, however, this problem is complicated by uncertain and dynamic package induction rates, which can lea…
Multi-agent Reinforcement Learning