Search results for "Community detection"
showing 6 items of 16 documents
Structure and evolution of a European Parliament via a network and correlation analysis
2016
We present a study of the network of relationships among elected members of the Finnish parliament, based on a quantitative analysis of initiative co-signatures, and its evolution over 16 years. To understand the structure of the parliament, we constructed a statistically validated network of members, based on the similarity between the patterns of initiatives they signed. We looked for communities within the network and characterized them in terms of members' attributes, such as electoral district and party. To gain insight on the nested structure of communities, we constructed a hierarchical tree of members from the correlation matrix. Afterwards, we studied parliament dynamics yearly, wi…
An analysis of ego network communities and temporal a affinity for online social networks
2017
The wide diffusion of Online Social Networks (OSNs) presents several advantages, like the definition of simple tools for information sharing and spreading. However, OSNs present also some drawbacks, one of the most important one is the problem of privacy disclosures. Distributed Online Social Networks (DOSNs), which decentralize the control of the social network, have been recently proposed to overcome these issues. The decentralization of the control has issued several challenges, one of the main ones is guaranteeing data availability without relying on a central server. To define users’ data allocation strategies, the knowledge of the structure of the ego network and of the user’ temporal…
An Image Segmentation Algorithm based on Community Detection
2016
International audience; With the recent advances in complex networks, image segmentation becomes one of the most appropriate application areas. In this context, we propose in this paper a new perspective of image segmentation by applying two efficient community detection algorithms. By considering regions as communities, these methods can give an over-segmented image that has many small regions. So, the proposed algorithms are improved to automatically merge those neighboring regions agglomerative to achieve the highest modularity/stability. To produce sizable regions and detect homogeneous communities, we use the combination of a feature based on the Histogram of Oriented Gradients of the …
Community detection-based deep neural network architectures: A fully automated framework based on Likert-scale data
2020
[EN] Deep neural networks (DNNs) have emerged as a state-of-the-art tool in very different research fields due to its adaptive power to the decision space since they do not presuppose any linear relationship between data. Some of the main disadvantages of these trending models are that the choice of the network underlying architecture profoundly influences the performance of the model and that the architecture design requires prior knowledge of the field of study. The use of questionnaires is hugely extended in social/behavioral sciences. The main contribution of this work is to automate the process of a DNN architecture design by using an agglomerative hierarchical algorithm that mimics th…
Community detection of seismic point processes
2022
In this paper, we combine robin and Local Indicators of Spatio-Temporal Association (LISTA) functions. robin is an R package to assess the robustness of the community structure of a network found by one or more methods to give indications about their reliability. We use it to propose a classification algorithm of events in a spatio-temporal point pattern, by means of the local second-order characteristics and the community detection procedure in network analysis. We demonstrate the proposed procedure on a real data analysis on seismic data.
Intelligent solutions for real-life data-driven applications
2017
The subject of this thesis belongs to the topic of machine learning or, specifically, to the development of advanced methods for regression analysis, clustering, and anomaly detection. Industry is constantly seeking improved production practices and minimized production time and costs. In connection to this, several industrial case studies are presented in which mathematical models for predicting paper quality were proposed. The most important variables for the prediction models are selected based on information-theoretic measures and regression trees approach. The rest of the original papers are devoted to unsupervised machine learning. The main focus is developing advanced spectral cluster…