Dynamic Community Detection into Analyzing of Wildfires Events

• URL: http://arxiv.org/abs/2011.01140v1
• Date: Mon, 2 Nov 2020 17:31:47 GMT
• Title: Dynamic Community Detection into Analyzing of Wildfires Events
• Authors: Alessandra Marli, Didier A Vega-Oliveros, Mosh\'e Cotacallapa, Leonardo N Ferreira, Elbert EN Macau, Marcos G Quiles
• Abstract summary: We investigate the information that dynamic community structures reveal about the dynamics of wildfires. Experiments with the MODIS dataset of fire events in the Amazon basing were conducted. Our results show that the dynamic communities can reveal wildfire patterns observed throughout the year.
• Score: 55.72431452586636
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The study and comprehension of complex systems are crucial intellectual and scientific challenges of the 21st century. In this scenario, network science has emerged as a mathematical tool to support the study of such systems. Examples include environmental processes such as wildfires, which are known for their considerable impact on human life. However, there is a considerable lack of studies of wildfire from a network science perspective. Here, employing the chronological network concept -- a temporal network where nodes are linked if two consecutive events occur between them -- we investigate the information that dynamic community structures reveal about the wildfires' dynamics. Particularly, we explore a two-phase dynamic community detection approach, i.e., we applied the Louvain algorithm on a series of snapshots. Then we used the Jaccard similarity coefficient to match communities across adjacent snapshots. Experiments with the MODIS dataset of fire events in the Amazon basing were conducted. Our results show that the dynamic communities can reveal wildfire patterns observed throughout the year.

Related papers

• Hierarchical Information Enhancement Network for Cascade Prediction in Social Networks [51.54002032659713]
  We propose a novel Hierarchical Information Enhancement Network (HIENet) for cascade prediction. Our approach integrates fundamental cascade sequence, user social graphs, and sub-cascade graph into a unified framework.
  arXiv  Detail & Related papers  (2024-03-22T14:57:27Z)
• Evidential Temporal-aware Graph-based Social Event Detection via Dempster-Shafer Theory [76.4580340399321]
  We propose ETGNN, a novel Evidential Temporal-aware Graph Neural Network. We construct view-specific graphs whose nodes are the texts and edges are determined by several types of shared elements respectively. Considering the view-specific uncertainty, the representations of all views are converted into mass functions through evidential deep learning (EDL) neural networks.
  arXiv  Detail & Related papers  (2022-05-24T16:22:40Z)
• On-board Volcanic Eruption Detection through CNNs and Satellite Multispectral Imagery [59.442493247857755]
  Authors propose a first prototype and a study of feasibility for an AI model to be 'loaded' on board. As a case study, the authors decided to investigate the detection of volcanic eruptions as a method to swiftly produce alerts. Two Convolutional Neural Networks have been proposed and created, also showing how to correctly implement them on real hardware.
  arXiv  Detail & Related papers  (2021-06-29T11:52:43Z)
• A Comprehensive Survey on Community Detection with Deep Learning [93.40332347374712]
  A community reveals the features and connections of its members that are different from those in other communities in a network. This survey devises and proposes a new taxonomy covering different categories of the state-of-the-art methods. The main category, i.e., deep neural networks, is further divided into convolutional networks, graph attention networks, generative adversarial networks and autoencoders.
  arXiv  Detail & Related papers  (2021-05-26T14:37:07Z)
• Reconstructing shared dynamics with a deep neural network [0.0]
  We present a method to identify hidden shared dynamics from time series by a two- module, feedforward neural network architecture. The method has the potential to reveal hidden components of dynamical systems, where experimental intervention is not possible.
  arXiv  Detail & Related papers  (2021-05-05T20:55:53Z)
• Dynamic Network Embedding Survey [11.742863376032112]
  We give a survey of dynamic network embedding in this paper. We present two basic data models, namely, discrete model and continuous model for dynamic networks. We build a taxonomy that refines the category hierarchy by typical learning models.
  arXiv  Detail & Related papers  (2021-03-29T09:27:53Z)
• Convolutional LSTM Neural Networks for Modeling Wildland Fire Dynamics [0.0]
  We evaluate the feasibility of using a Convolutional Long Short-Term Memory recurrent neural network to model the dynamics of wildland fire propagation. We show that ConvLSTMs can capture local fire transmission events, as well as the overall fire dynamics, such as the rate at which the fire spreads.
  arXiv  Detail & Related papers  (2020-12-11T23:31:43Z)
• Laplacian Change Point Detection for Dynamic Graphs [10.556288610354997]
  We propose Laplacian Anomaly Detection (LAD) which uses the spectrum of the Laplacian matrix of the graph structure at each snapshot to obtain low dimensional embeddings. In synthetic experiments, LAD outperforms the state-of-the-art method. We also evaluate our method on three real dynamic networks: UCI message network, US senate co-sponsorship network and Canadian bill voting network.
  arXiv  Detail & Related papers  (2020-07-02T16:24:24Z)
• Deep Learning for Community Detection: Progress, Challenges and Opportunities [79.26787486888549]
  Article summarizes the contributions of the various frameworks, models, and algorithms in deep neural networks. This article summarizes the contributions of the various frameworks, models, and algorithms in deep neural networks.
  arXiv  Detail & Related papers  (2020-05-17T11:22:11Z)
• Community Detection Clustering via Gumbel Softmax [0.0]
  We propose a method of community detection clustering the nodes of various graph datasets. The deep learning role in modeling the interaction between nodes in a network allows a revolution in the field of science relevant to graph network analysis.
  arXiv  Detail & Related papers  (2020-05-05T17:55:31Z)

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.