Detecting Evidence of Organization in groups by Trajectories

• URL: http://arxiv.org/abs/2309.00172v1
• Date: Thu, 31 Aug 2023 23:57:02 GMT
• Title: Detecting Evidence of Organization in groups by Trajectories
• Authors: T. F. Silva and J. E. B. Maia
• Abstract summary: We introduce two new approaches to detect network structure inferences based on agent trajectories. To evaluate the effectiveness of the new approaches, we conducted experiments using four scenario simulations based on the animal kingdom.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Effective detection of organizations is essential for fighting crime and maintaining public safety, especially considering the limited human resources and tools to deal with each group that exhibits co-movement patterns. This paper focuses on solving the Network Structure Inference (NSI) challenge. Thus, we introduce two new approaches to detect network structure inferences based on agent trajectories. The first approach is based on the evaluation of graph entropy, while the second considers the quality of clustering indices. To evaluate the effectiveness of the new approaches, we conducted experiments using four scenario simulations based on the animal kingdom, available on the NetLogo platform: Ants, Wolf Sheep Predation, Flocking, and Ant Adaptation. Furthermore, we compare the results obtained with those of an approach previously proposed in the literature, applying all methods to simulations of the NetLogo platform. The results demonstrate that our new detection approaches can more clearly identify the inferences of organizations or networks in the simulated scenarios.

Related papers

• Cross-Inferential Networks for Source-free Unsupervised Domain Adaptation [17.718392065388503]
  We propose to explore a new method called cross-inferential networks (CIN) Our main idea is that, when we adapt the network model to predict the sample labels from encoded features, we use these prediction results to construct new training samples with derived labels. Our experimental results on benchmark datasets demonstrate that our proposed CIN approach can significantly improve the performance of source-free UDA.
  arXiv  Detail & Related papers  (2023-06-29T14:04:24Z)
• Improving Link Prediction in Social Networks Using Local and Global Features: A Clustering-based Approach [0.0]
  We propose an approach based on the combination of first and second group methods to tackle the link prediction problem. Our two-phase developed method firstly determines new features related to the position and dynamic behavior of nodes. Then, a subspace clustering algorithm is applied to group social objects based on the computed similarity measures.
  arXiv  Detail & Related papers  (2023-05-17T14:45:02Z)
• A Unified Framework for Exploratory Learning-Aided Community Detection Under Topological Uncertainty [16.280950663982107]
  META-CODE is a unified framework for detecting overlapping communities in social networks. It consists of three steps: 1) node-level community-affiliation embeddings based on graph neural networks (GNNs) trained by our new reconstruction loss, 2) network exploration via community-affiliation-based node queries, and 3) network inference using an edge connectivity-based Siamese neural network model from the explored network.
  arXiv  Detail & Related papers  (2023-04-10T10:22:21Z)
• Interpolation-based Correlation Reduction Network for Semi-Supervised Graph Learning [49.94816548023729]
  We propose a novel graph contrastive learning method, termed Interpolation-based Correlation Reduction Network (ICRN) In our method, we improve the discriminative capability of the latent feature by enlarging the margin of decision boundaries. By combining the two settings, we extract rich supervision information from both the abundant unlabeled nodes and the rare yet valuable labeled nodes for discnative representation learning.
  arXiv  Detail & Related papers  (2022-06-06T14:26:34Z)
• Large-Scale Sequential Learning for Recommender and Engineering Systems [91.3755431537592]
  In this thesis, we focus on the design of an automatic algorithms that provide personalized ranking by adapting to the current conditions. For the former, we propose novel algorithm called SAROS that take into account both kinds of feedback for learning over the sequence of interactions. The proposed idea of taking into account the neighbour lines shows statistically significant results in comparison with the initial approach for faults detection in power grid.
  arXiv  Detail & Related papers  (2022-05-13T21:09:41Z)
• Unsupervised Domain-adaptive Hash for Networks [81.49184987430333]
  Domain-adaptive hash learning has enjoyed considerable success in the computer vision community. We develop an unsupervised domain-adaptive hash learning method for networks, dubbed UDAH.
  arXiv  Detail & Related papers  (2021-08-20T12:09:38Z)
• Triggering Failures: Out-Of-Distribution detection by learning from local adversarial attacks in Semantic Segmentation [76.2621758731288]
  We tackle the detection of out-of-distribution (OOD) objects in semantic segmentation. Our main contribution is a new OOD detection architecture called ObsNet associated with a dedicated training scheme based on Local Adversarial Attacks (LAA) We show it obtains top performances both in speed and accuracy when compared to ten recent methods of the literature on three different datasets.
  arXiv  Detail & Related papers  (2021-08-03T17:09:56Z)
• Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning [50.24174211654775]
  We present a novel contrastive self-supervised learning framework for anomaly detection on attributed networks. Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair. A graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure.
  arXiv  Detail & Related papers  (2021-02-27T03:17:20Z)
• Multi-object Tracking with a Hierarchical Single-branch Network [31.680667324595557]
  We propose an online multi-object tracking framework based on a hierarchical single-branch network. Our novel iHOIM loss function unifies the objectives of the two sub-tasks and encourages better detection performance. Experimental results on MOT16 and MOT20 datasets show that we can achieve state-of-the-art tracking performance.
  arXiv  Detail & Related papers  (2021-01-06T12:14:58Z)
• Parallelization Techniques for Verifying Neural Networks [52.917845265248744]
  We introduce an algorithm based on the verification problem in an iterative manner and explore two partitioning strategies. We also introduce a highly parallelizable pre-processing algorithm that uses the neuron activation phases to simplify the neural network verification problems.
  arXiv  Detail & Related papers  (2020-04-17T20:21:47Z)

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.