Learning the Markov order of paths in a network

• URL: http://arxiv.org/abs/2007.02861v1
• Date: Mon, 6 Jul 2020 16:27:02 GMT
• Title: Learning the Markov order of paths in a network
• Authors: Luka V. Petrovi\'c and Ingo Scholtes
• Abstract summary: We study the problem of learning the Markov order in categorical sequences that represent paths in a network. Adopting a multi-order modelling framework for paths, we develop a Bayesian learning technique that more reliably detects the correct Markov order. Our work is further relevant for the growing body of research that emphasizes the need for higher-order models in network analysis.
• Score: 1.5229257192293197
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study the problem of learning the Markov order in categorical sequences that represent paths in a network, i.e. sequences of variable lengths where transitions between states are constrained to a known graph. Such data pose challenges for standard Markov order detection methods and demand modelling techniques that explicitly account for the graph constraint. Adopting a multi-order modelling framework for paths, we develop a Bayesian learning technique that (i) more reliably detects the correct Markov order compared to a competing method based on the likelihood ratio test, (ii) requires considerably less data compared to methods using AIC or BIC, and (iii) is robust against partial knowledge of the underlying constraints. We further show that a recently published method that uses a likelihood ratio test has a tendency to overfit the true Markov order of paths, which is not the case for our Bayesian technique. Our method is important for data scientists analyzing patterns in categorical sequence data that are subject to (partially) known constraints, e.g. sequences with forbidden words, mobility trajectories and click stream data, or sequence data in bioinformatics. Addressing the key challenge of model selection, our work is further relevant for the growing body of research that emphasizes the need for higher-order models in network analysis.

Related papers

• Mitigating Label Noise on Graph via Topological Sample Selection [72.86862597508077]
  We propose a $textitTopological Sample Selection$ (TSS) method that boosts the informative sample selection process in a graph by utilising topological information. We theoretically prove that our procedure minimizes an upper bound of the expected risk under target clean distribution, and experimentally show the superiority of our method compared with state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-03-04T11:24:51Z)
• Scalable Structure Learning for Sparse Context-Specific Systems [0.0]
  We present an algorithm for learning context-specific models that scales to hundreds of variables. Our method is shown to perform well on synthetic data and real world examples.
  arXiv  Detail & Related papers  (2024-02-12T16:28:52Z)
• Bayesian Graph Contrastive Learning [55.36652660268726]
  We propose a novel perspective of graph contrastive learning methods showing random augmentations leads to encoders. Our proposed method represents each node by a distribution in the latent space in contrast to existing techniques which embed each node to a deterministic vector. We show a considerable improvement in performance compared to existing state-of-the-art methods on several benchmark datasets.
  arXiv  Detail & Related papers  (2021-12-15T01:45:32Z)
• Bayesian graph convolutional neural networks via tempered MCMC [0.41998444721319217]
  Deep learning models, such as convolutional neural networks, have long been applied to image and multi-media tasks. More recently, there has been more attention to unstructured data that can be represented via graphs. These types of data are often found in health and medicine, social networks, and research data repositories.
  arXiv  Detail & Related papers  (2021-04-17T04:03:25Z)
• Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation [37.742218733235084]
  We propose a novel graph-based active learning algorithm for the task of node classification in attributed graphs. Our algorithm uses graph cognizant logistic regression, equivalent to a linearized graph convolutional neural network (GCN) for the prediction phase and maximizes the expected error reduction in the query phase. We conduct experiments on five public benchmark datasets, demonstrating a significant improvement over state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-07-09T18:00:53Z)
• Semi-Supervised Learning with Meta-Gradient [123.26748223837802]
  We propose a simple yet effective meta-learning algorithm in semi-supervised learning. We find that the proposed algorithm performs favorably against state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-07-08T08:48:56Z)
• Learning while Respecting Privacy and Robustness to Distributional Uncertainties and Adversarial Data [66.78671826743884]
  The distributionally robust optimization framework is considered for training a parametric model. The objective is to endow the trained model with robustness against adversarially manipulated input data. Proposed algorithms offer robustness with little overhead.
  arXiv  Detail & Related papers  (2020-07-07T18:25:25Z)
• Learned Factor Graphs for Inference from Stationary Time Sequences [107.63351413549992]
  We propose a framework that combines model-based algorithms and data-driven ML tools for stationary time sequences. neural networks are developed to separately learn specific components of a factor graph describing the distribution of the time sequence. We present an inference algorithm based on learned stationary factor graphs, which learns to implement the sum-product scheme from labeled data.
  arXiv  Detail & Related papers  (2020-06-05T07:06:19Z)
• Document Ranking with a Pretrained Sequence-to-Sequence Model [56.44269917346376]
  We show how a sequence-to-sequence model can be trained to generate relevance labels as "target words" Our approach significantly outperforms an encoder-only model in a data-poor regime.
  arXiv  Detail & Related papers  (2020-03-14T22:29:50Z)

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.