Probing the statistical properties of enriched co-occurrence networks

• URL: http://arxiv.org/abs/2412.02664v1
• Date: Tue, 03 Dec 2024 18:38:14 GMT
• Title: Probing the statistical properties of enriched co-occurrence networks
• Authors: Diego R. Amancio, Jeaneth Machicao, Laura V. C. Quispe,
• Abstract summary: This study investigates two key statistical properties of text-based network models. We show that incorporating virtual edges can have positive and negative effects, depending on the specific network metric. Our results can serve as a guideline for determining which network metrics are most appropriate for specific applications.
• Score: 0.0
• License:
• Abstract: Recent studies have explored the addition of virtual edges to word co-occurrence networks using word embeddings to enhance graph representations, particularly for short texts. While these enriched networks have demonstrated some success, the impact of incorporating semantic edges into traditional co-occurrence networks remains uncertain. This study investigates two key statistical properties of text-based network models. First, we assess whether network metrics can effectively distinguish between meaningless and meaningful texts. Second, we analyze whether these metrics are more sensitive to syntactic or semantic aspects of the text. Our results show that incorporating virtual edges can have positive and negative effects, depending on the specific network metric. For instance, the informativeness of the average shortest path and closeness centrality improves in short texts, while the clustering coefficient's informativeness decreases as more virtual edges are added. Additionally, we found that including stopwords affects the statistical properties of enriched networks. Our results can serve as a guideline for determining which network metrics are most appropriate for specific applications, depending on the typical text size and the nature of the problem.

Related papers

• Improving Network Interpretability via Explanation Consistency Evaluation [56.14036428778861]
  We propose a framework that acquires more explainable activation heatmaps and simultaneously increase the model performance. Specifically, our framework introduces a new metric, i.e., explanation consistency, to reweight the training samples adaptively in model learning. Our framework then promotes the model learning by paying closer attention to those training samples with a high difference in explanations.
  arXiv  Detail & Related papers  (2024-08-08T17:20:08Z)
• Classification of vertices on social networks by multiple approaches [1.370151489527964]
  In the case of social networks, it is crucial to evaluate the labels of discrete communities. For each of these interaction-based entities, a social graph, a mailing dataset, and two citation sets are selected as the testbench repositories. This paper was not only assessed the most valuable method but also determined how graph neural networks work.
  arXiv  Detail & Related papers  (2023-01-13T09:42:55Z)
• Influencer Detection with Dynamic Graph Neural Networks [56.1837101824783]
  We investigate different dynamic Graph Neural Networks (GNNs) configurations for influencer detection. We show that using deep multi-head attention in GNN and encoding temporal attributes significantly improves performance.
  arXiv  Detail & Related papers  (2022-11-15T13:00:25Z)
• Affinity-Aware Graph Networks [9.888383815189176]
  Graph Neural Networks (GNNs) have emerged as a powerful technique for learning on relational data. We explore the use of affinity measures as features in graph neural networks. We propose message passing networks based on these features and evaluate their performance on a variety of node and graph property prediction tasks.
  arXiv  Detail & Related papers  (2022-06-23T18:51:35Z)
• TeKo: Text-Rich Graph Neural Networks with External Knowledge [75.91477450060808]
  We propose a novel text-rich graph neural network with external knowledge (TeKo) We first present a flexible heterogeneous semantic network that incorporates high-quality entities. We then introduce two types of external knowledge, that is, structured triplets and unstructured entity description.
  arXiv  Detail & Related papers  (2022-06-15T02:33:10Z)
• Neuro-Symbolic Artificial Intelligence (AI) for Intent based Semantic Communication [85.06664206117088]
  6G networks must consider semantics and effectiveness (at end-user) of the data transmission. NeSy AI is proposed as a pillar for learning causal structure behind the observed data. GFlowNet is leveraged for the first time in a wireless system to learn the probabilistic structure which generates the data.
  arXiv  Detail & Related papers  (2022-05-22T07:11:57Z)
• Using virtual edges to extract keywords from texts modeled as complex networks [0.1611401281366893]
  We modeled texts co-occurrence networks, where nodes are words and edges are established by contextual or semantical similarity. We found that, in fact, the use of virtual edges can improve the discriminability of co-occurrence networks.
  arXiv  Detail & Related papers  (2022-05-04T16:43:03Z)
• Be More with Less: Hypergraph Attention Networks for Inductive Text Classification [56.98218530073927]
  Graph neural networks (GNNs) have received increasing attention in the research community and demonstrated their promising results on this canonical task. Despite the success, their performance could be largely jeopardized in practice since they are unable to capture high-order interaction between words. We propose a principled model -- hypergraph attention networks (HyperGAT) which can obtain more expressive power with less computational consumption for text representation learning.
  arXiv  Detail & Related papers  (2020-11-01T00:21:59Z)
• Towards Accurate Scene Text Recognition with Semantic Reasoning Networks [52.86058031919856]
  We propose a novel end-to-end trainable framework named semantic reasoning network (SRN) for accurate scene text recognition. GSRM is introduced to capture global semantic context through multi-way parallel transmission. Results on 7 public benchmarks, including regular text, irregular text and non-Latin long text, verify the effectiveness and robustness of the proposed method.
  arXiv  Detail & Related papers  (2020-03-27T09:19:25Z)
• Using word embeddings to improve the discriminability of co-occurrence text networks [0.1611401281366893]
  We investigate whether the use of word embeddings as a tool to create virtual links in co-occurrence networks may improve the quality of classification systems. Our results revealed that the discriminability in the stylometry task is improved when using Glove, Word2Vec and FastText.
  arXiv  Detail & Related papers  (2020-03-13T13:35:44Z)

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.