Abstract The Mapper Algorithm is a powerful tool for representing the topology of a dataset’s structure as a similarity graph for the purposes of exploratory analysis. Despite Mapper’s ability to simplify complex high-dimensional data representations, interpreting the structure of the output graph remains a challenge. The conventional method of interpreting the Mapper graph by coloring nodes by features values is infeasible for high-dimensional data due to time limitations and the potential for subjectivity-related oversights. We present a novel method to enhance the interpretability of the Mapper algorithm. Specifically, we propose adapting eXplainable Artificial Intelligence techniques to determine feature importance, offering both local and global interpretations. Our approach can be used to assist domain experts in understanding functional differences across Mapper graphs, enabling them to draw meaningful conclusions from the graph’s structure. To validate our approach, we conducted experiments on five real-world medical datasets and the MNIST handwritten digit dataset. Our evaluation methods consist of a combination of visualization, classification tasks, and alignment of interpretations to existing literature. The results demonstrate our method’s effectiveness in providing a means to interpret Mapper graphs by highlighting the roles of specific features in the graph—such as pixel regions in MNIST and genes in TCGA datasets.

Topological methods have recently gained traction as powerful tools for extracting insights from high-dimensional data, forming the foundation of an approach known as Topological Data Analysis (TDA). Among the key developments in TDA is the Mapper algorithm, which constructs graph-based representations of complex datasets, capturing their topological structure at a user-defined resolution. The Mapper algorithm has shown promise across various applications, particularly in biomedical data analysis. However, its application requires careful selection of several parameters, especially the clustering algorithm and its settings. Without prior knowledge and a deep understanding of the data, these choices are non-trivial and can be a major barrier for researchers aiming to leverage Mapper effectively. In this work, we introduce enhancements to the Mapper algorithm to address this challenge. Specifically, we investigate the integration of ensemble learning (EL) techniques into Mapper’s graph construction to eliminate the need for arbitrary parameter selection. Additionally, we propose a data-driven criterion for selecting the clustering method best suited to the Mapper algorithm. Our experimental results demonstrate that the proposed approach enables the construction of Mapper graphs that accurately capture the underlying structure of the input data, all without manual parameter tuning.

Data integration is an important component of Big Data analytics. One of the key challenges in data integration is record linkage, that is, matching records that represent the same real-world entity. Because of computational costs, methods referred to as blocking are employed as a part of the record linkage pipeline in order to reduce the number of comparisons among records. In the past decade, a range of blocking techniques have been proposed. Real-world applications require approaches that can handle heterogeneous data sources and do not rely on labelled data. We propose high-value token-blocking (HVTB), a simple and efficient approach for blocking that is unsupervised and schema-agnostic, based on a crafted use of Term Frequency-Inverse Document Frequency. We compare HVTB with multiple methods and over a range of datasets, including a novel unstructured dataset composed of titles and abstracts of scientific papers. We thoroughly discuss results in terms of accuracy, use of computational resources, and different characteristics of datasets and records. The simplicity of HVTB yields fast computations and does not harm its accuracy when compared with existing approaches. It is shown to be significantly superior to other methods, suggesting that simpler methods for blocking should be considered before resorting to more sophisticated methods.

False information in the domain of online health related articles is of great concern, which can be witnessed in the current pandemic situation of Covid-19. It is markedly different from fake news in the political context as health information should be evaluated against the most recent and reliable medical resources such as scholarly repositories. However, one of the challenges with such an approach is the retrieval of the pertinent resources. In this work, we formulate a new unsupervised task of generating queries using keywords extracted from a health-related article which can be further applied to retrieve relevant authoritative and reliable medical content from scholarly repositories to assess the article’s veracity. We propose a three-step approach for it and illustrate that our method is able to generate effective queries. We also curate a new dataset to aid the evaluation for this task which will be made available upon request.

Disinformation is often presented in long textual articles, especially when it relates to domains such as health, often seen in relation to COVID-19. These articles are typically observed to have a number of trustworthy sentences among which core disinformation sentences are scattered. In this paper, we propose a novel unsupervised task of identifying sentences containing key disinformation within a document that is known to be untrustworthy. We design a three-phase statistical NLP solution for the task which starts with embedding sentences within a bespoke feature space designed for the task. Sentences represented using those features are then clustered, following which the key sentences are identified through proximity scoring. We also curate a new dataset with sentence level disinformation scorings to aid evaluation for this task; the dataset is being made publicly available to facilitate further research. Based on a comprehensive empirical evaluation against techniques from related tasks such as claim detection and summarization, as well as against simplified variants of our proposed approach, we illustrate that our method is able to identify core disinformation effectively.

Data integration is one of the key problems in the era of Big Data analytics. The key challenge of data integration is the identification of records representing the same entities (e.g. person). This task is referred to as Record Linkage. It is uncommon for different data sources to share a unique identifier hence the records must be matched by comparing their corresponding values. Most of the existing methods assume that records across different sources are structured and represented by the same set of attributes (e.g. name, date of birth). However, nowadays majority of the data comes without structure (e.g. social media sites). We propose a new approach to Record Linkage based on application of Siamese Neural Network. The model can be applied with structured, semi-structured and unstructured records and it does not assume a common format across different data sources. We demonstrate that the model performs on par with other approaches, which make constraining assumptions regarding the data.

One of the most popular techniques of generating classifier ensembles is known as stacking which is based on a meta-learning approach. In this paper, we introduce an alternative method to stacking which is based on cluster analysis. Similar to stacking, instances from a validation set are initially classified by all base classifiers. The output of each classifier is subsequently considered as a new attribute of the instance. Following this, a validation set is divided into clusters according to the new attributes and a small subset of the original attributes of the instances. For each cluster, we find its centroid and calculate its class label. The collection of centroids is considered as a meta-classifier. Experimental results show that the new method outperformed all benchmark methods, namely Majority Voting, Stacking J48, Stacking LR, AdaBoost J48, and Random Forest, in 12 out of 22 data sets. The proposed method has two advantageous properties: it is very robust to relatively small training sets and it can be applied in semi-supervised learning problems. We provide a theoretical investigation regarding the proposed method. This demonstrates that for the method to be successful, the base classifiers applied in the ensemble should have greater than 50% accuracy levels.