Visual Analysis of Multilayer Networks

An improved gravity centrality for finding important nodes in multi-layer networks based on multi-PageRank

Topology analysis of Lanzhou public transport network based on double-layer complex network theory

Multilayer networks for measuring interconnectedness among global stock markets through the lens of trading volume-price relationship

Many real-world complex systems are well represented as multilayer networks; predicting interactions in those systems is one of the most pressing problems in predictive network science. To address this challenge, we introduce two stochastic block models for multilayer and temporal networks; one of them uses nodes as its fundamental unit, whereas the other focuses on links. We also develop scalable algorithms for inferring the parameters of these models. Because our models describe all layers simultaneously, our approach takes full advantage of the information contained in the whole network when making predictions about any particular layer. We illustrate the potential of our approach by analyzing two empirical data sets: a temporal network of e-mail communications, and a network of drug interactions for treating different cancer types. We find that multilayer models consistently outperform their single-layer counterparts, but that the most predictive model depends on the data set under consideration; whereas the node-based model is more appropriate for predicting drug interactions, the link-based model is more appropriate for predicting e-mail communication.

BackgroundThe Entity Linking (EL) task links entity mentions from an unstructured document to entities in a knowledge base. Although this problem is well-studied in news and social media, this problem has not received much attention in the life science domain. One outcome of tackling the EL problem in the life sciences domain is to enable scientists to build computational models of biological processes with more efficiency. However, simply applying a news-trained entity linker produces inadequate results.MethodsSince existing supervised approaches require a large amount of manually-labeled training data, which is currently unavailable for the life science domain, we propose a novel unsupervised collective inference approach to link entities from unstructured full texts of biomedical literature to 300 ontologies. The approach leverages the rich semantic information and structures in ontologies for similarity computation and entity ranking.ResultsWithout using any manual annotation, our approach significantly outperforms state-of-the-art supervised EL method (9% absolute gain in linking accuracy). Furthermore, the state-of-the-art supervised EL method requires 15,000 manually annotated entity mentions for training. These promising results establish a benchmark for the EL task in the life science domain. We also provide in depth analysis and discussion on both challenges and opportunities on automatic knowledge enrichment for scientific literature.ConclusionsIn this paper, we propose a novel unsupervised collective inference approach to address the EL problem in a new domain. We show that our unsupervised approach is able to outperform a current state-of-the-art supervised approach that has been trained with a large amount of manually labeled data. Life science presents an underrepresented domain for applying EL techniques. By providing a small benchmark data set and identifying opportunities, we hope to stimulate discussions across natural language processing and bioinformatics and motivate others to develop techniques for this largely untapped domain.

Complex networks, such as transportation networks, social networks, or biological networks, capture the complex system they model by often representing only one type of interactions. In real world systems, there may be many different aspects that connect entities together. These can be captured using multilayer networks, which combine different modalities of interactions in a single model. Coupling in multilayer networks may exhibit different properties which can be related to the very nature of the data they model (or to events in time-dependent data). We hypothesise that such properties may be reflected in the way layers are intertwined. In this paper, we investigated these through the prism of layer entanglement in coupled multilayer networks. We test over 30 real-life networks in 6 different disciplines (social, genetic, transport, co-authorship, trade, and neuronal networks). We further propose a random generator, displaying comparable patterns of elementary layer entanglement and transition coupling entanglement across 1,329,696 synthetic coupled multilayer networks. Our experiments demonstrate difference of layer entanglement across disciplines, and even suggest a link between entanglement intensity and homophily. We additionally study entanglement in 3 real world temporal datasets displaying a potential rise in entanglement activity prior to other network activity.

Synchronizability of multi-layer small-world dynamical networks

Bio-zen plus is an OWL DL ontology for the domain of biomedical research. It incorporates several existing Semantic Web ontologies: the DOLCE foundational ontology, the Simple Knowledge Organisation System (SKOS), the Semantically Interlinked Online Communities ontology (SIOC), FOAF ontology, the Dublin Core metadata schema and the Creative Commons Metadata schema. The contents of many biomedical taxonomies and ontologies are available as extension packages. Bio-zen plus is the first ontology in the domain of life sciences that is optimized for practical use on the Semantic Web and makes extensive use of existing Semantic Web ontologies.

The distinction proposed by Ernst Mayr and François Jacob between functional biology and evolutionary biology is a fundamental key for a clear and conclusive treatment of two problems that, recurrently, are placed in the field of the Philosophy of Biology: that which concerns the possible character teleological of biological explanations and that relative to the autonomy of Biology in relation to Physics and Chemistry. Without supporting an alternative solution for any of these problems, we present that opposition showing its relevance for the treatment of such issues. Our idea is that they should not be raised without specifying which of the two domains of life sciences we are referring to.

International cooperation beyond borders, institutions, and intergenerationally is an important aspect of science and research-based learning. Timing of learning also matters. Early exposure to group research-based learning can potentially have lasting positive impacts on youth and their careers in life sciences. Here, we report our work on the International Group Project (IGP), which builds on the International Biology Olympiad (IBO) organized in Yerevan, Armenia, in 2022. The IBO is an annual international competition for high school students held since 1990 around the world. We envisioned the IGP as a novel opportunity for life sciences research-based education among youth. We formed diverse IGP research teams 2 months before the IBO, and comprised high school students from 32 countries, communicating in a digital environment via videoconferencing. Each team formulated a research question in an IGP theme from five domains of life sciences: "Biomedicine," "Molecular and cell biology," "Bioinformatics and Artificial Intelligence," "Bionics and Biomimicry," "Across Species." Subsequently, team members collectively solved their research question by applying life sciences methodologies under supervision from a facilitator scientist. Each team created a poster based on their research and presented in-person to the public at a satellite activity at the IBO. A special subcommittee of the IBO International Jury graded posters and allocated prizes based on scientific ingenuity and presentation quality. This experience from the IGP lends evidence to the feasibility of research-based learning in life sciences for high school youth beyond borders. Moving research-based learning upstream and internationally is well poised to advance 21st century life sciences from both interdisciplinary and intergenerational standpoints. The historic impact of the COVID-19 pandemic suggests that youth engagement in research-based learning and innovation in life sciences is timely.

Multilayer information spillover networks analysis of China’s financial institutions based on variance decompositions

The influence of average speed ratio on multilayer traffic network

The introduction of 5G technologies has enabled the possibility of designing and building several new classes of networked information systems that were previously impossible to implement due to limitations on data throughput or the reliability of transmission channels. Among them, one of the most interesting and successful examples with a highly positive impact in terms of the quality of urban environments and societal and economical welfare is a system of semi-autonomous connected vehicles, where IoT devices, data centers, and fleets of smart vehicles equipped with communication and computational resources are combined into a heterogeneous and distributed infrastructure, unifying hardware, networks, and software. In order to efficiently provide various services (e.g., patrolling, pickup and delivery, monitoring), these systems typically rely on collecting and broadcasting large amounts of data (e.g., sensor data, GPS traces, or maps), which need to be properly collected and processed in a timely manner. As is well documented in the literature, one of the most effective ways to achieve this purpose, especially in a real-time context, is to adopt a graph model of the data (e.g., to model communication networks, roads, or interactions between vehicles) and to employ suitable graph algorithms to solve properly defined computational problems of interest (e.g., shortest paths or distributed consensus). While research in this context has been extensive from a theoretical perspective, works that have focused on the implementation, deployment, and evaluation of the practical performance of graph algorithms for real-world systems of autonomous vehicles have been much rarer. In this paper, we present a study of this kind. Specifically, we first describe the main features of a real-world information system employing semi-autonomous connected vehicles that is currently being tested in the city of L’Aquila (Italy). Then, we present an overview of the computational challenges arising in the considered application domain and provide a systematic survey of known algorithmic results for one of the most relevant classes of computational problems that have to be addressed in said domain, namely, pickup and delivery problems. Finally, we discuss implementation issues, adopted software tools, and the deployment and testing phases concerning one of the algorithmic components of the mentioned real-world system dedicated to handling a specific problem of the above class, namely, the pickup and delivery multi-vehicle problem with time windows.

Cohesive subgraph detection is an important graph problem that is widely applied in many application domains, such as social community detection, network visualization, and network topology analysis. Most of existing cohesive subgraph metrics can guarantee good structural properties but may cause the free-rider effect. Here, by free-rider effect, we mean that some irrelevant subgraphs are combined as one subgraph if they only share a small number of vertices and edges. In this paper, we study k-vertex connected component (k-VCC) which can effectively eliminate the free-rider effect but less studied in the literature. A k-VCC is a connected subgraph in which the removal of any k-1 vertices will not disconnect the subgraph. In addition to eliminating the free-rider effect, k-VCC also has other advantages such as bounded diameter, high cohesiveness, bounded graph overlapping, and bounded subgraph number. We propose a polynomial time algorithm to enumerate all k-VCCs of a graph by recursively partitioning the graph into overlapped subgraphs. We find that the key to improving the algorithm is reducing the number of local connectivity testings. Therefore, we propose two effective optimization strategies, namely neighbor sweep and group sweep, to largely reduce the number of local connectivity testings. We conduct extensive performance studies using seven large real datasets to demonstrate the effectiveness of this model as well as the efficiency of our proposed algorithms.

Although the digital humanities have traditionally been conceived as a text-based discipline, both digital visualization techniques as well as visual analysis are increasingly used for research in various humanities disciplines. Since there are several overlaps in epistemic cultures of visually oriented and digitally supported research in art and architectural history studies, museology, and archaeology, as well as cultural heritage, we introduce ‘visual digital humanities’ as novel ‘umbrella’ term to cover research approaches in the digital humanities that are dependent on both consuming and producing pictorial, rather than textual, information to answer their humanities research questions. This article aims to determine this particular field of research in terms of (1) research topics, (2) disciplinary standards, and (3) a scholarly culture as well as (4) researchers’ habits and backgrounds. This study is intended to highlight a scope of phenomena and aspects of relevance. Information is gathered by interviews with researchers at London universities and workshops held in Germany and Sweden.