Language comparison via network topology

Modeling relations between languages can offer understanding of language characteristics and uncover similarities and differences between languages. Automated methods applied to large textual corpora can be seen as opportunities for novel statistical studies of language development over time, as well as for improving cross-lingual natural language processing techniques. In this work, we first propose how to represent textual data as a directed, weighted network by the text2net algorithm. We next explore how various fast, network-topological metrics, such as network community structure, can be used for cross-lingual comparisons. In our experiments, we employ eight different network topology metrics, and empirically showcase on a parallel corpus, how the methods can be used for modeling the relations between nine selected languages. We demonstrate that the proposed method scales to large corpora consisting of hundreds of thousands of aligned sentences on an of-the-shelf laptop. We observe that on the one hand properties such as communities, capture some of the known differences between the languages, while others can be seen as novel opportunities for linguistic studies

Mixed coordinate Node link Visualization for Co_authorship Hypergraph Networks

We present an algorithmic technique for visualizing the co-authorship networks and other networks modeled with hypergraphs (set systems). As more than two researchers can co-author a paper, a direct representation of the interaction of researchers through their joint works cannot be adequately modeled with direct links between the author-nodes. A hypergraph representation of a co-authorship network treats researchers/authors as nodes and papers as hyperedges (sets of authors). The visualization algorithm that we propose is based on one of the well-studied approaches representing both authors and papers as nodes of different classes. Our approach resembles some known ones like anchored maps but introduces some special techniques for optimizing the vertex positioning. The algorithm involves both continuous (force-directed) optimization and discrete optimization for determining the node coordinates. Moreover, one of the novelties of this work is classifying nodes and links using different colors. This usage has a meaningful purpose that helps the viewer to obtain valuable information from the visualization and increases the readability of the layout. The algorithm is tuned to enable the viewer to answer questions specific to co-authorship network studies.Comment: 10 pages, 3 figures, 1 tabl

Multi-layer local optima networks for the analysis of advanced local search-based algorithms

A Local Optima Network (LON) is a graph model that compresses the fitness landscape of a particular combinatorial optimization problem based on a specific neighborhood operator and a local search algorithm. Determining which and how landscape features affect the effectiveness of search algorithms is relevant for both predicting their performance and improving the design process. This paper proposes the concept of multi-layer LONs as well as a methodology to explore these models aiming at extracting metrics for fitness landscape analysis. Constructing such models, extracting and analyzing their metrics are the preliminary steps into the direction of extending the study on single neighborhood operator heuristics to more sophisticated ones that use multiple operators. Therefore, in the present paper we investigate a twolayer LON obtained from instances of a combinatorial problem using bitflip and swap operators. First, we enumerate instances of NK-landscape model and use the hill climbing heuristic to build the corresponding LONs. Then, using LON metrics, we analyze how efficiently the search might be when combining both strategies. The experiments show promising results and demonstrate the ability of multi-layer LONs to provide useful information that could be used for in metaheuristics based on multiple operators such as Variable Neighborhood Search.Comment: Accepted in GECCO202

Monitor, anticipate, respond, and learn: developing and interpreting a multilayer social network of resilience abilities

Resilient performance is influenced by social interactions of several types, which may be analysed as layers of interwoven networks. The combination of these layers gives rise to a “network of networks”, also known as a multilayer network. This study presents an approach to develop and interpret multilayer networks in light of resilience engineering. Layers correspond to the four abilities of resilient systems: monitor, anticipate, respond, and learn. The proposal is applied in a 34-bed intensive care unit. To map relationships between actors in each layer, a questionnaire was devised and answered by 133 staff members, including doctors, nurses, nurse technicians, and allied health professionals. Two multilayer networks were developed: one considering that actors are 100% available and reliable (work-as-imagined) and another considering suboptimal availability and reliability (work-as-done). The multilayer networks were analysed through actor-centred (Katz centrality, degree deviation, and neighbourhood centrality) and layer-centred metrics (inter-layer correlation, and assortativity correlation). Strengths and weaknesses of social interactions at the ICU are discussed based on the adopted metrics

Community Detection Methods in Multi-layer Networks

Táto práca sa zaoberá problematikou detekcie komunít na viacvrstvových sieťach. Cieľom tejto práce bola implementácia vybraných metód na detekciu komunít a ich následné použitie vo webovej aplikácie. Výsledkom práce je webová aplikácia umožňujúca analyzovať a vyhodnocovať viacvrstvové siete z pohľadu detekcie komunít.This thesis deals with the topic of community detection in multi-layered networks. The goal of this work was to implement selected community detection methods and its subsequent use in a web application. The result of this thesis is a web application, which enables to analyze and evaluate multi-layer networks from the perspective of communities.460 - Katedra informatikyvýborn