A5003377833- Complex Network Analysis Techniques
- Data Visualization and Analytics
- Computational Geometry and Mesh Generation
- Opinion Dynamics and Social Influence
- Data Management and Algorithms
- Graph Theory and Algorithms
- Advanced Graph Theory Research
- Topological and Geometric Data Analysis
- Graph theory and applications
- Geographic Information Systems Studies
- Wikis in Education and Collaboration
- Advanced Graph Neural Networks
- Social Media and Politics
- Complexity and Algorithms in Graphs
- Computer Graphics and Visualization Techniques
- Archaeology and ancient environmental studies
- Constraint Satisfaction and Optimization
- Sports Analytics and Performance
- Advanced Clustering Algorithms Research
- Interconnection Networks and Systems
- Video Analysis and Summarization
- Social Capital and Networks
- 3D Modeling in Geospatial Applications
- Bioinformatics and Genomic Networks
- Multimedia Communication and Technology
ETH Zurich
2018-2025
University of Groningen
2012-2023
Zurich University of Teacher Education
2023
Karlsruhe University of Education
2000-2023
University of Potsdam
2023
University of Stuttgart
2023
Goethe University Frankfurt
2023
Berlin Heart (Germany)
2023
TU Dortmund University
2023
University of Arizona
2016-2019
Motivated by the fast‐growing need to compute centrality indices on large, yet very sparse, networks, new algorithms for betweenness are introduced in this paper. They require O(n + m) space and run O(nm) O(nm n2 log n) time unweighted weighted respectively, where m is number of links. Experimental evidence provided that substantially increases range networks which analysis feasible. The index essential social but costly compute. Currently, fastest known ?(n 3) 2) space, n actors network.
Modularity is a recently introduced quality measure for graph clusterings. It has immediately received considerable attention in several disciplines, particularly the complex systems literature, although its properties are not well understood. We study problem of finding clusterings with maximum modularity, thus providing theoretical foundations past and present work based on this measure. More precisely, we prove conjectured hardness maximizing modularity both general case restriction to...
Centrality indices are an essential concept in network analysis. For those based on shortest-path distances the computation is at least quadratic number of nodes, since it usually involves solving single-source shortest-paths (SSSP) problem from every node. Therefore, exact infeasible for many large networks interest today. scores can be estimated, however, a limited SSSP computations. We present results experimental study quality such estimates under various selection strategies source vertices.
Abstract This is the beginning of Network Science . The journal has been created because network science exploding. As typical for a field in formation, discussions about its scope, contents, and foundations are intense. On these first few pages issue our new journal, we would like to share own vision emerging networks.
Random networks are frequently generated, for example, to investigate the effects of model parameters on network properties or test performance algorithms. Recent interest in statistics large-scale sparked a growing demand generators that can generate large numbers quickly. We here present simple and efficient algorithms randomly according most commonly used models. Their running time space requirement is linear size they easily implemented.
Several algorithms have been proposed to compute partitions of networks into communities that score high on a graph clustering index called modularity. While publications these typically contain experimental evaluations emphasize the plausibility results, none has shown actually optimal partitions. We here settle unknown complexity status modularity maximization by showing corresponding decision version is NP-complete in strong sense. As consequence, any efficient, i.e. polynomial-time,...
In this paper we give models and algorithms to describe analyze the collaboration among authors of Wikipedia from a network analytical perspective.The edit encodes who interacts how with whom when editing an article; it significantly extends previous that code author communities in Wikipedia.Several characteristics summarizing some aspects organization process allowing analyst identify certain types can be obtained network.Moreover, propose several indicators characterizing global structure...
With few exceptions, statistical analysis of social networks is currently focused on cross-sectional or panel data. On the other hand, automated collection network-data often produces event data, i.e., data encoding exact time interaction between actors. In this paper we propose models and methods to analyze such dyadic events determine factors that influence frequency quality interaction. We apply our empirical datasets about political conflicts test several hypotheses concerning...
While network analysis is a major methodological approach in many disciplines of the social and natural sciences, it has only recently come into focus sport researchers. This article assesses utility to analyze phenomena. We begin with an overview (SNA) related concepts. To explore research topics approaches, we conduct systematic review empirical literature SNA its application sport. Based on this review, provide six-dimensional conceptual typology applications – competition networks,...
Abstract The visualization community has developed to date many intuitions and understandings of how judge the quality views in visualizing data. computation a visualization's usefulness ranges from measuring clutter overlap, up existence perception specific (visual) patterns. This survey attempts report, categorize unify diverse aims establish common vocabulary that will enable wide audience understand their differences subtleties. For this purpose, we present commonly applicable metric...
We introduce a method for visualizing evolving networks. In addition to the intermediate states of network, it conveys nature change between by unrolling dynamics network. Each modification is shown in separate layer three-dimensional representation, where stack layers corresponds time line evolution. focus on networks dynamic discourse as driving application, but extends any type similar ways.
We propose a technique that allows straight-line graph drawings to be rendered interactively with adjustable level of detail. The approach consists novel combination edge cumulation density-based node aggregation and is designed exploit common graphics hardware for speed. It operates directly on data does not require precomputed hierarchies or meshes. As proof concept, we present an implementation scales graphs millions nodes edges, discuss several example applications.