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Graph Entropy Measures in Publication Network Data Andreas - - PowerPoint PPT Presentation
Graph Entropy Measures in Publication Network Data Andreas - - PowerPoint PPT Presentation
Graph Entropy Measures in Publication Network Data Andreas Holzinger Bernhard Ofner Christof Stocker Andr Calero Valdez Anne Kathrin Schaar Martina Ziefle Matthias Dehmer page 1 Agenda 1 Overview CSP1 - Interdisciplinary Innovation
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Research Background
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Disciplinary backgrounds and methodologies of the CSP1
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Research Questions
n How can one measure scientific success in interdisciplinary teams? n Which means can support interdisciplinary cooperation? n And how does the cybernetic approach apply for management of interdisciplinary
success?
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CSP1-fields of action and research questions
Interdisciplinary Innovation Management Diversity Management Performance Measurement Knowledge & Cooperation Engineering
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Cluster-App Vision 3 Publication Network Visualization 2 Overview CSP1 – Interdisciplinary Innovation Management 1
Agenda
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What is scientific success?
n The DFG has issued criteria for research clusters: – Blind Peer Reviewed Publication § Impact Factor – Completed Dissertations – Acquired third party funding n We focus on publications – Publically available information – Reflect scientific cooperation – Reveal interdisciplinarity n How can we find interdisciplinarity in publication data at
the micro level?
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Publication Network Visualization
n Network Graph (Nodes and Edges) n 2 Type of Nodes – Author (A) – Publication (P) n 1 Type of Edge for a Relationship – isAuthorOf (connects A and P) n Force Atlas Layout – Two nodes attract each other, if they are connected by an edge. – Two nodes repel each other, if they are not connected by an edge. => Visual grouping of nodes according to „relationship-nearness“
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Cluster publication vizualisation
n How can we interpret this graph? n Measures of centrality – Grayness codes for degree centrality – Node Size codes for betweeness centrality n Centrality gives node based information n What about subgraphs and total graph
properties?
n Graph-Entropy measures the amount of
information coded by a graph
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What is Graph Entropy?
n Entropy amount of uncertainty – Empty graphs and complete graphs have low entropy n Various interpretations exist – Shannon Entropy can not be uniquely mapped n 2 major variants exist – Invariant based Entropy measures § Partition according to an invariant (e.g. degree) § Measure probability of vertex being in invariant-class – Information functionals (f) § Every vertex is assigned a real number (according to the functional): f(vi) § From the sum of those number a probability is derived pf(vi) § => Entropy (benefit: calculable in polynomial time)
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Evaluation of the Cluster Network
n Different results for different entropies n Imowsh is based on graph automorphism – measures symmetry – Max I(G)=9.6366 (=log2(|V|) – Graph is very unsymmetrical (globally) n Idehm is based on vertex neighborhood diversity – Measures asymmetry – Max I(G)=9.6366 – Graph is very symmetrical (locally)
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Interdisciplinary Innovation Management
n Research approach for the CSP1 n Idea from cognitive psychology/computer science – The quantified self – Self-measurement as a self-management tool n Measuring and visualizing success factors leads to a
better and awarer handling of these factors.
– Allows identification of critical factors – Derivation of cluster specific interventions § Trainings, Seminars, etc. – Measurement of intervention success.
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Vision 3 Cluster Terminology (Glossary) 2 Overview CSP1 1
Agenda
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Scientific Cooperation Platform
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Potential of a cluster visualization tool
From the first steps… …to the vision
Interactive individualized Visualization of Publication Networks Integration of further data: Method Competences, Communication Flows, Research Interests, Spatial Relations, Ratings CiteSpace for the Cluster of Excellence Suggestion of Literature for individual current publications
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