Compressing strongly connected subgroups in social networks: An entropy-based approach

Brenner, Dominic; Dellnitz, Andreas; Kulmann, Friedhelm; Rödder, Wilhelm

doi:10.1080/0022250x.2017.1284070

Cited by 6 publications

(12 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our context, this information gain is realized when an actor exerts its full personal power in the vertex and makes the probability Q(V i = 1) to 1. The information gain measures change of (conditional) power relations in the net 19 and our observations in "Deployment of structural power and dominance" section. The higher the change potential of a vertex, the more influence an actor would have in the net.…”

Section: If An Actor Inmentioning

confidence: 70%

An entropy-based framework to analyze structural power and power alliances in social networks

Dellnitz

Rödder

2020

Sci Rep

Self Cite

View full text Add to dashboard Cite

power is a central phenomenon in societies. So for ages, numerous power perceptions in philosophy and sociology have existed. Measuring power of an actor in its social fabric is a difficult issue, however. After sketching first attempts for this in social network analyses, we develop a new power theory. To this end, we distinguish between vertices in the network and actors acting in vertices. Vertices get structural power potential from their position in the net. in an entropy-driven model such potential can be calculated for all vertices; for selected networks, the method is exemplified. Actors in vertices can deploy power potential once they have respective personal skills, and dominate actors in adjacent vertices. if chosen with suitable care, an alliance of actors can even dominate the whole network. the findings are applied to the famous 9/11-network with 34 vertices and 93 edges. A short survey of power perceptions in history. Power is a central phenomen in societies: Who exerts power on whom, to which degree, using which resources and at which costs. Following Witte 1 even in the animal kingdom we observe manyfold power relations perhaps indicating an evolutionary setting. In occidental cultures, the issue of power was and is omnipresent. Linguistic terms with different etymologic roots like Anweald, Auctoritas, Macht, Maht, Potentia, Potestas, Pouvoir, Power refer to this. According to Platon exertion of power is part of human nature. Aristoteles brings into focus hierarchical dominance structures like slavery, despotism and political sovereignty. Often the consideration of power served as a justification of brute force executed by church or state, e.g. Padua 2 , Ockham 3 , Hobbes 4 , Marx 5 , then broaden this narrow view. So Ockham as well as Marx detect estate/capital as an instrument of power. Witte perceives different power systems 1 • according to their extent (individual, micro, meso, macro), • according to quality (affective, cognitive, conative). This distinction then adds up to different forms of social power: expert power, information power, power by pressure, power by reward. In all aspects presented so far there was little attempt to measure power. Jakob Moreno in 1925 emigrated from Vienna to the US and wrote his pioneering article 6 "Who shall survive: a new approach to the problem of human interrelation". For the first time sociological relations between actors were illustrated by graphs. Further research of sociologists made graphs a successful tool to measure structural characteristics of the social fabric, like centrality, closeness, betweenness, etc. But only in the 1960s did the very question come up of how to measure power. power in social networks. Social Networks (SN) are sets of actors and their manifold relations. Graphs, hypergraphs and multigraphs are modern tools to illustrate such networks. A first introduction we find in the textbooks of Jansen 7 or Scott 8 ; the reader interested in more sophisticated mathematical models might tend to study the compendium of Newman 9. Impor...

show abstract

Section: If An Actor Inmentioning

confidence: 70%

An entropy-based framework to analyze structural power and power alliances in social networks

Dellnitz

Rödder

2020

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…The details in this section are based on the representation of knowledge processing in social networks in Rödder et al [ 13 ], Brenner et al [ 18 ]. They are repeated here and applied to affiliation networks in the subsequent Section 3.2 .…”

Section: Entropy-driven Bipartite Network Analysismentioning

confidence: 99%

“…For

as in (22),

measures the change of the conditional structure from

, cf. Brenner et al [ 18 ]. Rödder et al [ 13 ] called this number the diffusion potential of a node, and in Theorem 2 on page 7975, it is shown that:

…”

Section: Entropy-driven Bipartite Network Analysismentioning

confidence: 99%

Bipartite Structures in Social Networks: Traditional versus Entropy-Driven Analyses

Rödder

Dellnitz

Kulmann

et al. 2019

Entropy

Self Cite

View full text Add to dashboard Cite

A special type of social networks is the so-called affiliation network, consisting of two modes of vertices: actors and events. Up to now, in the undirected case, the closeness of actors in such networks has been measured by their jointly-attended events. Indirect contacts and attenuated and directed links are of minor interest in affiliation networks. These flaws make a veritable estimation of, e.g., possible message transfers amongst actors questionable. In this contribution, first, we discuss these matters from a graph-theoretical point of view. Second, so as to avoid the identified weaknesses, we propose an up-and-coming entropy-based approach for modeling such networks in their generic structure, replacing directed (attenuated) links by conditionals: if-then. In this framework, the contribution of actors and events to a reliable message transfer from one actor to another—even via intermediaries—is then calculated applying the principle of maximum entropy. The usefulness of this new approach is demonstrated by the analysis of an affiliation network called “corporate directors”.

show abstract

“…For a networked population, cooperation is achievable through PD games [13]. Unlike the regularities of periodic lattice networks, heterogeneous networks are more suitable for modeling social systems in which local connectivities vary throughout the population [50,51]. The topologies of networks have significant influence on the results of games.…”

Section: Introductionmentioning

confidence: 99%

Ranking game on networks: The evolution of hierarchical society

Zhang

Tang

Xiong

et al. 2020

Physica A: Statistical Mechanics and its Applications

View full text Add to dashboard Cite

Compressing strongly connected subgroups in social networks: An entropy-based approach

Cited by 6 publications

References 29 publications

An entropy-based framework to analyze structural power and power alliances in social networks

An entropy-based framework to analyze structural power and power alliances in social networks

Bipartite Structures in Social Networks: Traditional versus Entropy-Driven Analyses

Ranking game on networks: The evolution of hierarchical society

Contact Info

Product

Resources

About