The Trilemma of Network Analysis

Dorn, I.; Lindenblatt, Andreas; Zweig, Katharina Anna

doi:10.1109/asonam.2012.12

Cited by 18 publications

(6 citation statements)

References 23 publications

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“…Not only the representation as network itself, but also many network analytic methods assume the presence of a network process, for example all distance-based measures or most centrality measures (Borgatti 2005). We argue that a meaningful network analysis needs to account for the process of interest: the network representation cannot be chosen independently from the process of interest, and also the set of applicable network methods is restricted by the process of interest (Borgatti 2005;Dorn et al 2012;Zweig 2016;Butts 2009). We call this approach process-driven network analysis.…”

Section: Resultsmentioning

confidence: 99%

“…Furthermore, the process of interest dictates the network representation since the network needs to be chosen such that network and process match. The interdependence of network representation, network measure, and network process is sometimes phrased as Trilemma of Network Analysis (Dorn et al 2012).…”

Section: How To Determine the Most Reasonable Network Representation?mentioning

confidence: 99%

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Towards a process-driven network analysis

Bockholt

Zweig

2020

Appl Netw Sci

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A popular approach for understanding complex systems is a network analytic one: the system’s entities and their interactions are represented by a graph structure such that readily available methods suitable for graph structures can be applied. A network representation of a system enables the analysis of indirect effects: if A has an impact on B, and B has an impact on C, then, A also has an impact on C. This is often due to some kind of process flowing through the network, for example, pieces of informations or viral infections in social systems, passenger flows in transportation systems, or traded goods in economic systems. We argue that taking into account the actual usage of the system additionally to the static network representation of the system can yield interesting insights: first, the network representation and applicable network methods cannot be chosen independently from the network process of interest (Borgatti 2005; Dorn et al. 2012; Zweig 2016; Butts 2009). Therefore, focussing on the relevant network process in an early stage of the research project helps to determine suitable network representations and methods in order to obtain meaningful results (we call this approach process-driven network analysis). Second, many network methods assume that the spreading of some entity follows shortest or random paths. However, we show that not all flows are well approximated by this. In these cases, incorporating the network usage creates a real addition of knowledge to the static aggregated network representation. Note This is an extended and revised version of a conference article (Bockholt and Zweig 2019), published and presented at COMPLEX NETWORKS 2019.

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Section: Resultsmentioning

confidence: 99%

Section: How To Determine the Most Reasonable Network Representation?mentioning

confidence: 99%

Towards a process-driven network analysis

Bockholt

Zweig

2020

Appl Netw Sci

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“…In this case, not every method can be meaningfully applied to all networks; the interpretation of any method depends strongly on the research question and the specific network representation-a situation that Dorn, Lindenblatt, and Zweig have called the trilemma of social network analysis (Dorn et al 2012). The interpretation of why this structure emerges and what its function is in the system of interest is not of prime concern.…”

Section: Walk-based Methods and Network Flowsmentioning

confidence: 99%

“…While it is clear that a word-adjacency network is the result of a speech production process, does that mean that the word-adjacency network is used for speech planning or speech production? However, for this process of interest it does not make sense to use the original betweenness centrality (Dorn et al 2012). Only in this case does it make sense to interpret the average distance with respect to the speech production.…”

Section: Word-adjacency Network In the Literaturementioning

confidence: 99%

Towards a Theoretical Framework for Analyzing Complex Linguistic Networks

Mehler¹,

Lcking²,

Banisch³

et al. 2016

Understanding Complex Systems

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Complexity is an interdisciplinary program publishing the best research and academiclevel teaching on both fundamental and applied aspects of complex systems-cutting across all traditional disciplines of the natural and life sciences, engineering, economics, medicine, neuroscience, social and computer science.Complex Systems are systems that comprise many interacting parts with the ability to generate a new quality of macroscopic collective behavior the manifestations of which are the spontaneous formation of distinctive temporal, spatial or functional structures. Models of such systems can be successfully mapped onto quite diverse "real-life" situations like the climate, the coherent emission of light from lasers, chemical reaction-diffusion systems, biological cellular networks, the dynamics of stock markets and of the internet, earthquake statistics and prediction, freeway traffic, the human brain, or the formation of opinions in social systems, to name just some of the popular applications.Although their scope and methodologies overlap somewhat, one can distinguish the following main concepts and tools: self-organization, nonlinear dynamics, synergetics, turbulence, dynamical systems, catastrophes, instabilities, stochastic processes, chaos, graphs and networks, cellular automata, adaptive systems, genetic algorithms and computational intelligence.The three major book publication platforms of the Springer Complexity program are the monograph series "Understanding Complex Systems" focusing on the various applications of complexity, and the "Springer Series in Synergetics", which is devoted to the quantitative theoretical and methodological foundations, and the "SpringerBriefs in Complexity" which are concise and topical working reports, case-studies, surveys, essays and lecture notes of relevance to the field. In addition to the books in these two core series, the program also incorporates individual titles ranging from textbooks to major reference works.Future scientific and technological developments in many fields will necessarily depend upon coming to grips with complex systems. Such systems are complex in both their composition-typically many different kinds of components interacting simultaneously and nonlinearly with each other and their environments on multiple levels-and in the rich diversity of behavior of which they are capable.The Springer Series in Understanding Complex Systems series (UCS) promotes new strategies and paradigms for understanding and realizing applications of complex systems research in a wide variety of fields and endeavors. UCS is explicitly transdisciplinary. It has three main goals: First, to elaborate the concepts, methods and tools of complex systems at all levels of description and in all scientific fields, especially newly emerging areas within the life, social, behavioral, economic, neuro-and cognitive sciences (and derivatives thereof); second, to encourage novel applications of these ideas in various fields of engineering and computation such as robotics, nano-technology...

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“…They use the real user traffic to validate the random surfer model contained in the PageRank algorithm and analyze for which assumptions of the random surfer behaviour the real users' behaviour differs. Dorn et al (2012) use the passenger flow data in the US air transportation network to introduce a stress betweenness centrality counting on how many passenger journeys an airport is contained in and compare the resulting rankings to rankings of the standard betweenness centrality. They find that the results significantly change when the centrality considers the number of actually taken paths instead of all possible shortest paths.…”

Section: Related Workmentioning

confidence: 99%

A systematic evaluation of assumptions in centrality measures by empirical flow data

Bockholt

Zweig

2021

Soc. Netw. Anal. Min.

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When considering complex systems, identifying the most important actors is often of relevance. When the system is modeled as a network, centrality measures are used which assign each node a value due to its position in the network. It is often disregarded that they implicitly assume a network process flowing through a network, and also make assumptions of how the network process flows through the network. A node is then central with respect to this network process (Borgatti in Soc Netw 27(1):55–71, 2005, 10.1016/j.socnet.2004.11.008). It has been shown that real-world processes often do not fulfill these assumptions (Bockholt and Zweig, in Complex networks and their applications VIII, Springer, Cham, 2019, 10.1007/978-3-030-36683-4_7). In this work, we systematically investigate the impact of the measures’ assumptions by using four datasets of real-world processes. In order to do so, we introduce several variants of the betweenness and closeness centrality which, for each assumption, use either the assumed process model or the behavior of the real-world process. The results are twofold: on the one hand, for all measure variants and almost all datasets, we find that, in general, the standard centrality measures are quite robust against deviations in their process model. On the other hand, we observe a large variation of ranking positions of single nodes, even among the nodes ranked high by the standard measures. This has implications for the interpretability of results of those centrality measures. Since a mismatch of the behaviour of the real network process and the assumed process model does even affect the highly-ranked nodes, resulting rankings need to be interpreted with care.

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The Trilemma of Network Analysis

Cited by 18 publications

References 23 publications

Towards a process-driven network analysis

Towards a process-driven network analysis

Towards a Theoretical Framework for Analyzing Complex Linguistic Networks

A systematic evaluation of assumptions in centrality measures by empirical flow data

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