Motifs in co-authorship networks and their relation to the impact of scientific publications

Krumov, Lachezar; Fretter, Christoph; Müller‐Hannemann, Matthias; Weihe, Karsten; Hütt, Marc Thorsten

doi:10.1140/epjb/e2011-10746-5

“…For example, the authors of [15,16] compare networks from different domains (e.g., biology, electrical engineering) and find that the motif signatures from different domains may serve as a fingerprint of the respective domain. Among others, further studies analyze the motif signatures of languages [2], social networks [5], reputation networks [33], and citation networks [8].…”

Section: Related Workmentioning

confidence: 99%

“…Next, v calls the TryIterator procedure to conduct suitable graph operations based on the selected iterator (lines [8][9][10]. This procedure is introduced in detail in the next subsection.…”

Section: Graph Operations Each Node V ∈ V Restricts Itself To Three Gmentioning

confidence: 99%

Distributed Graph-based Topology Adaptation using Motif Signatures

Stein

¹

,

Weihe

²

,

Wilberg

³

et al. 2017

2017 Proceedings of the Ninteenth Workshop on Algorithm Engineering and Experiments (ALENEX)

2

0

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A motif is a small graph pattern, and a motif signature counts the occurrences of selected motifs in a network. The motif signature of a real-world network is an important characteristic because it is closely related to a variety of semantic and functional aspects. In recent years, motif analysis has been successfully applied for adapting topologies of communication networks: The motif signatures of very good networks (e.g., in terms of load balancing) are determined a priori to derive a target motif signature. Then, a given network is adapted in iterative steps, subject to side constraints and in a distributed way, such that its motif signature approximates the target motif signature.In this paper, we formalize this adaptation problem and show that it is N P-hard. We present LoMbA, a generic approach for motif-based graph adaptation: All types of networks, all selections of motifs, and all types of consistency-maintaining constraints can be incorporated.To evaluate LoMbA, we conduct a simulation study based on several scenarios of topology adaptation from the domain of communication networks. We consider topology control in wireless ad-hoc networks, balancing of video streaming trees, and load balancing of peer-to-peer overlays. In each considered application scenario, the simulation results are remarkably good, although the implementation was not tuned toward these scenarios.

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“…The measure for the importance of a motif is the squared sum of its coefficients, for the first and the second principle components. The first five most distinguishing motifs are (5-21), (4-2), (5-9), (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) and (4)(5)(6), highlighted in red color in the figures. Motifs having comparable importance are depicted in the same color.…”

Section: Significance Analysis Of Motifsmentioning

confidence: 99%

“…They can really encapsulate and characterize the structure of networks, and motif profiles have been shown to be a very powerful measurement [12]. The correlation of small network motifs (of sizes 3 and 4) with citation frequencies in co-authorship networks has already been the subject of a study [13].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Co-authorship Networks across Scientific Fields Using Motifs

Choobdar

¹

,

Ribeiro

²

,

Bugla

³

et al. 2012

2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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Comparing scientific production across different fields of knowledge is commonly controversial and subject to disagreement. Such comparisons are often based on quantitative indicators, such as papers per researcher, and data normalization is very difficult to accomplish. Different approaches can provide new insight and in this paper we focus on the comparison of different scientific fields based on their research collaboration networks. We use co-authorship networks where nodes are researchers and the edges show the existing co-authorship relations between them. Our comparison methodology is based on network motifs, which are over represented patterns, or subgraphs. We derive motif fingerprints for 22 scientific fields based on 29 different small motifs found in the corresponding co-authorship networks. These fingerprints provide a metric for assessing similarity among scientific fields, and our analysis shows that the discrimination power of the 29 motif types is not identical. We use a co-authorship dataset built from over 15,361 publications inducing a co-authorship network with over 32,842 researchers. Our results also show that we can group different fields according to their fingerprints, supporting the notion that some fields present higher similarity and can be more easily compared.

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“…Motifs, graph patterns of small size, are an essential building block of complex networks [16]: In the last decade, a plethora of research papers, e.g., [5,8,20], have analyzed motif distributions of real-world graphs, showing that the motif signature, which counts the occurrences of selected motifs, is closely related to a variety of semantic and functional aspects.…”

Section: Introductionmentioning

confidence: 99%

2017 Proceedings of the Ninteenth Workshop on Algorithm Engineering and Experiments (ALENEX)

2017

0

View full text Add to dashboard Cite

A motif is a small graph pattern, and a motif signature counts the occurrences of selected motifs in a network. The motif signature of a real-world network is an important characteristic because it is closely related to a variety of semantic and functional aspects. In recent years, motif analysis has been successfully applied for adapting topologies of communication networks: The motif signatures of very good networks (e.g., in terms of load balancing) are determined a priori to derive a target motif signature. Then, a given network is adapted in iterative steps, subject to side constraints and in a distributed way, such that its motif signature approximates the target motif signature.In this paper, we formalize this adaptation problem and show that it is N P-hard. We present LoMbA, a generic approach for motif-based graph adaptation: All types of networks, all selections of motifs, and all types of consistency-maintaining constraints can be incorporated.To evaluate LoMbA, we conduct a simulation study based on several scenarios of topology adaptation from the domain of communication networks. We consider topology control in wireless ad-hoc networks, balancing of video streaming trees, and load balancing of peer-to-peer overlays. In each considered application scenario, the simulation results are remarkably good, although the implementation was not tuned toward these scenarios.

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Motifs in co-authorship networks and their relation to the impact of scientific publications

Cited by 41 publications

References 41 publications

Distributed Graph-based Topology Adaptation using Motif Signatures

Distributed Graph-based Topology Adaptation using Motif Signatures

Comparison of Co-authorship Networks across Scientific Fields Using Motifs

2017 Proceedings of the Ninteenth Workshop on Algorithm Engineering and Experiments (ALENEX)

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