Authorship Attribution via Network Motifs Identification

Marinho, Vanessa Queiroz; Hirst, Graeme; Amancio, Diego R.

doi:10.1109/bracis.2016.071

Cited by 28 publications

(28 citation statements)

References 38 publications

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“…Motifs also foster connectivity in real-world networks, for instance in medicine, food webs or the world wide web, carrying information forward [26][27][28][29] . Basically, each network consists of certain motif structures that might be essential for the dynamics of the whole graph.…”

Section: Motifs In a Real-world Application: The Amazon Rainforestmentioning

confidence: 99%

“…The notion of motifs has been introduced by Milo et al 25 as the basic building blocks of complex networks. It has been shown that motifs can be identified for instance in food webs 26 , authorship attribution 27 up to transcriptional networks that control the expression of genes 28 , e.g., in tumor suppressors or E. Coli [29][30][31][32] . The so-called feed forward loop is an essential motif in such networks since it is significantly overexpressed in these real-world networks compared to typical random graphs 25 .…”

Section: Introductionmentioning

confidence: 99%

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How motifs condition critical thresholds for tipping cascades in complex networks: Linking micro- to macro-scales

Wunderling

Stumpf

Krönke

et al. 2020

Chaos: An Interdisciplinary Journal of Nonlinear Science

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How motifs condition critical thresholds: Linking Micro-to Macro-scales In this study, we investigate how specific micro interaction structures (motifs) affect the occurrence of tipping cascades on networks of stylized tipping elements. We compare the properties of cascades in Erdős-Rényi networks and an exemplary moisture recycling network of the Amazon rainforest. Within these networks, decisive small-scale motifs are the feed forward loop, the secondary feed forward loop, the zero loop and the neighboring loop.Of all motifs, the feed forward loop motif stands out in tipping cascades since it decreases the critical coupling strength necessary to initiate a cascade more than the other motifs.We find that for this motif, the reduction of critical coupling strength is 11% less than the critical coupling of a pair of tipping elements. For highly connected networks, our analysis reveals that coupled feed forward loops coincide with a strong 90% decrease of the critical coupling strength.For the highly clustered moisture recycling network in the Amazon, we observe regions of very high motif occurrence for each of the four investigated motifs suggesting that these regions are more vulnerable. The occurrence of motifs is found to be one order of magnitude higher than in a random Erdős-Rényi network.This emphasizes the importance of local interaction structures for the emergence of global cascades and the stability of the network as a whole. a) These authors equally contributed to this study. Correspondences should be addressed to: nico.wunderling@pikpotsdam.de 2 How motifs condition critical thresholds: Linking Micro-to Macro-scales Tipping elements are nonlinear systems, where a small perturbation can be sufficient to induce a qualitative change of the whole system as soon as a critical threshold (tipping point) is crossed. Coupled tipping elements exist for instance in connected lake systems, in the Earth's climate system or in social systems. Here, we investigate networks of interacting tipping elements, where each node consists of a stylized tipping element and explore important interaction structures on the micro scale of the network, the so-called motifs. Such motifs in complex networks have been found in multiple systems such as cell metabolism, food webs or neural networks and are known to be significantly overexpressed in real-world compared to random networks. However, motifs have not yet been studied extensively in complex networks, where nodes have their own dynamics. In our study, we find that tipping cascades occur more often at locations with high motif frequency revealing locations (nodes) of decreased robustness.

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Section: Motifs In a Real-world Application: The Amazon Rainforestmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

How motifs condition critical thresholds for tipping cascades in complex networks: Linking micro- to macro-scales

Wunderling

Stumpf

Krönke

et al. 2020

Chaos: An Interdisciplinary Journal of Nonlinear Science

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“…Marinho et al [39] achieved an accuracy of 57.5%. In a similar study, Mehri, Darooneh and Shariati [40] identified the authorship of several Persian books written by 5 authors.…”

Section: Authorship Attribution Taskmentioning

confidence: 95%

Labelled network subgraphs reveal stylistic subtleties in written texts

Marinho

Hirst

Amancio

2017

Journal of Complex Networks

Self Cite

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The vast amount of data and increase of computational capacity have allowed the analysis of texts from several perspectives, including the representation of texts as complex networks. Nodes of the network represent the words, and edges represent some relationship, usually word co-occurrence. Even though networked representations have been applied to study some tasks, such approaches are not usually combined with traditional models relying upon statistical paradigms. Because networked models are able to grasp textual patterns, we devised a hybrid classifier, called labelled motifs, that combines the frequency of common words with small structures found in the topology of the network, known as motifs. Our approach is illustrated in two contexts, authorship attribution and translationese identification. In the former, a set of novels written by different authors is analyzed. To identify translationese, texts from the Canadian Hansard and the European parliament were classified as to original and translated instances. Our results suggest that labelled motifs are able to represent texts and it should be further explored in other tasks, such as the analysis of text complexity, language proficiency, and machine translation.

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“…ey used directed motifs with three nodes as features for document classification and compared the result with four machine learning methods. e results show that the method based on motif significantly improves the accuracy of document classification [22]. Qi et al used natural language processing technology to analyze enterprise documents, established a complex network of reserved knowledge topics, and used a fuzzy comprehensive evaluation method to calculate the 2 Complexity ability of enterprises to meet specific knowledge needs [23].…”

Section: Application Of Complex Network In Natural Languagementioning

confidence: 99%

Culture under Complex Perspective: A Classification for Traditional Chinese Cultural Elements Based on NLP and Complex Networks

Wang

Chen

et al. 2021

Complexity

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The cultural element is the minimum unit of a cultural system. The systematic categorizing, organizing, and retrieval of the traditional Chinese cultural elements are essential prerequisites for the realization of effective extracting and rational utilization, as well as the prerequisite for exploiting the contemporary value of the traditional Chinese culture. To build an objective, integrated, and reliable classification method and a system of traditional Chinese cultural elements, this study takes the text of Taiping Imperial Encyclopedia in Northern Song Dynasty as the primary data source. The unsupervised word segmentation methods are used to detect Out-of-Vocabulary (OOV), and then the segmentation results by the THULAC tool with and without custom dictionary are compared. The TF-IDF algorithm is applied to extract the keywords of cultural elements and the Ochiia coefficient is introduced to create complex networks of traditional Chinese cultural elements. After analyzing the topological characteristics of the network, the community detection algorithm is used to identify the topics of cultural elements. Finally, a “Means-Ends” two-dimensional orthogonal classification system is established to categorize the topics. The results showed that the degree distribution in the complex network of Chinese traditional cultural elements is a scale-free network with γ = 2.28. The network shows a structure of community and hierarchy features. The top 12 communities have taken up to 91.77% of the scale of the networks. Those 12 topics of the traditional Chinese cultural elements are circularly distributed in the orthogonal system of cultural elements’ categorization.

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Authorship Attribution via Network Motifs Identification

Cited by 28 publications

References 38 publications

How motifs condition critical thresholds for tipping cascades in complex networks: Linking micro- to macro-scales

How motifs condition critical thresholds for tipping cascades in complex networks: Linking micro- to macro-scales

Labelled network subgraphs reveal stylistic subtleties in written texts

Culture under Complex Perspective: A Classification for Traditional Chinese Cultural Elements Based on NLP and Complex Networks

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