A Guide to Selecting a Network Similarity Method

Soundarajan, Sucheta; Eliassi-Rad, Tina; Gallagher, Brian

doi:10.1137/1.9781611973440.118

Cited by 47 publications

(35 citation statements)

References 15 publications

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“…Quantifying dissimilarities and determining isomorphisms among graphs are fundamental open problems in computer science, with a very long history123456789101112131415. The graph isomorphism problem consists in deciding whether two graphs are identical, presenting a one-to-one correspondence between its components.…”

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confidence: 99%

Quantification of network structural dissimilarities

et al. 2017

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Identifying and quantifying dissimilarities among graphs is a fundamental and challenging problem of practical importance in many fields of science. Current methods of network comparison are limited to extract only partial information or are computationally very demanding. Here we propose an efficient and precise measure for network comparison, which is based on quantifying differences among distance probability distributions extracted from the networks. Extensive experiments on synthetic and real-world networks show that this measure returns non-zero values only when the graphs are non-isomorphic. Most importantly, the measure proposed here can identify and quantify structural topological differences that have a practical impact on the information flow through the network, such as the presence or absence of critical links that connect or disconnect connected components.

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confidence: 99%

Quantification of network structural dissimilarities

et al. 2017

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“…Soundarajan et al [127] show that NetSimile is consistently close to the consensus among all measures studied by them, showing that it approximates the results of more complex competitors. is nding makes NetSimile a rst choice in any comparative study of graph similarities.…”

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confidence: 72%

From Topic Networks to Distributed Cognitive Maps: Zipfian Topic Universes in the Area of Volunteered Geographic Information

et al. 2020

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Are nearby places (e.g. cities) described by related words? In this article we transfer this research question in the eld of lexical encoding of geographic information onto the level of intertextuality. To this end, we explore Volunteered Geographic Information (VGI) to model texts addressing places at the level of cities or regions with the help of so-called topic networks. is is done to examine how language encodes and networks geographic information on the aboutness level of texts. Our hypothesis is that the networked thematizations of places are similar -regardless of their distances and the underlying communities of authors. To investigate this we introduce Multiplex Topic Networks (MTN), which we automatically derive from Linguistic Multilayer Networks (LMN) as a novel model, especially of thematic networking in text corpora. Our study shows a Zip an organization of the thematic universe in which geographical places (especially cities) are located in online communication. We interpret this nding in the context of cognitive maps, a notion which we extend by so-called thematic maps. According to our interpretation of this nding, the organization of thematic maps as part of cognitive maps results from a tendency of authors to generate shareable content that ensures the continued existence of the underlying media. We test our hypothesis by example of special wikis and extracts of Wikipedia. In this way we come to the conclusion: Places, whether close to each other or not, are located in neighboring places that span similar subnetworks in the topic universe.

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“…While the choice of the measures to be used for a specific empirical network is of course influenced by what the analyst is interested in, e.g., degree-based similarity, betweenness-based, or specific motifs that are motivated by the application context, our experiments show that different measures highlight different types of similarities. (33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50). For each network type on the left is generalized multiplex network and on the right the node-aligned multiplex network.…”

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confidence: 99%

Quantifying layer similarity in multiplex networks: a systematic study

et al. 2018

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Computing layer similarities is an important way of characterizing multiplex networks because various static properties and dynamic processes depend on the relationships between layers. We provide a taxonomy and experimental evaluation of approaches to compare layers in multiplex networks. Our taxonomy includes, systematizes and extends existing approaches, and is complemented by a set of practical guidelines on how to apply them.

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A Guide to Selecting a Network Similarity Method

Cited by 47 publications

References 15 publications

Quantification of network structural dissimilarities

Quantification of network structural dissimilarities

From Topic Networks to Distributed Cognitive Maps: Zipfian Topic Universes in the Area of Volunteered Geographic Information

Quantifying layer similarity in multiplex networks: a systematic study

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