Ranking in evolving complex networks

Liao, Hao; Mariani, Manuel Sebastian; Medo, Matúš; Zhang, Yicheng; Zhou, Mingyang

doi:10.1016/j.physrep.2017.05.001

Cited by 223 publications

(178 citation statements)

References 330 publications

(711 reference statements)

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“…State-of-the-art similarity evaluation methods could be utilized to carry out link prediction, including common neighbors (CN), Jaccard index (JB), resource allocation index (RA), local path index (LP), and structural perturbation method (SPM) (see the part of Baseline and [38]). …”

Section: Network Formation and Metricsmentioning

confidence: 99%

Connecting Patterns Inspire Link Prediction in Complex Networks

et al. 2017

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Link prediction uses observed data to predict future or potential relations in complex networks. An underlying hypothesis is that two nodes have a high likelihood of connecting together if they share many common characteristics. The key issue is to develop different similarity-evaluating approaches. However, in this paper, by characterizing the differences of the similarity scores of existing and nonexisting links, we find an interesting phenomenon that two nodes with some particular low similarity scores also have a high probability to connect together. Thus, we put forward a new framework that utilizes an optimal one-variable function to adjust the similarity scores of two nodes. Theoretical analysis suggests that more links of low similarity scores (long-range links) could be predicted correctly by our method without losing accuracy. Experiments in real networks reveal that our framework not only enhances the precision significantly but also predicts more long-range links than state-of-the-art methods, which deepens our understanding of the structure of complex networks.

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Section: Network Formation and Metricsmentioning

confidence: 99%

Connecting Patterns Inspire Link Prediction in Complex Networks

et al. 2017

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“…Our work naturally embeds into this context, since the scholar citation network we use is an instance of a temporal network in which citation links are timestamped with the time of publication of the citing paper. Static ranking methods applied to dynamic and evolving networks have been found to exhibit important shortcomings [Liao et al, 2017], consequently a variety of dedicated methods have been put forward.…”

Section: Related Workmentioning

confidence: 99%

TimeRank: A dynamic approach to rate scholars using citations

Franceschet

Colavizza

2017

Journal of Informetrics

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Rating has become a common practice of modern science. No rating system can be considered as final, but instead several approaches can be taken, which magnify different aspects of the fabric of science. We introduce an approach for rating scholars which uses citations in a dynamic fashion, allocating ratings by considering the relative position of two authors at the time of the citation among them. Our main goal is to introduce the notion of citation timing as a complement to the usual suspects of popularity and prestige. We aim to produce a rating able to account for a variety of interesting phenomena, such as positioning raising stars on a more even footing with established researchers. We apply our method on the bibliometrics community using data from the Web of Science from 2000 to 2016, showing how the dynamic method is more effective than alternatives in this respect.

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“…The presence of growth challenges traditional network analysis [6,7] and makes it essential to develop and validate time-aware methods to achieve a solid understanding of the structure of these systems [8]. In particular, extensive research has shown that the inclusion of temporal information into network analysis has a dramatic impact on long-studied problems such as community detection [9][10][11], node ranking [11][12][13], dynamics control [14], and spreading phenomena [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Optimal timescale for community detection in growing networks

2019

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Time-stamped data are increasingly available for many social, economic, and information systems that can be represented as networks growing with time. The World Wide Web, social contact networks, and citation networks of scientific papers and online news articles, for example, are of this kind. Static methods can be inadequate for the analysis of growing networks as they miss essential information on the system's dynamics. At the same time, time-aware methods require the choice of an observation timescale, yet we lack principled ways to determine it. We focus on the popular community detection problem which aims to partition a network's nodes into meaningful groups. We use a multi-layer quality function to show, on both synthetic and real datasets, that the observation timescale that leads to optimal communities is tightly related to the system's intrinsic aging timescale that can be inferred from the time-stamped network data. The use of temporal information leads to drastically different conclusions on the community structure of real information networks, which challenges the current understanding of the large-scale organization of growing networks. Our findings indicate that before attempting to assess structural patterns of evolving networks, it is vital to uncover the timescales of the dynamical processes that generated them.A popular approach to community detection is to maximize a function, called modularity, which quantifies how much the total number of intra-community edges deviates from its expected value under a null model that preserves the individual nodes' number of connections [24]. Modularity has been studied from many viewpoints, and it is widely-recognized as a standard tool in network analysis [18]. Despite past research and the wide use of modularity optimization in a broad range of contexts, we still lack a systematic understanding of its behavior and performance in growing networks where time and aging phenomena are fundamental [25][26][27]. Albeit modularity has been used in such systems in its original form [28][29][30], the results can be expected to be suboptimal as modularity neglects the vital time information. A multi-layer form of modularity has been developed that can take into account network snapshots at various times [31,32]. However, when we wish to apply a multi-layer approach to identify relevant communities in growing networks, we face an impasse: existing works assume layered input data [31][32][33][34] and thus they do not consider the question of how to divide an arbitrary time-stamped network into layers. Addressing this question requires to choose an appropriate observation timescale, i.e. the temporal duration for each layer [5,35,36]. This choice is essential because different timescales might reveal substantially different community structures, which in turn might lead to different conclusions on the large-scale organization of the system.In this work, we derive analytically a criterion to estimate when a time-aggregated, static view of a growing network ceases to be ...

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Ranking in evolving complex networks

Cited by 223 publications

References 330 publications

Connecting Patterns Inspire Link Prediction in Complex Networks

Connecting Patterns Inspire Link Prediction in Complex Networks

TimeRank: A dynamic approach to rate scholars using citations

Optimal timescale for community detection in growing networks

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