Community detection in directed acyclic graphs

Speidel, Leo; Takaguchi, Taro; Masuda, Naoki

doi:10.1140/epjb/e2015-60226-y

Cited by 22 publications

(16 citation statements)

References 58 publications

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“…Multiplex networks have been used to model citation networks mainly for clustering purpose (Boden et al 2012; Dong et al 2012; Renoust et al 2014; Speidel et al 2015). Boden et al (2012) model the multiplex network from the keyword-publication associations in order to create clusters of publications of similar topic.…”

Section: Related Workmentioning

confidence: 99%

“…Renoust et al (2014) use an approach closer to Boden’s, with the different goal to find cohesive groups of co-authors in an author-publication network. Analysis of multiplex DAGs has also recently been proposed by Speidel et al (2015) using the same arXiv HEP-Th dataset that we use, with the difference that layers are defined by year of publication. Beyond community detection, Pujari and Kanawati (2015) investigated the multiplex citation networks to predict the creation of link between authors: from the citation network they infer a multiplex network of authors and predict links, based on community structure.…”

Section: Related Workmentioning

confidence: 99%

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Multiplex flows in citation networks

2017

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Knowledge is created and transmitted through generations, and innovation is often seen as a process generated from collective intelligence. There is rising interest in studying how innovation emerges from the blending of accumulated knowledge, and from which path an innovation mostly inherits. A citation network can be seen as a perfect example of one generative process leading to innovation. However, the impact and influence of scientific publication are always difficult to capture and measure. We offer a new take on investigating how the knowledge circulates and is transmitted, inspired by the notion of “stream of knowledge”. We propose to look at this question under the lens of flows in directed acyclic graphs (DAGs). In this framework inspired by the work of Strahler, we can also account for other well known measures of influence such as the h-index. We propose then to analyze flows of influence in a citation networks as an ascending flow. From this point on, we can take a finer look at the diffusion of knowledge through the lens of a multiplex network. In this network, each citation of a specific work constitutes one layer of interaction. Within our framework, we design three measures of multiplex flows in DAGs, namely the aggregated, sum and selective flow, to better understand how citations are influenced. We conduct our experiments with the arXiv HEP-Th dataset, and find insights through the visualization of these multiplex networks.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Multiplex flows in citation networks

2017

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“…In recent years, the literature has proposed many new methods to cluster a directed network [15–25], including naïve network transformation, transformations maintaining directionality, extending an existing method for a directed network, and other innovative new approaches [15]; among them, only a few demonstrate their methods using citation networks [20, 23]. Moreover, only one of them specifically addresses the directed acyclic network [25], by proposing ‘modularity’ (quality measure for clustering results) for such a network and applying the method to two citation networks among others. This study finds that the resulting modularity values are similar, whether directionality is taken into account or not, and that the resulting subnetworks are also fairly similar when compared against those reported elsewhere.…”

Section: Methodsmentioning

confidence: 99%

Recent Themes in Social Networking Service Research

Liu

2017

PLoS ONE

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The body of literature addressing the phenomenon related to social networking services (SNSs) has grown rather fast recently. Through a systematic and quantitative approach, this study identifies the recent SNS research themes, which are the issues discussed by a coherent and growing subset of this literature. A set of academic articles retrieved from the Web of Science database is used as the basis for uncovering the recent themes. We begin the analysis by constructing a citation network which is further separated into groups after applying a widely used clustering method. The resulting clusters all consist of articles coherent in citation relationships. This study suggests eight fast growing recent themes. They span widely encompassing politics, romantic relationships, public relations, journalism, and health. Among them, four focus their issues largely on Twitter, three on Facebook, and one generally on both. While discussions on traditional issues in SNSs such as personality, motivations, self-disclosure, narcissism, etc. continue to lead the pack, the proliferation of the highlighted recent themes in the near future is very likely to happen.

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“…In this section, we describe the DAG representation of a temporal network, which is useful when solving problems related to temporal paths and describing the centrality notions we will introduce in Section 3. This DAG representation and its variants have been considered in the analysis of temporal networks [15,[36][37][38][39][40]. For temporal network G = (V, E), the DAG representation of G, denoted by G = ( V , E), is constructed as follows.…”

Section: Directed Acyclic Graph Representationmentioning

confidence: 99%

Coverage centralities for temporal networks

2016

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Abstract. Structure of real networked systems, such as social relationship, can be modeled as temporal networks in which each edge appears only at the prescribed time. Understanding the structure of temporal networks requires quantifying the importance of a temporal vertex, which is a pair of vertex index and time. In this paper, we define two centrality measures of a temporal vertex based on the fastest temporal paths which use the temporal vertex. The definition is free from parameters and robust against the change in time scale on which we focus. In addition, we can efficiently compute these centrality values for all temporal vertices. Using the two centrality measures, we reveal that distributions of these centrality values of realworld temporal networks are heterogeneous. For various datasets, we also demonstrate that a majority of the highly central temporal vertices are located within a narrow time window around a particular time. In other words, there is a bottleneck time at which most information sent in the temporal network passes through a small number of temporal vertices, which suggests an important role of these temporal vertices in spreading phenomena.

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Community detection in directed acyclic graphs

Cited by 22 publications

References 58 publications

Multiplex flows in citation networks

Multiplex flows in citation networks

Recent Themes in Social Networking Service Research

Coverage centralities for temporal networks

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