Exposing multi-relational networks to single-relational network analysis algorithms

Rodriguez, Marko A.; Shinavier, Joshua

doi:10.1016/j.joi.2009.06.004

Cited by 80 publications

(54 citation statements)

References 40 publications

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“…Also the networks where more than one type of relation exists are not new in the world of science 2 and they were analysed mainly at the small scale 5,6,7 . However, recently the area of large-scale multi-layered network has started attracting more and more attention from researchers from different fields 8,9,10,11 . These networks are also known as multirelational, multiplex, multi-layered or multivariate networks 11 .…”

Section: Related Workmentioning

confidence: 99%

“…Development of new metrics is very important from the perspective of further advances in the web science as the multi-relational networks can be found almost everywhere, they are more expressive in terms of the semantic information and give opportunity to analyse different types of human relationships 10 . Researchers usually try to cope with multi-layered large-scale networks by analysing layers separately using the existing methods for one-layered networks and then comparing the results using some correlation measure (e.g.…”

Section: Published By Atlantis Pressmentioning

confidence: 99%

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Analysis of Neighbourhoods in Multi-layered Dynamic Social Networks

Bródka¹,

Kazienko²,

Musiał³

et al. 2012

IJCIS

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Social networks existing among employees, customers or other types of users of various IT systems have become one of the research areas of growing importance. Data about people and their interactions that exist in social media, provides information about many different types of relationships within one network. Analysing this data one can obtain knowledge not only about the structure and characteristics of the network but it also enables to understand the semantic of human relations. Each social network consists of nodes -social entities and edges linking pairs of nodes. In regular, one-layered networks, two nodesi.e. people are connected with a single edge whereas in the multi-layered social networks, there may be many links of different types for a pair of nodes. Most of the methods used for social network analysis (SNA) may be applied only to one-layered networks. Thus, some new structural measures for multi-layered social networks are proposed in the paper. This study focuses on definitions and analysis of cross-layer clustering coefficient, cross-layer degree centrality and various versions of multi-layered degree centralities. Authors also investigated the dynamics of multi-layered neighbourhood. The evaluation of the presented concepts on the real-world dataset is presented. The measures proposed in the paper may directly be used to various methods for collective classification, in which nodes are assigned to labels according to their structural input features.

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Section: Related Workmentioning

confidence: 99%

Section: Published By Atlantis Pressmentioning

confidence: 99%

Analysis of Neighbourhoods in Multi-layered Dynamic Social Networks

Bródka¹,

Kazienko²,

Musiał³

et al. 2012

IJCIS

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“…The resulting vector is a ranking of documents where the knowledge of the network is deposited, according to the interpretation of the steady state of energy (Rodriguez & Shinavier, 2010). Defining it more clearly, the Eigenvector Centrality measure, applied to the Forward network ω 2forward offers us the documents that, in the researchers' perspective, gather the most relevant information at the current time.…”

Section: Figure 2 Bi-relational Algorithm For Reading Heuristicsmentioning

confidence: 99%

“…Forward citation has been used until now (Belter, 2016;Couteau, 2014) but locally around a document, and to interpret an existing document when a new one cites it, but saying nothing about the citing new one. Namely, Eigenvector Centrality is an energy diffusion vector in a steady state of energy, representing a ranking of documents where the knowledge (the diffused element) of the network is deposited (Rodriguez & Shinavier, 2010). Therefore, these interpretation offers a relevance criteria about new publications without time delay when it is applied to Forward Networks.…”

Section: Introductionmentioning

confidence: 99%

Proceedings of the 2nd International Conference on Advanced Research Methods and Analytics (CARMA 2018)

2018

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Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information. As these sources, methods, and applications become more interdisciplinary, the 2nd International Conference on Advanced Research Methods and Analytics (CARMA) is an excellent forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges. Keywords: Big Data sources, Web scraping Social media mining, Official Statistics, Internet Econometrics, Digital transformation, global society. 2nd International Conference on Advanced Research Methods and Analytics (CARMA2018)Universitat Politècnica de València, València, 2018 This work is licensed under a Creative Commons License CC BY-NC-ND 4.0 Editorial Universitat Politècnica de València I Preface Preface to CARMA2018This volume contains the selected papers of the Second International Conference on Advanced Research Methods and Analytics (CARMA 2018) hosted by the Universitat Politècnica de València, Spain during 12 and 13 July 2018. This second edition consolidated CARMA as a unique forum where Economics and Social Sciences research meets Internet and Big Data. CARMA provided researchers and practitioners with an ideal environment to exchange ideas and advances on how Internet and Big Data sources and methods contribute to overcome challenges in Economics and Social Sciences, as well as on the changes in the society because of the digital transformation.The selection of the scientific program was directed by Maria Rosalia Vicente, who led an international team of 33 scientific committee members representing 28 institutions. Following the call for papers, the conference received 73 paper submissions from all around the globe. All submissions were reviewed by the scientific committee members under a double blind review process. Finally, 40 papers were accepted for oral presentation during the conference. This represents an overall paper acceptance rate of 54%, ensuring a high quality scientific program. It covers a wide range of research topics in Internet and Big Data, including nowcasting people mobility and economic indicators, applications of Big Data methods in retail and finance, using search and social media data, among others.CARMA 2018 also featured two special sessions on "Big Data for Central Banks" and "Using Big Data in Official Statistics," chaired by Juri Marcucci and Gian Luigi Mazzi, respectively. Both sessions gave a complementary institutional perspective on how to use Internet and Big Data sources and methods for public policy and official statistics. The perspective from the private sector was contributed by Norbert Wirth, who talked about "Data Science development at scale" in his keynote speech. Organizing Committee General chairJosep Domènech, Universitat Politècnica de València Scientific committee chairMaría Rosalía Vicente, Universidad ...

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“…However, in the real word, objects are usually connected via different types of relationships, forming multi-relational networks [27,22]. For example, in the bibliographic information network case, researchers could be linked together via different types of relationships: collaboration relationships, citation relationships, sharing common co-authors, co-attending conferences, etc.…”

Section: Multi-relational Information Networkmentioning

confidence: 99%

Modeling Topic Diffusion in Multi-Relational Bibliographic Information Networks

Gui

Sun

Han

et al. 2014

Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

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Information diffusion has been widely studied in networks, aiming to model the spread of information among objects when they are connected with each other. Most of the current research assumes the underlying network is homogeneous, i.e., objects are of the same type and they are connected by links with the same semantic meanings. However, in the real word, objects are connected via different types of relationships, forming multi-relational heterogeneous information networks.In this paper, we propose to model information diffusion in such multi-relational networks, by distinguishing the power in passing information around for different types of relationships. We propose two variations of the linear threshold model for multi-relational networks, by considering the aggregation of information at either the model level or the relation level. In addition, we use real diffusion action logs to learn the parameters in these models, which will benefit diffusion prediction in real networks. We apply our diffusion models in two real bibliographic information networks, DBLP network and APS network, and experimentally demonstrate the effectiveness of our models compared with single-relational diffusion models. Moreover, our models can determine the diffusion power of each relation type, which helps us understand the diffusion process better in the multi-relational bibliographic network scenario.

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Exposing multi-relational networks to single-relational network analysis algorithms

Cited by 80 publications

References 40 publications

Analysis of Neighbourhoods in Multi-layered Dynamic Social Networks

Analysis of Neighbourhoods in Multi-layered Dynamic Social Networks

Proceedings of the 2nd International Conference on Advanced Research Methods and Analytics (CARMA 2018)

Modeling Topic Diffusion in Multi-Relational Bibliographic Information Networks

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