Learning Social Circles in Ego-Networks Based on Multi-View Network Structure

Lan, Chao; Yang, Yuhao; Li, Xiaoli; Luo, Bo; Huan, Jun

doi:10.1109/tkde.2017.2685385

Cited by 17 publications

(8 citation statements)

References 47 publications

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“…In Definition 1, the outgoing links vector at a node is utilized to describe the directional relations of a node pointing to the other nodes. This is inspired by the ones in the ego-network [25]. (ii) As part of the coupling, the outgoing links vector L i (t) is shown in Equation ( 2), which reflects the influence of the outgoing links of the ith node on its dynamics.…”

Section: Remark 1 (I)mentioning

confidence: 99%

Double Tracking Control for the Directed Complex Dynamic Network via the Observer of Outgoing Links

Gao¹,

Wang²,

Liu³

et al. 2021

Preprint

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From the large system perspective, the directed complex dynamic network is considered as being composed of the nodes subsystem (NS) and the links subsystem (LS), which are coupled with together. Different from the previous studies which propose the dynamic model of LS with the matrix differential equations, this paper describes the dynamic behavior of LS with the outgoing links vector at every node, by which the dynamic model of LS can be represented as the vector differential equation to form the outgoing links subsystem (OLS). Since the vectors possess the flexible mathematical operational properties than matrices, this paper proposes the more convenient mathematic method to investigate the double tracking control problems of NS and OLS. Under the state of OLS can be unavailable, the asymptotical state observer of OLS is designed in this paper, by which the tracking controllers of NS and OLS are synthesized to ensure achieving the double tracking goals. Finally, the example simulations for supporting the theoretical results are also provided.

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Section: Remark 1 (I)mentioning

confidence: 99%

Double Tracking Control for the Directed Complex Dynamic Network via the Observer of Outgoing Links

Gao¹,

Wang²,

Liu³

et al. 2021

Preprint

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“…This framework helps in identifying connectors and authorities in the given network. The usage of the clustering approach was carried out by Lan et al [21] for promoting the beneficial feature of a multi-view clustering approach. Koufogiannis and Pappas [22] has discussed the propagation of malicious contents through the social network as a part of awareness study.…”

Section: Related Workmentioning

confidence: 99%

Novel Approach for Optimizing Information Propagation in Dynamic Social Network

Selva

Kayavizhy

2020

Wseas Transactions on Computers

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Identification of potential node is one of the essential operations to be carried out in social network analysis as it is necessary to undertake various important decisions associated with the information propagation. Review of existing literature towards social network highlights that there is very less work carried out towards emphasizing potential node. Therefore, the proposed study offers a novel and unique solution that is capable of optimizing the level of information propagation when it is exposed to dynamic networks. The proposed study has been modeled using graph theory and it uses degree centrality distribution in order to offer more insight towards analyzing the selection of potential nodes in a social network. The study significantly contributes towards precise information propagation and its sustainability in the presence of dynamic social network in every aspect.

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“…Among the existing representations, knowledge graphs i.e., large networks of entities and relationships, usually expressed as RDF triples, relevant to a specific domain or an organization [6], provide a great method to organize information in a structured way. They already have been successfully used to understand complex processes in various domains such as social networks ego-nets [7] and biological functions [8].…”

Section: Introductionmentioning

confidence: 99%

“…Scientific knowledge graphs focus on the scholarly domain and typically contain metadata describing research publications such as authors, venues, organizations, research topics, and citations. Some examples are Open Academic Graph 3 , Scholarlydata.org [10], Microsoft Academic Graph 4 [11] (MAG), Scopus 5 , Semantic Scholar 6 , Aminer [12], Core [13], OpenCitations [14], and Dimensions 7 . These resources provide substantial benefits to researchers, companies, and policy makers by powering data-driven services for navigating, analyzing, and making sense of research dynamics.…”

Section: Introductionmentioning

confidence: 99%