A Study on Social Network Metrics and Their Application in Trust Networks

Varlamis, Iraklis; Eirinaki, Magdalini; Louta, Malamati

doi:10.1109/asonam.2010.40

Cited by 40 publications

(25 citation statements)

References 14 publications

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“…Outdegree centrality (a part of degree centrality which is described in [37][38][39][40][41][42][43]) of the vertex v is measured as the number of edges leading from the node v. The values of the outdegree centrality in the network of semantic resources built on the basis of identity links means the number of other semantic resources, which are linked from the resource represented by the vertex v.…”

Section: Resultsmentioning

confidence: 99%

“…Closeness centrality [37][38][39][40][41][42][43][44] is defined as the average shortest path length between a particular vertex v and other nodes in the graph G. High values of closeness centrality in the case described in this text mean that the concrete semantic resource is close to other resources. It causes simple movement through the network of resources and acquiring of new information.…”

Section: Resultsmentioning

confidence: 99%

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Centrality as a Method for the Evaluation of Semantic Resources for Disaster Risk Reduction

Čerba

Jedlička

Čada

et al. 2017

IJGI

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Clear and straightforward communication is a key aspect of all human activities related to crisis management. Since crisis management activities involve professionals from various disciplines using different terminology, clear and straightforward communication is difficult to achieve. Semantics as a broad science can help to overcome communication difficulties. This research focuses on the evaluation of available semantic resources including ontologies, thesauri, and controlled vocabularies for disaster risk reduction as part of crisis management. The main idea of the study is that the most appropriate source of broadly understandable terminology is such a semantic resource, which is accepted by-or at least connected to the majority of other resources. Important is not only the number of interconnected resources, but also the concrete position of the resource in the complex network of Linked Data resources. Although this is usually done by user experience, objective methods of resource semantic centrality can be applied. This can be described by centrality methods used mainly in graph theory. This article describes the calculation of four types of centrality methods (Outdegree, Indegree, Closeness, and Betweenness) applied to 160 geographic concepts published as Linked Data and related to disaster risk reduction. Centralities were calculated for graph structures containing particular semantic resources as nodes and identity links as edges. The results show that (with some discussed exceptions) the datasets with high values of centrality serve as important information resources, but they also include more concepts from preselected 160 geographic concepts. Therefore, they could be considered as the most suitable resources of terminology to make communication in the domain easier. The main research goal is to automate the semantic resources evaluation and to apply a well-known theoretical method (centrality) to the semantic issues of Linked Data. It is necessary to mention the limits of this study: the number of tested concepts and the fact that centralities represents just one view on evaluation of semantic resources.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Centrality as a Method for the Evaluation of Semantic Resources for Disaster Risk Reduction

Čerba

Jedlička

Čada

et al. 2017

IJGI

View full text Add to dashboard Cite

show abstract

“…The research believes that the user's friend recommendation has an impact on user preferences in a social network. All these models [16][17][18][19] believe that there exist social relationships between users in the system that affect the evaluation of recommendation trustworthiness. However, in open spaces comprised of multiple devices (pervasive environment), these devices in close physical proximity form an ad hoc network for spontaneous service access [20].…”

Section: Related Workmentioning

confidence: 99%

“…They demonstrated in their experiments that social trust is able to evaluate similarity in a more distinctive way when the ratings are extreme and with large differences. In [17], the authors have modeled a social network as a directed graph and have evaluated recommendations based on the position and interconnections of the user represented as actors in the graph. The model employs social network analysis metric including centrality and rank prestige to identify the influence of actors in the social network.…”

Section: Related Workmentioning

confidence: 99%

“…In such environment where the service providers have no personal experience with unknown service requesters, trust and recommendation models are used to evaluate the trustworthiness of unfamiliar entities. Recently, research in designing defense mechanisms to detect dishonest recommendation in these open distributed environments has been carried out [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. The defense mechanisms against dishonest recommendations has been grouped into two broad categories, namely exogenous method and endogenous method [9].…”

Section: Related Workmentioning

confidence: 99%

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A mechanism for detecting dishonest recommendation in indirect trust computation

Iltaf

Ghafoor

Zia³

2013

J Wireless Com Network

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Indirect trust computation based on recommendations form an important component in trust-based access control models for pervasive environment. It can provide the service provider the confidence to interact with unknown service requesters. However, recommendation-based indirect trust computation is vulnerable to various types of attacks. This paper proposes a defense mechanism for filtering out dishonest recommendations based on a measure of dissimilarity function between the two subsets. A subset of recommendations with the highest measure of dissimilarity is considered as a set of dishonest recommendations. To analyze the effectiveness of the proposed approach, we have simulated three inherent attack scenarios for recommendation models (bad mouthing, ballot stuffing, and random opinion attack). The simulation results show that the proposed approach can effectively filter out the dishonest recommendations based on the majority rule. A comparison between the exiting schemes and our proposed approach is also given.

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Exploiting trust, influence, and physical proximity for improving routing performance in vehicular ad hoc networks: The SCALE protocol

Mantas

Louta

Karetsos

2022

Int J Communication

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Vehicular ad hoc networks (VANETs) are receiving increased attention, and several routing protocols have been proposed already by the research community. In such a framework, social aspects and human mobility are important performance enablers, which however are mostly overlooked. In this work, we propose the socially aware CLWPR (SCALE) protocol, significantly enhancing cross-layer weighted position-based routing (CLWPR), a routing protocol for urban VANET environments, combining social properties such as trust, influence, and users' individual mobility patterns to its core design in order to support an efficient content dissemination scheme. The design principles of SCALE that rely on both online and offline social metrics quantifying relationships on social networking platforms and opportunistic contacts of nodes due to physical proximity, respectively, are presented, while nodes with close online and offline social relationship are favored as next forwarder nodes. Subsequently we provide the performance evaluation of SCALE against other routing protocols in distributed vehicular networks, employing representative urban scenarios with synthetic and real traffic. It is shown that the proposed approach presents improved performance in terms of Packet Delivery Ratio (PDR) and throughput when compared with other similar protocols by an average of 37% in scenarios with synthetic dataset and 58% with real dataset for PDR and by an average of 45% in scenarios with synthetic dataset and 61% with real dataset for throughput.

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A Study on Social Network Metrics and Their Application in Trust Networks

Cited by 40 publications

References 14 publications

Centrality as a Method for the Evaluation of Semantic Resources for Disaster Risk Reduction

Centrality as a Method for the Evaluation of Semantic Resources for Disaster Risk Reduction

A mechanism for detecting dishonest recommendation in indirect trust computation

Exploiting trust, influence, and physical proximity for improving routing performance in vehicular ad hoc networks: The SCALE protocol

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