A Comprehensive Algorithm for Evaluating Node Influences in Social Networks Based on Preference Analysis and Random Walk

Mao, Chengying; Xiao, Weisong

doi:10.1155/2018/1528341

Cited by 22 publications

(14 citation statements)

References 53 publications

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“…Apart from the community clustering and information diffusion, GT concepts can be applied to represent the users' influence/trust on each other in a SN [116]. An example of experts' influence network is shown in Figure 28.…”

Section: Users' Influence/trust Score Representation In a Social Netwmentioning

confidence: 99%

Graph Theory: A Comprehensive Survey about Graph Theory Applications in Computer Science and Social Networks

Majeed

Rauf

2020

Inventions

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Graph theory (GT) concepts are potentially applicable in the field of computer science (CS) for many purposes. The unique applications of GT in the CS field such as clustering of web documents, cryptography, and analyzing an algorithm’s execution, among others, are promising applications. Furthermore, GT concepts can be employed to electronic circuit simplifications and analysis. Recently, graphs have been extensively used in social networks (SNs) for many purposes related to modelling and analysis of the SN structures, SN operation modelling, SN user analysis, and many other related aspects. Considering the widespread applications of GT in SNs, this article comprehensively summarizes GT use in the SNs. The goal of this survey paper is twofold. First, we briefly discuss the potential applications of GT in the CS field along with practical examples. Second, we explain the GT uses in the SNs with sufficient concepts and examples to demonstrate the significance of graphs in SN modeling and analysis.

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Section: Users' Influence/trust Score Representation In a Social Netwmentioning

confidence: 99%

Graph Theory: A Comprehensive Survey about Graph Theory Applications in Computer Science and Social Networks

Majeed

Rauf

2020

Inventions

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“…The correctness of the rankings obtained from the proposed ranking methodology is evaluated by the Kendall's tau [31] metric commonly used in the field of information retrieval. Kendall's tau has been chosen given its wide use in the literature [23,[35][36][37][38] and has been shown to be a more robust and efficient metrics than the others [39]. We have also used rankbiased overlap (RBO) metric that puts more importance to the top of the ranked list similar to the weighted Kendall's tau [40] as our work is focussed on identifying the top ranked HS.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Ranking potentially harmful Tor hidden services: Illicit drugs perspective

Faizan

Khan

Agrawal

2020

ACI

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Cryptomarkets on the dark web have emerged as a hub for the sale of illicit drugs. They have made it easier for the customers to get access to illicit drugs online while ensuring their anonymity. The easy availability of potentially harmful drugs has resulted in a significant impact on public health. Consequently, law enforcement agencies put a lot of effort and resources into shutting down online markets on the dark web. A lot of research work has also been conducted to understand the working of customers and vendors involved in the cryptomarkets that may help the law enforcement agencies. In this research, we present a ranking methodology to identify and rank top markets dealing in harmful illicit drugs. Using named entity recognition, a harm score of a drug market is calculated to indicate the degree of threat followed by the ranking of drug markets. The top-ranked markets are the ones selling the most harmful drugs. The rankings thus obtained can be helpful to law enforcement agencies by locating specific markets selling harmful illicit drugs and their further monitoring.

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“…Current harvesting techniques can extract different types of travel-related information from trajectories [6,7] or a social network [8][9][10] and fuse them to find a ride-share partner. Various kinds of auxiliary data (e.g., spatial dispersion, temporal duration, and movement velocity) become available in ridesharing matching systems.…”

Section: Introductionmentioning

confidence: 99%

Efficient Ridesharing Framework for Ride-matching via Heterogeneous Network Embedding

Tang

Liu

Zhao

et al. 2020

ACM Trans. Knowl. Discov. Data

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Ridesharing has attracted increasing attention in recent years, and combines the flexibility and speed of private cars with the reduced cost of fixed-line systems to benefit alleviating traffic pressure. A major issue in ridesharing is the accurate assignment of passengers to drivers, and how to maximize the number of rides shared between people being assigned to different drivers has become an increasingly popular research topic. There are two major challenges facing ride-matching: scalability and sparsity. Here, we show that network embedding drives the optimal matches between drivers and riders. Contrary to existing approaches that merely depend on the proximity between passengers and drivers, we employ a heterogeneous network to learn the latent semantics from different choices in two types of ridesharing, and extract features in terms of user trajectories and sentiment. A novel framework for ridesharing, RShareForm, which encodes not only the objects but also a variety of semantic relationships between them, is proposed. This article extends the existing skip-gram model to incorporate meta-paths over a proposed heterogeneous network. It allows diverse features to be used to search for similar participants and then ranks them to improve the quality of ride-matching. Extensive experiments on a large-scale dataset from DiDi in Chengdu, China show that by leveraging heterogeneous network embedding with meta paths, RShareForm can significantly improve the accuracy of identifying the participants for ridesharing over existing methods, including both meta-path guided similarity search methods and variants of embedding methods. CCS Concepts: • Human-centered computing → Empirical studies in ubiquitous and mobile computing;

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A Comprehensive Algorithm for Evaluating Node Influences in Social Networks Based on Preference Analysis and Random Walk

Cited by 22 publications

References 53 publications

Graph Theory: A Comprehensive Survey about Graph Theory Applications in Computer Science and Social Networks

Graph Theory: A Comprehensive Survey about Graph Theory Applications in Computer Science and Social Networks

Ranking potentially harmful Tor hidden services: Illicit drugs perspective

Efficient Ridesharing Framework for Ride-matching via Heterogeneous Network Embedding

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