CLP-ID: Community-based link prediction using information diffusion

2020 ◽  
Vol 514 ◽  
pp. 402-433
Author(s):  
Shashank Sheshar Singh ◽  
Shivansh Mishra ◽  
Ajay Kumar ◽  
Bhaskar Biswas
Keyword(s):  
Link Prediction ◽  
Information Diffusion ◽  
Community Based

Link Prediction Based on Stochastic Information Diffusion

2021 ◽  
pp. 1-11
Author(s):  
Didier A. Vega-Oliveros ◽  
Liang Zhao ◽  
Anderson Rocha ◽  
Lilian Berton
Keyword(s):  
Link Prediction ◽  
Information Diffusion

scholarly journals Impact of community-based health insurance on utilisation of preventive health services in rural Uganda: a propensity score matching approach

2021 ◽  
Author(s):  
Emmanuel Nshakira-Rukundo ◽  
Essa Chanie Mussa ◽  
Nathan Nshakira ◽  
Nicolas Gerber ◽  
Joachim von Braun
Keyword(s):  
Developing Countries ◽  
Health Insurance ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Information Diffusion ◽  
Preventive Health ◽  
Financial Protection ◽  
Community Based ◽  
Research Attention ◽  
Community Based Health Insurance

AbstractThe effect of voluntary health insurance on preventive health has received limited research attention in developing countries, even when they suffer immensely from easily preventable illnesses. This paper surveys households in rural south-western Uganda, which are geographically serviced by a voluntary Community-based health insurance scheme, and applied propensity score matching to assess the effect of enrolment on using mosquito nets and deworming under-five children. We find that enrolment in the scheme increased the probability of using a mosquito net by 26% and deworming by 18%. We postulate that these findings are partly mediated by information diffusion and social networks, financial protection, which gives households the capacity to save and use service more, especially curative services that are delivered alongside preventive services. This paper provides more insight into the broader effects of health insurance in developing countries, beyond financial protection and utilisation of hospital-based services.

Community-based disaster communication: how does it become trustworthy?

2016 ◽  
Vol 25 (4) ◽  
pp. 478-491 ◽  
Author(s):  
Dwie Irmawaty Gultom
Keyword(s):  
Disaster Response ◽  
Information Diffusion ◽  
Weak Ties ◽  
Network Ties ◽  
Community Based ◽  
Strong Ties ◽  
Content Type ◽  
Individual Level ◽  
Disaster Communication ◽  
Disaster Information

Purpose – Trust in disaster communication is significant because a lack of trust will prevent the transformation of information into usable knowledge for an effective disaster response. Therefore, the purpose of this paper is to investigate how the culture and network ties of an affected community can encourage trust and participation in disaster communication. Design/methodology/approach – A qualitative case study of Jalin Merapi (JM) was conducted by interviewing 33 research participants in the Mt Merapi surroundings. Findings – The findings indicate that culture-embedded disaster communication plays important roles in increasing the effectiveness of disaster information and encouraging trust in the authenticity of locally based disaster information at the individual level. The findings also identify that strong ties and weak ties play different roles in disaster communication. The strong ties are more effective in facilitating information diffusion and encourage trust and community participation within the affected community. Furthermore, the weak ties are more effective in disseminating information to wider audiences, and have an indirect influence in encouraging trust by extending the offline social network owned by the affected community. Originality/value – Most literature on disaster communication focusses on the construction of disaster messages to encourage effective disaster response. Less attention has been paid to the information receivers regarding how disaster information is considered to be trustworthy by the affected community and how it can increase collective participation in community-based disaster communication.

Identifying a Set of Key Members in Social Networks Using SDP-Based Stochastic Search and Integer Programming Algorithms

2017 ◽  
Vol 34 (03) ◽  
pp. 1750002 ◽  
Author(s):  
Wentao Wu ◽  
Wai Kin Victor Chan ◽  
Lei Chi ◽  
Zhiguo Gong
Keyword(s):  
Information Diffusion ◽  
Integer Program ◽  
Stochastic Search ◽  
Scale Free Network ◽  
Final Solution ◽  
Community Based ◽  
Scale Free ◽  
Free Network ◽  
Key Player Problem ◽  
Programming Algorithms

This paper presents two semi-definite programming (SDP) based methods to solve the Key Player Problem (KPP). The KPP is to identify a set of [Formula: see text] nodes (i.e., key players) from a social network of size [Formula: see text] such that the number of nodes connected to these [Formula: see text] nodes is maximized. The KPP has applications in social diffusion and products adoption as it helps maximizing information diffusion and impact. We first formulate the KPP as an integer program (IP) and then convert it into an SDP formulation, which can be solved efficiently and produce a set of high quality candidate solutions. We develop an IP-based algorithm and a stochastic search (greedy) algorithm to find the final solution for the KPP. We compare our algorithms with existing methods in small and large networks with different network structures, including random graph, scale-free network, and community-based scale-free network (CSN). Computational results show that our algorithms are more efficient in solving the KPP in all networks. In addition, we examine how the network structure influences the nodes coverage. It is found that CSNs allow the highest nodes coverage due to their community and scale-free structure.

scholarly journals Educational Resource Management in Grid Community Based on Learning Object Metadata Standard

2018 ◽  
Vol 13 (11) ◽  
pp. 130
Author(s):  
Yuhang Chen
Keyword(s):  
Resource Management ◽  
Information Diffusion ◽  
Educational Resources ◽  
Learning Object ◽  
Educational Resource ◽  
Community Group ◽  
Community Based ◽  
Metadata Standard ◽  
Learning Object Metadata ◽  
Peer Community

To manage the educational resources of the grid community based on the standard of learning object metadata, the resource classification standard of the learning object metadata standard is taken as the basis of the grid community division. According to the principle of the classification of educational resources, and based on the characteristics of the grid management system and the features of the grid community, the construction and internal structure of grid community are discussed, and the idea of constructing peer community group is proposed. In accordance with the idea of peer community, similar educational resources achieve a logical connection between peer communities. The results show that the mechanism of information sharing and information diffusion is established among communities, and the framework of educational resource management is constructed through simulation and evaluation.

A community-based approach to identify the most influential nodes in social networks

2016 ◽  
Vol 43 (2) ◽  
pp. 204-220 ◽  
Author(s):  
Maryam Hosseini-Pozveh ◽  
Kamran Zamanifar ◽  
Ahmad Reza Naghsh-Nilchi
Keyword(s):  
Social Networks ◽  
Information Diffusion ◽  
Closeness Centrality ◽  
Centrality Measures ◽  
Community Based ◽  
Spreading Process ◽  
Centrality Measure ◽  
Influence Spread ◽  
The Social ◽  
Influential Nodes

One of the important issues concerning the spreading process in social networks is the influence maximization. This is the problem of identifying the set of the most influential nodes in order to begin the spreading process based on an information diffusion model in the social networks. In this study, two new methods considering the community structure of the social networks and influence-based closeness centrality measure of the nodes are presented to maximize the spread of influence on the multiplication threshold, minimum threshold and linear threshold information diffusion models. The main objective of this study is to improve the efficiency with respect to the run time while maintaining the accuracy of the final influence spread. Efficiency improvement is obtained by reducing the number of candidate nodes subject to evaluation in order to find the most influential. Experiments consist of two parts: first, the effectiveness of the proposed influence-based closeness centrality measure is established by comparing it with available centrality measures; second, the evaluations are conducted to compare the two proposed community-based methods with well-known benchmarks in the literature on the real datasets, leading to the results demonstrate the efficiency and effectiveness of these methods in maximizing the influence spread in social networks.

scholarly journals A Community-Based Approach for Link Prediction in Signed Social Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Saeed Reza Shahriary ◽  
Mohsen Shahriari ◽  
Rafidah MD Noor
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Link Prediction ◽  
Large Scale ◽  
Prediction Problem ◽  
Signed Graphs ◽  
Community Based ◽  
Ranking Algorithms ◽  
Detection Approach ◽  
Signed Social Networks

In signed social networks, relationships among nodes are of the types positive (friendship) and negative (hostility). One absorbing issue in signed social networks is predicting sign of edges among people who are members of these networks. Other than edge sign prediction, one can define importance of people or nodes in networks via ranking algorithms. There exist few ranking algorithms for signed graphs; also few studies have shown role of ranking in link prediction problem. Hence, we were motivated to investigate ranking algorithms availed for signed graphs and their effect on sign prediction problem. This paper makes the contribution of using community detection approach for ranking algorithms in signed graphs. Therefore, community detection which is another active area of research in social networks is also investigated in this paper. Community detection algorithms try to find groups of nodes in which they share common properties like similarity. We were able to devise three community-based ranking algorithms which are suitable for signed graphs, and also we evaluated these ranking algorithms via sign prediction problem. These ranking algorithms were tested on three large-scale datasets: Epinions, Slashdot, and Wikipedia. We indicated that, in some cases, these ranking algorithms outperform previous works because their prediction accuracies are better.

scholarly journals Exploiting Information Diffusion Feature for Link Prediction in Sina Weibo

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Dong Li ◽  
Yongchao Zhang ◽  
Zhiming Xu ◽  
Dianhui Chu ◽  
Sheng Li
Keyword(s):  
Link Prediction ◽  
Information Diffusion ◽  
Sina Weibo

scholarly journals HM-EIICT: Fairness-aware link prediction in complex networks using community information

2021 ◽  
Author(s):  
Akrati Saxena ◽  
George Fletcher ◽  
Mykola Pechenizkiy
Keyword(s):  
Network Topology ◽  
Online Social Networks ◽  
Link Prediction ◽  
Prediction Method ◽  
High Accuracy ◽  
Community Based ◽  
Research Directions ◽  
Open Research ◽  
Community Information ◽  
Network Modularity

AbstractThe evolution of online social networks is highly dependent on the recommended links. Most of the existing works focus on predicting intra-community links efficiently. However, it is equally important to predict inter-community links with high accuracy for diversifying a network. In this work, we propose a link prediction method, called HM-EIICT, that considers both the similarity of nodes and their community information to predict both kinds of links, intra-community links as well as inter-community links, with higher accuracy. The proposed framework is built on the concept that the connection likelihood between two given nodes differs for inter-community and intra-community node-pairs. The performance of the proposed methods is evaluated using link prediction accuracy and network modularity reduction. The results are studied on real-world networks and show the effectiveness of the proposed method as compared to the baselines. The experiments suggest that the inter-community links can be predicted with a higher accuracy using community information extracted from the network topology, and the proposed framework outperforms several measures especially proposed for community-based link prediction. The paper is concluded with open research directions.

Sign in / Sign up

Export Citation Format

Share Document