NDLI: Assessing the Quality of Diffusion Models Using Real-World Social Network Data

Content Provider	IEEE Xplore Digital Library
Author	Tsung-Ting Kuo San-Chuan Hung Wei-Shih Lin Shou-De Lin Ting-Chun Peng Chia-Chun Shih
Copyright Year	2011
Abstract	Recently, there has been growing interest in understanding information cascading phenomenon on popular social networks such as Face book, Twitter and Plurk. The numerous diffusion events indicate huge governmental and commercial potential. People have proposed several diffusion and cascading models based on certain assumption, but until now we do not know which one is better in predicting information propagation. In this paper, we propose a novel framework that utilizes the micro-blog data to evaluate which model is better under different circumstances. In our framework, we devise two schemes for evaluation: the direct and the indirect schemes. We conduct experiments using three diffusion models on Plurk data. The results show Independent Cascade model outperforms other diffusion models using direct scheme, while Linear Threshold model, Degree, In-Degree and Page Rank perform best using indirect scheme. The main contribution is to provide a general evaluation framework for various diffusion models.
Starting Page	200
Ending Page	205
File Size	190437
Page Count	6
File Format	PDF
ISBN	9781457721748
DOI	10.1109/TAAI.2011.42
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-11
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Social network analysis Biological system modeling Predictive models Data models Information diffusion Integrated circuit modeling Facebook Diffusion model evaluation
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Extracting diffusion channels from real-world social data: A delay-agnostic learning of transmission probabilities

Topic Based Information Diffusion Prediction Model with External Trends

Inferring Latent Co-activation Patterns for Information Diffusion

The roles of network communities in social information diffusion

Influence Strength Aware Diffusion Models for Dynamic Influence Maximization in Social Networks

Exploiting rank-learning models to predict the diffusion of preferences on social networks

Observing Behaviors of Information Diffusion Models for Diverse Topics of Posts on VK

Diffusion of Information in Mobile Social Networks: A Brief Survey

Characterizing Information Diffusion in Online Social Networks with Linear Diffusive Model

Assessing the Quality of Diffusion Models Using Real-World Social Network Data

Similar Documents

Extracting diffusion channels from real-world social data: A delay-agnostic learning of transmission probabilities

Topic Based Information Diffusion Prediction Model with External Trends

Inferring Latent Co-activation Patterns for Information Diffusion

The roles of network communities in social information diffusion

Influence Strength Aware Diffusion Models for Dynamic Influence Maximization in Social Networks

Exploiting rank-learning models to predict the diffusion of preferences on social networks

Observing Behaviors of Information Diffusion Models for Diverse Topics of Posts on VK

Diffusion of Information in Mobile Social Networks: A Brief Survey

Characterizing Information Diffusion in Online Social Networks with Linear Diffusive Model

Assessing the Quality of Diffusion Models Using Real-World Social Network Data