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Community detection using diffusion information
, Article ACM Transactions on Knowledge Discovery from Data ; Volume 12, Issue 2 , 2018 ; 15564681 (ISSN) ; Khodadadi, A ; Rabiee, H. R ; Sharif University of Technology
Association for Computing Machinery
2018
Abstract
Community detection in social networks has become a popular topic of research during the last decade. There exist a variety of algorithms for modularizing the network graph into different communities. However, they mostly assume that partial or complete information of the network graphs are available that is not feasible in many cases. In this article, we focus on detecting communities by exploiting their diffusion information. To this end, we utilize the Conditional Random Fields (CRF) to discover the community structures. The proposed method, community diffusion (CoDi), does not require any prior knowledge about the network structure or specific properties of communities. Furthermore, in...
Community Detection in Social Networks by Using Information from Diffusion Network
, M.Sc. Thesis Sharif University of Technology ; Rabiee, Hamid Reza (Supervisor)
Abstract
Nowadays, Online Social Networks (OSNs) play an important role in the exchange of information among people. Some previous studies indicate that diffusion behavior and network structure are tightly related. Community structure is one of the most important features of OSNs. Access to the whole network topology is the necessary and prevalent requirement for most of community detection methods, so the limited access to full or partial topology can decrease their accuracy. Using traceable information over diffusion network is a solution to surmount this difficulty. In this work, we are concerned with the community detection by only using the diffusion information, while unlike the previous...
Sampling from diffusion networks
, Article Proceedings of the 2012 ASE International Conference on Social Informatics ; 2013 , Pages 106-112 ; 9780769550152 (ISBN) ; Rabiee, H. R ; Salehi, M ; Academy of Science and Engineering (ASE) ; Sharif University of Technology
2013
Abstract
The diffusion phenomenon has a remarkable impact on Online Social Networks (OSNs). Gathering diffusion data over these large networks encounters many challenges which can be alleviated by adopting a suitable sampling approach. The contributions of this paper is twofold. First we study the sampling approaches over diffusion networks, and for the first time, classify these approaches into two categories, (1) Structure-based Sampling (SBS), and (2) Diffusion-based Sampling (DBS). The dependency of the former approach to topological features of the network, and unavailability of real diffusion paths in the latter, converts the problem of choosing an appropriate sampling approach to a trade-off....
Inferring dynamic diffusion networks in online media
, Article ACM Transactions on Knowledge Discovery from Data ; Volume 10, Issue 4 , 2016 ; 15564681 (ISSN) ; Hemmatyar, A. M. A ; Rabiee, H. R ; Ramezani, M ; Sharif University of Technology
Association for Computing Machinery
Abstract
Online media play an important role in information societies by providing a convenient infrastructure for different processes. Information diffusion that is a fundamental process taking place on social and information networks has been investigated in many studies. Research on information diffusion in these networks faces two main challenges: (1) In most cases, diffusion takes place on an underlying network, which is latent and its structure is unknown. (2) This latent network is not fixed and changes over time. In this article, we investigate the diffusion network extraction (DNE) problem when the underlying network is dynamic and latent. We model the diffusion behavior (existence...
Adjoint Inverse Modeling of PM2.5 Emissions in Order to Improve Performance of Air Quality Models
, Ph.D. Dissertation Sharif University of Technology ; Hosseini, Vahid (Supervisor) ; Mozafari, Ali Asghar (Supervisor)
Abstract
In atmospheric studies, chemical transport models are formulated to simulate the spatial and temporal distribution of pollutant concentrations. However, the performance of these models is strongly dependent on the input parameters such as emissions. Inverse modeling is a widely used mathematical approach for estimating model parameters by minimizing the discrepancy between model output and observations. For air quality studies, inverse modeling is often used for emission inversion as emissions are associated with significant amount of uncertainties.This research aims to estimate optimal values for anthropogenic PM2.5 emission through a four-dimensional variational (4D-Var) inverse modeling...