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Diffusion-aware sampling and estimation in information diffusion networks
, Article Proceedings - 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust and 2012 ASE/IEEE International Conference on Social Computing, SocialCom/PASSAT 2012 ; 2012 , Pages 176-183 ; 9780769548487 (ISBN) ; Rabiee, H. R ; Salehi, M ; Sharif University of Technology
2012
Abstract
Partially-observed data collected by sampling methods is often being studied to obtain the characteristics of information diffusion networks. However, these methods usually do not consider the behavior of diffusion process. In this paper, we propose a novel two-step (sampling/estimation) measurement framework by utilizing the diffusion process characteristics. To this end, we propose a link-tracing based sampling design which uses the infection times as local information without any knowledge about the latent structure of diffusion network. To correct the bias of sampled data, we introduce three estimators for different categories, link-based, node-based, and cascade-based. To the best of...
Correlated cascades: Compete or cooperate
, Article 31st AAAI Conference on Artificial Intelligence, AAAI 2017, 4 February 2017 through 10 February 2017 ; 2017 , Pages 238-244 ; Khodadadi, A ; Farajtabar, M ; Rabiee, H. R ; Zha, H ; Amazon; Artificial Intelligence; Baidu; et al.; IBM; Tencent ; Sharif University of Technology
AAAI press
2017
Abstract
In real world social networks, there are multiple cascades which are rarely independent. They usually compete or cooperate with each other. Motivated by the reinforcement theory in sociology we leverage the fact that adoption of a user to any behavior is modeled by the aggregation of behaviors of its neighbors. We use a multidimensional marked Hawkes process to model users product adoption and consequently spread of cascades in social networks. The resulting inference problem is proved to be convex and is solved in parallel by using the barrier method. The advantage of the proposed model is twofold; it models correlated cascades and also learns the latent diffusion network. Experimental...
Structural virality estimation and maximization in diffusion networks
, Article Expert Systems with Applications ; Volume 206 , 2022 ; 09574174 (ISSN) ; Beigy, H ; Sharif University of Technology
Elsevier Ltd
2022
Abstract
Social media usage is one of the most popular online activities and people shares millions of message in a short time; however this information rarely goes viral. The diffusion process begins with an initial set of source nodes and continues with other nodes. In addition, the viral cascade is triggered when the number of infected nodes exceeds a specific threshold. Then, we find an initial set of source nodes that maximizes the number of infected nodes given the source nodes. This study aims to answer the following questions: how does a spread like a viral cascade propagate in a network? Do the structural properties of the propagation pattern play an important role in virality? If so, can we...
Extracting Cascaded Information Networks FromSocial Networks
,
M.Sc. Thesis
Sharif University of Technology
;
Rabiei, Hamid Reza
(Supervisor)
Abstract
The diffusion process propagates information, viruses, ideas, innovations and new be-haviours over social networks. Adopting a new behaviour, which is mentioned as an in-fection, starts from a little group of people. Spreading it over more neighbors and friendscan result in an epidemic phenomenon over the network. As this infection propagates, aninformation cascade will be generated. The spread of information cascades over social net-works forms the diffusion networks. Although observing the infection time of a person ispossible, determining the source of infection is usually a difficult problem. Additionally, inmany applications we can not observe the underlying network which diffusion...
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...
Link Prediction in Social Networks Using the Diffusion Network Characteristics
, M.Sc. Thesis Sharif University of Technology ; Rabiee, Hamid Reza (Supervisor)
Abstract
Given a snapshot of a network, link preditction methods try to infer future intractions be-tween its nodes. These methods may be used in either analyzing current state of the network or predicting future links of it. Link prediction techniques have many applications among which we can mention recommendation systems. These systems are implemented for com-mercial reasons or preventing user confusion in huge amount of information available.A new perspective toward link prediction is based on supervised random walk. In such methods, a random walker starts from a node in the network and randomly traverses to one of the current node’s neighbours with a probability proportional to the chosen link’s...
Inferring the Diffusion Network in Dynamic Online Social Networks
, Ph.D. Dissertation Sharif University of Technology ; Afshin Hemmatyar, Ali Mohammad (Supervisor) ; Rabiee, Hamid Reza (Supervisor)
Abstract
The emergence of online social networks (OSN) has encouraged scientists to investigate them more deeply due to availability of excessive data and increased computational power. Diffusion is a fundamental process taking place in such networks. The spread of news, ideas, influence and diseases are examples of diffusion in social networks. The importance of information diffusion in different disciplines such as economics, politics and social behavior has motivated us to study diffusion networks in this thesis. Diffusion’s behavior is strongly influenced by the underlying network in social networks. Despite this fact most research done in this area ignores the dynamics of the underlying network....
Inference and Analysis of Hidden Structures of Financial Networks Using Diffusion Models on Complex Network
, M.Sc. Thesis Sharif University of Technology ; Jalili, Mahdi (Supervisor) ; Habibi, Jafar (Supervisor)
Abstract
For along time, analysis of financial markets is an interesting topic for mankind. There are different statistical methods to analyze financial time series. In this manuscript, financial markets are analyzed from network view point. Inference of hidden structures between companies of a market is merit information so we introduce different methods to infer
hidden network structure of financial markets. In addition, we will show that dynamic of network structure can provide important information about external sources of influence on financial markets. Actually, we show affect of political events on financial market of Iran. At last, we generalize diffusion concept at financial networks...
hidden network structure of financial markets. In addition, we will show that dynamic of network structure can provide important information about external sources of influence on financial markets. Actually, we show affect of political events on financial market of Iran. At last, we generalize diffusion concept at financial networks...
Information and Influence Diffusion in Social Network
,
Ph.D. Dissertation
Sharif University of Technology
;
Beigy, Hamid
(Supervisor)
Abstract
People use social networks to share millions of stories every day, but these stories rarely become viral. Can we estimate the probability that a story becomes a u/rof cascade? If so, can we find a set of users that are more likely to trigger viral cascades? There are many factors influenced the message virality. In this thesis, we investigate the effect of graph structure, diffusion pattern as well as the message text on virality measure. Finally, the authors solve both source localization and inferring COVID-19 network via propsed methods.First, the authors investigate probability estimation and maximization of cascade virality. In this section, we develop an efficient viral cascade...
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....
DNE: A method for extracting cascaded diffusion networks from social networks
, Article Proceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom 2011, 9 October 2011 through 11 October 2011 ; October , 2011 , Pages 41-48 ; 9780769545783 (ISBN) ; Rabiee, H. R ; Salehi, M ; Sharif University of Technology
2011
Abstract
The spread of information cascades over social networks forms the diffusion networks. The latent structure of diffusion networks makes the problem of extracting diffusion links difficult. As observing the sources of information is not usually possible, the only available prior knowledge is the infection times of individuals. We confront these challenges by proposing a new method called DNE to extract the diffusion networks by using the time-series data. We model the diffusion process on information networks as a Markov random walk process and develop an algorithm to discover the most probable diffusion links. We validate our model on both synthetic and real data and show the low dependency...
Viral cascade probability estimation and maximization in diffusion networks
, Article IEEE Transactions on Knowledge and Data Engineering ; 28 May , 2018 ; 10414347 (ISSN) ; Beigy, H ; Sharif University of Technology
IEEE Computer Society
2018
Abstract
People use social networks to share millions of stories every day, but these stories rarely become viral. Can we estimate the probability that a story becomes a viral cascade If so, can we find a set of users that are more likely to trigger viral cascades These estimation and maximization problems are very challenging since both rare-event nature of viral cascades and efficiency requirement should be considered. Unfortunately, this problem still remains largely unexplored to date. In this paper, given temporal dynamics of a network, we first develop an efficient viral cascade probability estimation method, VICE, that leverages an special importance sampling approximation to achieve high...
Viral cascade probability estimation and maximization in diffusion networks
, Article IEEE Transactions on Knowledge and Data Engineering ; Volume 31, Issue 3 , 2019 , Pages 589-600 ; 10414347 (ISSN) ; Beigy, H ; Sharif University of Technology
IEEE Computer Society
2019
Abstract
People use social networks to share millions of stories every day, but these stories rarely become viral. Can we estimate the probability that a story becomes a viral cascade? If so, can we find a set of users that are more likely to trigger viral cascades? These estimation and maximization problems are very challenging since both rare-event nature of viral cascades and efficiency requirement should be considered. Unfortunately, this problem still remains largely unexplored to date. In this paper, given temporal dynamics of a network, we first develop an efficient viral cascade probability estimation method, ViCE, that leverages an special importance sampling approximation to achieve high...