A DANI: A Fast Diffusion Aware Network Inference Algorithm MARYAM RAMEZANI, Sharif University of Technology HAMID R. RABIEE, Sharif University of Technology MARYAM TAHANI, Sharif University of Technology AREZOO RAJABI, Sharif University of Technology The fast growth of social networks and their privacy requirements in recent years, has lead to increasing difficulty in obtaining complete topology of these networks. However, diffusion information over these net- works is available and many algorithms have been proposed to infer the underlying networks by using this information. The previously proposed algorithms only focus on inferring more links and do not pay atten- tion to the important characteristics of the underlying social networks In this paper, we propose a novel algorithm, called DANI, to infer the underlying network structure while preserving its properties by using the diffusion information. Moreover, the running time of the proposed method is considerably lower than the previous methods. We applied the proposed method to both real and synthetic networks. The experi- mental results showed that DANI has higher accuracy and lower run time compared to well-known network inference methods. Categories and Subject Descriptors: G.2.2 [Graph Theory]: Graph algorithms; H.3.3 [Information Stor- age and Retrieval]: Information Filtering, Information Search and Retrieval General Terms: Algorithms; Experimentation; Performance Additional Key Words and Phrases: Social Networks, Network Inference, Diffusion Information, Community Structure, Random Process Model ACM Reference Format: Maryam Ramezani, Hamid R. Rabiee, Maryam Tahani and Arezoo Rajabi, 2014. DANI: A Fast Community- Preserving, Diffusion Aware Network Inference Algorithm. ACM Transaction on Intelligent Systems and Technology. ACM V, N, Article A (January YYYY), 27 pages. DOI:http://dx.doi.org/10.1145/0000000.0000000 1. INTRODUCTION Online Social Networks (OSNs) that play an important role in the exchange of infor- mation between people, have grown noticeably in the last few years. Diffusion is a fundamental process over these networks by which information, ideas and new behav- iors, namely contagion, disseminate over the network [Gomez Rodriguez et al. 2010]. Propagation of a contagion over a network creates a trace that is called cascade (Fig. 1) [Gomez Rodriguez et al. 2010]. These cascades represent the diffusion behavior that convey valuable information about the underlying network. Some previous studies indicate that diffusion behavior and network structure are tightly related [Easley and Kleinberg 2010; Eftekhar et al. 2013; Barbieri et al. 2013a; 2013b]. In other words, actions of users do not only emanate from their individual Author’s addresses: M. Ramezani (m ramezani@ce.sharif.edu), H. R. Rabiee (rabiee@sharif.edu), M. Tahani (tahani@ce.sharif.edu) and A. Rajabi (arezoorajabi@ce.sharif.edu), Department of Computer Engineering, Sharif University of Technology, Tehran, Iran. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is per- mitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA, fax +1 (212) 869-0481, or permissions@acm.org. c  YYYY ACM 0000-0000/YYYY/01-ARTA $15.00 DOI:http://dx.doi.org/10.1145/0000000.0000000 ACM Transactions on Intelligent Systems and Technology, Vol. V, No. N, Article A, Publication date: January YYYY. arXiv:1706.00941v1 [cs.SI] 3 Jun 2017