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Centrality-based group formation in group recommender systems
, Article 26th International World Wide Web Conference, WWW 2017 Companion, 3 April 2017 through 7 April 2017 ; 2019 , Pages 1187-1196 ; 9781450349147 (ISBN) ; Khalili, S ; Elahe Ghalebi, K ; Grosu, R ; Mojde Morshedi, S ; Movaghar, A ; Sharif University of Technology
International World Wide Web Conferences Steering Committee
2019
Abstract
Recommender Systems have become an attractive field within the recent decade because they facilitate users' selection process within limited time. Conventional recommender systems have proposed numerous methods focusing on recommendations to individual users. Recently, due to a significant increase in the number of users, studies in this field have shifted to properly identify groups of people with similar preferences and provide a list of recommendations for each group. Offering a recommendations list to each individual requires significant computational cost and it is therefore often not efficient. So far, most of the studies impose four restrictive assumptions: (1) limited number of...
Analyzing the Interplay of Power Management and Communication Reliability of Wireless Networks in Embedded Applications
, M.Sc. Thesis Sharif University of Technology ; Ejlali, Alireza (Supervisor)
Abstract
Due to safety critical applications of the embedded systems, reliability is one of the most important properties of these systems. Wireless networks also have error prone data transmission channels and limited power consumption. Therefore reliability and power conservation are two vital characteristics of the wireless network especially in embedded applications. Since achieving to a desirable level of data reliability is power consuming, we must consider a trade-off between power consumption and data reliability. In wireless networks power consumption can be categorized into: power consumption in wireless devices and power consumption in data transmission. For decrement of wireless devices...
Live Layered Video Streaming over Multichannel P2P Networks
, M.Sc. Thesis Sharif University of Technology ; Rabiee, Hamid Reza (Supervisor)
Abstract
Nowadays, video streaming over peer-to-peer networks has become an interesting field to deliver video in large scale networks. As multi-channel live video streaming networks increase,distributing video with high quality among channels faces many challenges. The most significant challenges cause from frequent channel churns, unbalanced channel resources, network heterogeneity and diversity of users’ bandwidths. They include: nodes’ unstability, low users participations, large startup and playback delays, low video quality received by users and lack of resources in unpopular channels.In order to solve the above problems, we have proposed several solutions such as: 1- using distribution groups...
Confidential Access to the Outsourced Relational Data
, M.Sc. Thesis Sharif University of Technology ; Jalili, Rasool (Supervisor)
Abstract
In recent years, there has been a trend toward outsourcing data to the cloud provider. These companies must tackle the data security challenges. Generally these parties are assumed to be honest but curious. In past years, the research communities have been investigating different solution to ensure confidentiality.
In addition to data confidentiality access and pattern confidentiality is a high-priority issue in some cases so. potential adversary should be unable to drive information from the observed access pattern to the outsourced data. Despite the fact that there are more investigation in the field of data confidentiality, concern over data security are the rise in outsourcing data,...
In addition to data confidentiality access and pattern confidentiality is a high-priority issue in some cases so. potential adversary should be unable to drive information from the observed access pattern to the outsourced data. Despite the fact that there are more investigation in the field of data confidentiality, concern over data security are the rise in outsourcing data,...
A low-cost sparse recovery framework for weighted networks under compressive sensing
, Article Proceedings - 2015 IEEE International Conference on Smart City, SmartCity 2015, Held Jointly with 8th IEEE International Conference on Social Computing and Networking, SocialCom 2015, 5th IEEE International Conference on Sustainable Computing and Communications, SustainCom 2015, 2015 International Conference on Big Data Intelligence and Computing, DataCom 2015, 5th International Symposium on Cloud and Service Computing, SC2 2015, 19 December 2015 through 21 December 2015 ; 2015 , Pages 183-190 ; 9781509018932 (ISBN) ; Rabiee, H. R ; Movaghar, A ; Hasheminezhad, R ; Ghalebi, E ; Nazemian, A ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2015
Abstract
In this paper, motivated by network inference, we introduce a general framework, called LSR-Weighted, to efficiently recover sparse characteristic of links in weighted networks. The links in many real-world networks are not only binary entities, either present or not, but rather have associated weights that record their strengths relative to one another. Such models are generally described in terms of weighted networks. The LSR-Weighted framework uses a newly emerged paradigm in sparse signal recovery named compressive sensing. We study the problem of recovering sparse link vectors with network topological constraints over weighted networks. We evaluate performance of the proposed framework...
HellRank: a hellinger-based centrality measure for bipartite social networks
, Article Social Network Analysis and Mining ; Volume 7, Issue 22 , 2017 ; 18695450 (ISSN) ; Mahyar, H ; Firouzi, M ; Ghalebi, E ; Grosu, R ; Movaghar, A ; Sharif University of Technology
2017
Abstract
Measuring centrality in a social network, especially in bipartite mode, poses many challenges, for example, the requirement of full knowledge of the network topology, and the lack of properly detecting top-kbehavioral representative users. To overcome the above mentioned challenges, we propose HellRank, an accurate centrality measure for identifying central nodes in bipartite social networks. HellRank is based on the Hellinger distance between two nodes on the same side of a bipartite network. We theoretically analyze the impact of this distance on a bipartite network and find upper and lower bounds for it. The computation of the HellRank centrality measure can be distributed, by letting...
Investigate Laboratory Production of Hydroxyurea
, M.Sc. Thesis Sharif University of Technology ; Bastani, Dariush (Supervisor) ; Seifkordi, Ali Akbar (Supervisor) ; Zegordi, Elahe (Co-Advisor)
Abstract
Hydroxyurea is an anticancer drug that is commonly used to treat chronic myeloid leukemia (white blood cell cancer in bone marrow), polycythemia vera (red blood cell cancer), sickle cell anemia, solid tumors, and in particular leukemia. In this research, in addition to analyzing all available methods for synthesis of hydroxyurea, the most economical method for synthesis of hydroxyurea which is Hantzsch method was chosen; then we synthesized hydroxyurea crystals using a precipitation or crystallization process. To do so, two hydroxylamine hydrochloride and potassium cyanate reactants have been used in the semi batch reactor. Since the reaction efficiency and purity of the synthesized...
Extracting implicit social relation for social recommendation techniques in user rating prediction
, Article 26th International World Wide Web Conference, WWW 2017 Companion, 3 April 2017 through 7 April 2017 ; 2019 , Pages 1343-1351 ; 9781450349147 (ISBN) ; Elahe Ghalebi, K ; Mahyar, H ; Grosu, R ; Firouzi, M ; Movaghar, A ; Sharif University of Technology
International World Wide Web Conferences Steering Committee
2019
Abstract
Recommendation plays an increasingly important role in our daily lives. Recommender systems automatically suggest items to users that might be interesting for them. Recent studies illustrate that incorporating social trust in Matrix Factorization methods demonstrably improves accuracy of rating prediction. Such approaches mainly use the trust scores explicitly expressed by users. However, it is often challenging to have users provide explicit trust scores of each other. There exist quite a few works, which propose Trust Metrics to compute and predict trust scores between users based on their interactions. In this paper, first we present how social relation can be extracted from users'...
Extracting implicit social relation for social recommendation techniques in user rating prediction
, Article 26th International World Wide Web Conference, WWW 2017 Companion, 3 April 2017 through 7 April 2017 ; 2019 , Pages 1343-1351 ; 9781450349147 (ISBN) ; Elahe Ghalebi, K ; Mahyar, H ; Grosu, R ; Firouzi, M ; Movaghar, A ; Sharif University of Technology
International World Wide Web Conferences Steering Committee
2019
Abstract
Recommendation plays an increasingly important role in our daily lives. Recommender systems automatically suggest items to users that might be interesting for them. Recent studies illustrate that incorporating social trust in Matrix Factorization methods demonstrably improves accuracy of rating prediction. Such approaches mainly use the trust scores explicitly expressed by users. However, it is often challenging to have users provide explicit trust scores of each other. There exist quite a few works, which propose Trust Metrics to compute and predict trust scores between users based on their interactions. In this paper, first we present how social relation can be extracted from users'...
Identifying central nodes for information flow in social networks using compressive sensing
, Article Social Network Analysis and Mining ; Volume 8, Issue 1 , 2018 ; 18695450 (ISSN) ; Hasheminezhad, R ; Ghalebi, E ; Nazemian, A ; Grosu, R ; Movaghar, A ; Rabiee, H. R ; Sharif University of Technology
Springer-Verlag Wien
2018
Abstract
This paper addresses the problem of identifying central nodes from the information flow standpoint in a social network. Betweenness centrality is the most prominent measure that shows the node importance from the information flow standpoint in the network. High betweenness centrality nodes play crucial roles in the spread of propaganda, ideologies, or gossips in social networks, the bottlenecks in communication networks, and the connector hubs in biological systems. In this paper, we introduce DICeNod, a new approach to efficiently identify central nodes in social networks without direct measurement of each individual node using compressive sensing, which is a well-known paradigm in sparse...
Compressive sensing of high betweenness centrality nodes in networks
, Article Physica A: Statistical Mechanics and its Applications ; Volume 497 , 1 May , 2018 , Pages 166-184 ; 03784371 (ISSN) ; Hasheminezhad, R ; Ghalebi, E ; Nazemian, A ; Grosu, R ; Movaghar, A ; Rabiee, H. R ; Sharif University of Technology
Elsevier B.V
2018
Abstract
Betweenness centrality is a prominent centrality measure expressing importance of a node within a network, in terms of the fraction of shortest paths passing through that node. Nodes with high betweenness centrality have significant impacts on the spread of influence and idea in social networks, the user activity in mobile phone networks, the contagion process in biological networks, and the bottlenecks in communication networks. Thus, identifying k-highest betweenness centrality nodes in networks will be of great interest in many applications. In this paper, we introduce CS-HiBet, a new method to efficiently detect top-k betweenness centrality nodes in networks, using compressive sensing....
CS-ComDet: A compressive sensing approach for inter-community detection in social networks
, Article Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015, 25 August 2015 through 28 August 2015 ; 2015 , Pages 89-96 ; 9781450338547 (ISBN) ; Rabiee, H. R ; Movaghar, A ; Ghalebi, E ; Nazemian, A ; Pei, J ; Tang, J ; Silvestri, F ; Sharif University of Technology
Association for Computing Machinery, Inc
2015
Abstract
One of the most relevant characteristics of social networks is community structure, in which network nodes are joined together in densely connected groups between which there are only sparser links. Uncovering these sparse links (i.e. intercommunity links) has a significant role in community detection problem which has been of great importance in sociology, biology, and computer science. In this paper, we propose a novel approach, called CS-ComDet, to efficiently detect the inter-community links based on a newly emerged paradigm in sparse signal recovery, called compressive sensing. We test our method on real-world networks of various kinds whose community structures are already known, and...