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centrality-measures
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An opportunistic network approach towards disease spreading
, Article 6th International Conference on Complex Networks and Their Applications, Complex Networks 2017, 29 November 2017 through 1 December 2017 ; Volume 689 , November , 2018 , Pages 314-325 ; 1860949X (ISSN) ; 9783319721491 (ISBN) ; Khansari, M ; Kaveh, A ; Sharif University of Technology
Springer Verlag
2018
Abstract
Research in modeling epidemic spreading in static networks has reached maturity in recent years. On the contrary, disease spreading in dynamic networks can be considered as an important open issue. Hence, mapping dynamic interactions of crowds to a static network and then immunizing the resulting network is the subject of this paper. In this work, we analogize spreading of diseases in dynamic networks -based on dynamic interactions- to message delivery in opportunistic networks. Thus, different diseases which have different spreading behaviors could be simulated by specific routing protocols. Having used interactions among individuals in the utilized dataset, the resulting network is...
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....
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...
Cascading failure tolerance of modular small-world networks
, Article IEEE Transactions on Circuits and Systems II: Express Briefs ; Volume 58, Issue 8 , 2011 , Pages 527-531 ; 15497747 (ISSN) ; Ghassemieh, H ; Jalili, M ; Sharif University of Technology
2011
Abstract
Many real-world networks have a modular structure, and their component may undergo random errors and/or intentional attacks. More devastating situations may happen if the network components have a limited load capacity; the errors and attacks may lead to a cascading component removal process, and consequently, the network may lose its desired performance. In this brief, we investigate the tolerance of cascading errors and attacks in modular small-world networks. This brief studies the size of the largest connected component of the networks when cascading errors or attacks occur. The robustness of the network is tested as a function of both the intermodular connection and intramodular...
Optimal pinning controllability of complex networks: Dependence on network structure
, Article Physical Review E - Statistical, Nonlinear, and Soft Matter Physics ; Volume 91, Issue 1 , January , 2015 ; 15393755 (ISSN) ; Askari Sichani, O ; Yu, X ; Sharif University of Technology
American Physical Society
2015
Abstract
Controlling networked structures has many applications in science and engineering. In this paper, we consider the problem of pinning control (pinning the dynamics into the reference state), and optimally placing the driver nodes, i.e., the nodes to which the control signal is fed. Considering the local controllability concept, a metric based on the eigenvalues of the Laplacian matrix is taken into account as a measure of controllability. We show that the proposed optimal placement strategy considerably outperforms heuristic methods including choosing hub nodes with high degree or betweenness centrality as drivers. We also study properties of optimal drivers in terms of various centrality...
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
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...
Predicting scientific research trends based on link prediction in keyword networks
, Article Journal of Informetrics ; Volume 14, Issue 4 , 2020 ; Shafaeipour Sarmoor, Z ; Hajsadeghi, K ; Kavousi, K ; Sharif University of Technology
Elsevier Ltd
2020
Abstract
The rapid development of scientific fields in this modern era has raised the concern for prospective scholars to find a proper research field to conduct their future studies. Thus, having a vision of future could be helpful to pick the right path for doing research and ensuring that it is worth investing in. In this study, we use article keywords of computer science journals and conferences, assigned by INSPEC controlled indexing, to construct a temporal scientific knowledge network. By observing keyword networks snapshots over time, we can utilize the link prediction methods to foresee the future structures of these networks. We use two different approaches for this link prediction problem....
Centrality-based epidemic control in complex social networks
, Article Social Network Analysis and Mining ; Volume 10, Issue 1 , 2020 ; Rabiee, H. R ; Khan, U. A ; Sharif University of Technology
Springer
2020
Abstract
Recent progress in the areas of network science and control has shown a significant promise in understanding and analyzing epidemic processes. A well-known model to study epidemics processes used by both control and epidemiological research communities is the susceptible–infected–susceptible (SIS) dynamics to model the spread of disease/viruses over contact networks of infected and susceptible individuals. The SIS model has two metastable equilibria: one is called the endemic equilibrium and the other is known as the disease-free or healthy-state equilibrium. Control theory provides the tools to design control actions (allocating curing or vaccination resources) in order to achieve and...
A graph weighting method for reducing consensus time in random geographical networks
, Article 24th IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2010, 20 April 2010 through 23 April 2010, Perth ; 2010 , Pages 317-322 ; 9780769540191 (ISBN) ; Sharif University of Technology
2010
Abstract
Sensor networks are increasingly employed in many applications ranging from environmental to military cases. The network topology used in many sensor network applications has a kind of geographical structure. A graph weighting method for reducing consensus time in random geographical networks is proposed in this paper. We consider a method based on the mutually coupled oscillators for providing general consensus in the network. In this way, one can relate the consensus time to the properties of the Laplacian matrix of the connection graph, i.e. to the second smallest eigenvalue (algebraic connectivity). Our weighting algorithm is based on the node and edge between centrality measures. The...