VNU-UET Repository: No conditions. Results ordered -Date Deposited. 2024-03-29T13:55:15ZEPrintshttp://eprints.uet.vnu.edu.vn/images/sitelogo.pnghttps://eprints.uet.vnu.edu.vn/eprints/2020-10-13T08:18:56Z2020-10-13T08:20:11Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4073This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/40732020-10-13T08:18:56ZInfluence Maximization with Priority in Online
Social NetworksThe Influence Maximization (IM) problem, which finds a set of k nodes (called seedset)
in a social network to initiate the influence spread so that the number of influenced nodes after
propagation process is maximized, is an important problem in information propagation and social
network analysis. However, previous studies ignored the constraint of priority that led to inefficient
seed collections. In some real situations, companies or organizations often prioritize influencing
potential users during their influence diffusion campaigns. With a new approach to these existing
works, we propose a new problem called Influence Maximization with Priority (IMP) which finds out
a set seed of k nodes in a social network to be able to influence the largest number of nodes subject
to the influence spread to a specific set of nodes U (called priority set) at least a given threshold T in
this paper. We show that the problem is NP-hard under well-known IC model. To find the solution,
we propose two efficient algorithms, called Integrated Greedy (IG) and Integrated Greedy Sampling (IGS)
with provable theoretical guarantees. IG provides a �
1 − (1 − 1
k
)
t
�
-approximation solution with t
is an outcome of algorithm and t ≥ 1. The worst-case approximation ratio is obtained when t = 1
and it is equal to 1/k. In addition, IGS is an efficient randomized approximation algorithm based
on sampling method that provides a �
1 − (1 − 1
k
)
t − e
�
-approximation solution with probability
at least 1 − δ with e > 0, δ ∈ (0, 1) as input parameters of the problem. We conduct extensive
experiments on various real networks to compare our IGS algorithm to the state-of-the-art algorithms
in IM problem. The results indicate that our algorithm provides better solutions interns of influence
on the priority sets when approximately give twice to ten times higher than threshold T while running
time, memory usage and the influence spread also give considerable results compared to the others.Xuan Huan Hoanghuanhx@vnu.edu.vnVan Canh PhamK.T. Dung HaC Quang VuSu Anh Nguyen2019-02-18T03:42:27Z2019-02-18T03:42:27Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/3435This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/34352019-02-18T03:42:27ZMaximizing misinformation restriction within time and budget constraintsOnline social networks have become popular media worldwide. However,they also allow rapid dissemination of misinformation causing negative impacts tousers. With a source of misinformation, the longer the misinformation spreads, thegreater the number of affected users will be. Therefore, it is necessary to preventthe spread of misinformation in a specific time period. In this paper, we proposemaximizing misinformation restriction (MMR) problem with the purpose of finding aset of nodes whose removal from a social network maximizes the influence reductionfrom the source of misinformation within time and budget constraints. We demonstratethat theMMRproblem is NP-hard even in the case where the network is a rooted treeCanh V. Phammaicanhki@gmail.comMy T. ThaiHieu V. DuongBao Q. BuiXuan Huan Hoanghuanhx@vnu.edu.vn2017-12-20T02:40:31Z2017-12-20T02:40:31Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/2797This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/27972017-12-20T02:40:31ZTransitivity Demolition and the Fall of Social NetworksIn this paper, we study crucial elements of a complex network, namely its nodes and connections, which play a key role in maintaining the network’s structure and function under unexpected structural perturbations of nodes and edges removal. Specifically, we want to identify vital nodes and edges whose failure (either random or intentional) will break the most number of connected triples(or triangles)in the network. This problem is extremely important, because connected triples form the foundation of strong connections in many real-world systems, such as mutual relationships in social networks, reliable data transmission in communication networks, and stable routing strategies in mobile networks. Disconnected triples, analog to broken mutual connections, can greatly affect the network’s structure and disrupt its normal function, which can further lead to the corruption of the entire system. The analysis of such crucial elements will shed light on key factors behind the resilience and robustness of many complex systems in practice. We formulate the analysis under multiple optimization problems and show their intractability. We next propose efficient approximation algorithms, namely, DAK-n and DAK-e, which guarantee an (1 − 1/e)-approximate ratio (compared with the overall optimal solutions) while having the same time complexity as the best triangle counting and listing algorithm on power-lawnetworks.Thisadvantagemakes ouralgorithmsscaleextremelywellevenforverylargenetworks.Inanapplicationperspective,we perform comprehensive experiments on real social traces with millions of nodes and billions of edges. Empirical results indicate that our approaches achieve comparably better solution quality while are up to 100×faster than the current state-of-the-art methods.The Hung Nguyenthehung912000@gmail.comXuan Huan Hoanghuanhx@vnu.edu.vnP. Nam NguyenVu Tam NguyenNgoc Thang Dinh2017-12-16T05:42:37Z2019-02-18T03:37:48Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/2790This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/27902017-12-16T05:42:37ZTargeted Misinformation Blocking on Online Social NetworksIn this paper, we investigate a problem of finding smallest set of nodes to remove from a social network so that influence reduction of misinformation sources at least given threshold γ, called Targeted Misinformation Blocking (TMB) problem. We prove that TBM is #P-hard under LT model. For any parameter � ∈ (0,γ), we designed Greedy algorithm which return the solution A with the expected influence reduction greater than γ−�, and the size of A is within factor 1+ln(γ/�) of the optimal size. To speed-up Greedy algorithm, we designed an efficient heuristic algorithm, called STBM algorithm. Experiments were conducted on real world networks which showed the effectiveness of proposed algorithms in term of both effectiveness and efficiency.Canh V. Phammaicanhki@gmail.comQuat V. Phuquatphu97mdbg@gmail.comXuan Huan Hoanghuanhx@vnu.edu.vn2017-12-12T08:10:01Z2017-12-12T08:10:01Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/2763This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/27632017-12-12T08:10:01ZMinimizing the Spread of Misinformation on online Social Networks with Time and Budget constraintIn this article, we propose a linear threshold model to the problem of minimizing the spread of misinformation for cases where partial knowledge of misinformation sources is available by using monitors,the misinformation propagation time and the budget for placing monitors are restricted, and proved it is NP-Hardness. At the same time, we also suggest two greedy algorithms to solve the problem. Experimental results show the dominant advantages of the algorithms in comparison with other commonly used algorithms.Minh Manh Vuvuminhmanh@gmail.comXuan Huan Hoanghuanhx@vnu.edu.vn2017-12-12T07:42:59Z2017-12-12T07:42:59Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/2764This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/27642017-12-12T07:42:59ZLimiting the Spread of Epidemics within Time Constraint on Online Social NetworksIn this paper, we investigate the problem of limiting the spread of epidemics on online social networks (OSNs) with the aim to seek a set nodes of size at most k to remove from the networks such that the number of saved nodes is maximal for cases where we already know the set of infected nodes on the networks. The problem is proved to be NP-hard and it is NP-hard to approximate the problem with ratio nexp(1−ϵ), for 0 < ϵ < 1. Besides, we also suggest two algorithms to solve the problem. Experimental results show that our proposed outperform baseline algorithms.Canh V. Phammaicanhki@gmail.comXuan Huan Hoanghuanhx@vnu.edu.vn2016-12-30T08:26:32Z2016-12-30T08:26:32Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/2373This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/23732016-12-30T08:26:32ZA new viral marketing strategy with the competition in the large-scale Online Social NetworksThe problem of Influence Maximization (IM) on social networks proposed firstly by Kempe et al. (2003) has been researched and developed with many cases. However, the IM in limited time while unwanted users are restricted is still a new potential subject. In this paper, we conducted research the problem on the model of information diffusion name Locally Bounded Diffusion and tested some useful heuristic algorithms. The results of the experiment on some real datasets of social networks show that the algorithm meta-heuristic generated better output than the others.Van Canh PhamKim Dung HaQuang Dung NgoQuang Cao VuXuan Huan Hoang2016-12-30T08:26:04Z2016-12-30T08:26:04Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/2366This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/23662016-12-30T08:26:04ZMột thuật toán hiệu quả dựa trên giải thuật tối ưu đàn
kiến giải bài toán r|p trung tâmDuc Quang VuXuan Huan HoangThanh Mai Do2016-12-29T12:13:36Z2016-12-29T12:13:36Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/2372This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/23722016-12-29T12:13:36ZTime-Critical Viral Marketing Strategy with the Competition on Online Social NetworksVan Canh PhamTra My ThaiKim Dung HaQuang Dung NgoXuan Huan Hoanghuanhx@vnu.edu.vn2016-12-17T16:00:31Z2016-12-17T16:00:31Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/2080This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/20802016-12-17T16:00:31ZAcoSeeD: An ant colony optimization for finding optimal spaced seeds in biological sequence searchDuc Dong DoQuang Huy DinhThanh Hai Danghai.dang@vnu.edu.vnKris LaukensXuan Huan Hoanghuanhx@vnu.edu.vn2016-06-04T08:45:26Z2016-06-04T08:45:26Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/1719This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/17192016-06-04T08:45:26ZMột thuật toán tối ưu đàn kiến dóng hàng toàn cục mạng tương tác proteinNgoc Ha TranXuan Huan Hoanghuanhx@vnu.edu.vn2016-06-04T08:43:23Z2016-06-04T08:43:23Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/1718This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/17182016-06-04T08:43:23ZPreventing and detecting infiltration on Online Social NetworksVan Canh PhamXuan Huan Hoanghuanhx@vnu.edu.vnMinh Manh Vu2016-05-24T09:11:02Z2016-12-17T16:08:02Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/1597This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/15972016-05-24T09:11:02ZAn efficient algorithm for global alignment of protein-protein interaction networksGlobal aligning two protein-protein interaction networks is an essentially important task in
bioinformatics computational biology field of study. It is a challenging and widely studied
research topic in recent years. Accurately aligned networks allow us to identify functional
modules of proteins and/ororthologous proteins from which unknown functions of a protein
can be inferred. We here introduce a novel efficient heuristic global network alignment
algorithm called FASTAn, including two phases: the first to construct an initial alignment
and the second to improve such alignment by exerting a local optimization repeated
procedure. The experimental results demonstrated that FASTAn outperformed the stateof-the-art
global network alignment algorithmnamely SPINAL in terms of both commonly
used objective scoresand the run-time.
Keywords: FASTAn, Heuristic algorithm, Biological network alignment, Protein-protein
interaction networksDuc Dong DoNgoc Ha TranThanh Hai Danghai.dang@vnu.edu.vnCao Cuong DangXuan Huan Hoanghuanhx@vnu.edu.vn2016-05-23T02:54:22Z2016-05-23T02:55:33Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/1553This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/15532016-05-23T02:54:22ZAn Efficient Ant Based Algorithm for Global Alignment of Protein-Protein Interaction NetworksGlobal aligning two protein-protein interaction networks is an essentially important task in bioinformatics. It is a challenging and widely studied research topic in recent years. Accurately aligned networks allow us to identify functional modules of proteins and/ororthologous proteins from which unknown functions of a protein can be inferred. In this article, we introduce an ant based global network alignment algorithm called ACOGA. The experiments show that the method that we proposed get better results than the introduced methods recently.Ngoc Ha TranXuan Huan Hoanghuanhx@vnu.edu.vn