A general method of active friending in different diffusion models in social networks
Active friending is a problem in social networks that is to assist a user to build a relationship to a target user by sending invitations to a set of intermediate users; the goal is to maximize the acceptance probability at the target node taking advantage of the social influence through the network...
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| Published in | Social network analysis and mining Vol. 10; no. 1; p. 41 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Vienna
Springer Vienna
01.12.2020
Springer Nature B.V |
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| Abstract | Active friending is a problem in social networks that is to assist a user to build a relationship to a target user by sending invitations to a set of intermediate users; the goal is to maximize the acceptance probability at the target node taking advantage of the social influence through the network formed by the intermediate nodes. In this paper, we convert the original formulated active friending problem of nonsubmodular maximization subject to cardinality constraint into a submodular cost submodular knapsack problem in the IC model, and we show that the two problems are equivalent. We similarly make the conversion on the active friending in the LT model. Then we give a general combinatorial optimization algorithm to solve active friending problems in both the IC model and the LT model with a guaranteed approximation. We analyze the computational complexity of the problem and the algorithm performance. The effectiveness of the generalized method is verified on real data sets. |
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| AbstractList | Active friending is a problem in social networks that is to assist a user to build a relationship to a target user by sending invitations to a set of intermediate users; the goal is to maximize the acceptance probability at the target node taking advantage of the social influence through the network formed by the intermediate nodes. In this paper, we convert the original formulated active friending problem of nonsubmodular maximization subject to cardinality constraint into a submodular cost submodular knapsack problem in the IC model, and we show that the two problems are equivalent. We similarly make the conversion on the active friending in the LT model. Then we give a general combinatorial optimization algorithm to solve active friending problems in both the IC model and the LT model with a guaranteed approximation. We analyze the computational complexity of the problem and the algorithm performance. The effectiveness of the generalized method is verified on real data sets. |
| ArticleNumber | 41 |
| Author | Gao, Chuangen Gu, Shuyang Wang, Hua Yang, Ruiqi Wu, Weili Xu, Dachuan |
| Author_xml | – sequence: 1 givenname: Shuyang orcidid: 0000-0003-4535-2280 surname: Gu fullname: Gu, Shuyang email: gushuy@gmail.com organization: Department of Computer Information Systems, Texas A&M University-Central Texas – sequence: 2 givenname: Chuangen surname: Gao fullname: Gao, Chuangen organization: The School of Computer Science and Technology, Shandong University – sequence: 3 givenname: Ruiqi surname: Yang fullname: Yang, Ruiqi organization: Department of Information and Operations Research, Beijing University of Technology – sequence: 4 givenname: Weili surname: Wu fullname: Wu, Weili organization: Department of Computer Science, The University of Texas at Dallas – sequence: 5 givenname: Hua surname: Wang fullname: Wang, Hua organization: The School of Computer Science and Technology, Shandong University – sequence: 6 givenname: Dachuan surname: Xu fullname: Xu, Dachuan organization: Department of Information and Operations Research, Beijing University of Technology |
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| Keywords | Submodular cost submodular knapsack maximization Friending Social networks |
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| References | Domingos P, Richardson M (2001) Mining the network value of customers. In: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 57–66 Vondrák J (2010) Submodularity and curvature: the optimal algorithm (combinatorial optimization and discrete algorithms) LuZZhangZWuWSolution of bharathi-kempe-salek conjecture for influence maximization on arborescenceJ Comb Optim2017332803808360410210.1007/s10878-016-0006-z Chen W, Yuan Y, Zhang L (2010) Scalable influence maximization in social networks under the linear threshold model. In: 2010 IEEE international conference on data mining. IEEE, pp 8–97 Goyal A, Lu W, Lakshmanan LV (2011) Celf++: optimizing the greedy algorithm for influence maximization in social networks. In: Proceedings of the 20th international conference companion on world wide web. ACM, pp 47–48 KwonJKimSFriend recommendation method using physical and social contextInt J Comput Sci Netw Secur20101011116120 Wu W, Du D-Z, et al. (2018) An approximation algorithm for active friending in online social networks. arXiv preprint arXiv:1811.00643 ConfortiMCornuéjolsGSubmodular set functions, matroids and the greedy algorithm: tight worst-case bounds and some generalizations of the rado-edmonds theoremDiscrete Appl Math19847325127473689010.1016/0166-218X(84)90003-9 SviridenkoMA note on maximizing a submodular set function subject to a knapsack constraintOper Res Lett20043214143201710710.1016/S0167-6377(03)00062-2 Iyer RK, Jegelka S, Bilmes JA (2013) Curvature and optimal algorithms for learning and minimizing submodular functions. In: Advances in neural information processing systems, pp 2742–2750 Silva NB, Tsang R, Cavalcanti GD, Tsang J (2010) A graph-based friend recommendation system using genetic algorithm. In: IEEE congress on evolutionary computation. IEEE, pp 1–7 Yuan J, Wu W, Li Y, Du D (2017) Active friending in online social networks. In: Proceedings of the Fourth IEEE/ACM international conference on big data computing, applications and technologies. ACM, pp 139–148 Chen W, Wang Y, Yang S (2009) Efficient influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 199–208 Iyer RK, Bilmes JA (2013) Submodular optimization with submodular cover and submodular knapsack constraints. In: Advances in neural information processing systems, pp 2436–2444 Chen H, Xu W, Zhai X, Bi Y, Wang A, Du D-Z (2014) How could a boy influence a girl?. In: 2014 10th International conference on mobile ad-hoc and sensor networks. IEEE, pp 279–287 WangZLiaoJCaoQQiHWangZFriendbook: a semantic-based friend recommendation system for social networksIEEE Trans Mob Comput201514353855110.1109/TMC.2014.2322373 Richardson M, Domingos P (2002) Mining knowledge-sharing sites for viral marketing. In: Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 61–70 Tang Y, Xiao X, Shi Y (2014) Influence maximization: near-optimal time complexity meets practical efficiency. In: Proceedings of the 2014 ACM SIGMOD international conference on management of data. ACM, pp 75–86 Bharathi S, Kempe D, Salek M (2007) Competitive influence maximization in social networks. In: International workshop on web and internet economics. Springer, pp 306–311 WangAWuWCuiLOn bharathi-kempe-salek conjecture for influence maximization on arborescenceJ Comb Optim201631416781684348059210.1007/s10878-016-9991-1 Goyal A, Lu W, Lakshmanan LV (2011) Simpath: an efficient algorithm for influence maximization under the linear threshold model. In 2011 IEEE 11th international conference on data mining. IEEE, pp 211–220 Kempe D, Kleinberg J, Tardos É (2003) Maximizing the spread of influence through a social network. In: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 137–146 Yang D-N, Hung H-J, Lee W-C, Chen W (2013) Maximizing acceptance probability for active friending in online social networks. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 713–721 Xie X (2010) Potential friend recommendation in online social network. In: Proceedings of the 2010 IEEE/ACM int’l conference on green computing and communications & int’l conference on cyber, physical and social computing. IEEE Computer Society, pp 831–835 Jegelka S, Bilmes J (2011) Submodularity beyond submodular energies: coupling edges in graph cuts. In: CVPR 2011. IEEE, pp 1897–1904 653_CR6 653_CR7 653_CR8 653_CR9 653_CR2 653_CR3 653_CR4 M Sviridenko (653_CR17) 2004; 32 653_CR1 653_CR12 653_CR11 653_CR10 653_CR16 653_CR15 M Conforti (653_CR5) 1984; 7 Z Lu (653_CR14) 2017; 33 653_CR19 653_CR18 653_CR23 653_CR22 Z Wang (653_CR20) 2015; 14 A Wang (653_CR21) 2016; 31 653_CR25 653_CR24 J Kwon (653_CR13) 2010; 10 |
| References_xml | – reference: Goyal A, Lu W, Lakshmanan LV (2011) Celf++: optimizing the greedy algorithm for influence maximization in social networks. In: Proceedings of the 20th international conference companion on world wide web. ACM, pp 47–48 – reference: Kempe D, Kleinberg J, Tardos É (2003) Maximizing the spread of influence through a social network. In: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 137–146 – reference: Richardson M, Domingos P (2002) Mining knowledge-sharing sites for viral marketing. In: Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 61–70 – reference: Bharathi S, Kempe D, Salek M (2007) Competitive influence maximization in social networks. In: International workshop on web and internet economics. Springer, pp 306–311 – reference: Wu W, Du D-Z, et al. (2018) An approximation algorithm for active friending in online social networks. arXiv preprint arXiv:1811.00643 – reference: KwonJKimSFriend recommendation method using physical and social contextInt J Comput Sci Netw Secur20101011116120 – reference: Jegelka S, Bilmes J (2011) Submodularity beyond submodular energies: coupling edges in graph cuts. In: CVPR 2011. IEEE, pp 1897–1904 – reference: LuZZhangZWuWSolution of bharathi-kempe-salek conjecture for influence maximization on arborescenceJ Comb Optim2017332803808360410210.1007/s10878-016-0006-z – reference: Yuan J, Wu W, Li Y, Du D (2017) Active friending in online social networks. In: Proceedings of the Fourth IEEE/ACM international conference on big data computing, applications and technologies. ACM, pp 139–148 – reference: Chen H, Xu W, Zhai X, Bi Y, Wang A, Du D-Z (2014) How could a boy influence a girl?. In: 2014 10th International conference on mobile ad-hoc and sensor networks. IEEE, pp 279–287 – reference: Silva NB, Tsang R, Cavalcanti GD, Tsang J (2010) A graph-based friend recommendation system using genetic algorithm. In: IEEE congress on evolutionary computation. IEEE, pp 1–7 – reference: Yang D-N, Hung H-J, Lee W-C, Chen W (2013) Maximizing acceptance probability for active friending in online social networks. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 713–721 – reference: Vondrák J (2010) Submodularity and curvature: the optimal algorithm (combinatorial optimization and discrete algorithms) – reference: Xie X (2010) Potential friend recommendation in online social network. In: Proceedings of the 2010 IEEE/ACM int’l conference on green computing and communications & int’l conference on cyber, physical and social computing. IEEE Computer Society, pp 831–835 – reference: Goyal A, Lu W, Lakshmanan LV (2011) Simpath: an efficient algorithm for influence maximization under the linear threshold model. In 2011 IEEE 11th international conference on data mining. IEEE, pp 211–220 – reference: WangAWuWCuiLOn bharathi-kempe-salek conjecture for influence maximization on arborescenceJ Comb Optim201631416781684348059210.1007/s10878-016-9991-1 – reference: WangZLiaoJCaoQQiHWangZFriendbook: a semantic-based friend recommendation system for social networksIEEE Trans Mob Comput201514353855110.1109/TMC.2014.2322373 – reference: Chen W, Wang Y, Yang S (2009) Efficient influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 199–208 – reference: ConfortiMCornuéjolsGSubmodular set functions, matroids and the greedy algorithm: tight worst-case bounds and some generalizations of the rado-edmonds theoremDiscrete Appl Math19847325127473689010.1016/0166-218X(84)90003-9 – reference: Tang Y, Xiao X, Shi Y (2014) Influence maximization: near-optimal time complexity meets practical efficiency. In: Proceedings of the 2014 ACM SIGMOD international conference on management of data. ACM, pp 75–86 – reference: Domingos P, Richardson M (2001) Mining the network value of customers. In: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 57–66 – reference: Chen W, Yuan Y, Zhang L (2010) Scalable influence maximization in social networks under the linear threshold model. In: 2010 IEEE international conference on data mining. IEEE, pp 8–97 – reference: Iyer RK, Bilmes JA (2013) Submodular optimization with submodular cover and submodular knapsack constraints. In: Advances in neural information processing systems, pp 2436–2444 – reference: SviridenkoMA note on maximizing a submodular set function subject to a knapsack constraintOper Res Lett20043214143201710710.1016/S0167-6377(03)00062-2 – reference: Iyer RK, Jegelka S, Bilmes JA (2013) Curvature and optimal algorithms for learning and minimizing submodular functions. In: Advances in neural information processing systems, pp 2742–2750 – ident: 653_CR15 doi: 10.1145/775047.775057 – volume: 31 start-page: 1678 issue: 4 year: 2016 ident: 653_CR21 publication-title: J Comb Optim doi: 10.1007/s10878-016-9991-1 – ident: 653_CR22 – ident: 653_CR1 doi: 10.1007/978-3-540-77105-0_31 – ident: 653_CR6 doi: 10.1145/502512.502525 – ident: 653_CR4 doi: 10.1109/ICDM.2010.118 – volume: 7 start-page: 251 issue: 3 year: 1984 ident: 653_CR5 publication-title: Discrete Appl Math doi: 10.1016/0166-218X(84)90003-9 – ident: 653_CR10 – ident: 653_CR23 doi: 10.1109/GreenCom-CPSCom.2010.28 – ident: 653_CR24 doi: 10.1145/2487575.2487599 – ident: 653_CR9 – ident: 653_CR19 – ident: 653_CR25 doi: 10.1145/3148055.3148073 – volume: 14 start-page: 538 issue: 3 year: 2015 ident: 653_CR20 publication-title: IEEE Trans Mob Comput doi: 10.1109/TMC.2014.2322373 – volume: 10 start-page: 116 issue: 11 year: 2010 ident: 653_CR13 publication-title: Int J Comput Sci Netw Secur – ident: 653_CR11 doi: 10.1109/CVPR.2011.5995589 – ident: 653_CR16 doi: 10.1109/CEC.2010.5586144 – volume: 33 start-page: 803 issue: 2 year: 2017 ident: 653_CR14 publication-title: J Comb Optim doi: 10.1007/s10878-016-0006-z – ident: 653_CR2 doi: 10.1145/1557019.1557047 – ident: 653_CR7 doi: 10.1145/1963192.1963217 – ident: 653_CR8 doi: 10.1109/ICDM.2011.132 – ident: 653_CR12 doi: 10.1145/956750.956769 – ident: 653_CR18 doi: 10.1145/2588555.2593670 – ident: 653_CR3 doi: 10.1109/MSN.2014.45 – volume: 32 start-page: 41 issue: 1 year: 2004 ident: 653_CR17 publication-title: Oper Res Lett doi: 10.1016/S0167-6377(03)00062-2 |
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| SubjectTerms | Algorithms Applications of Graph Theory and Complex Networks Cardinality Combinatorial analysis Computer Science Data Mining and Knowledge Discovery Diffusion models Economics Game Theory Graphs Humanities Influence Knapsack problem Law Methodology of the Social Sciences Optimization Original Article Propagation Social and Behav. Sciences Social networks Statistics for Social Sciences |
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| Title | A general method of active friending in different diffusion models in social networks |
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