How to choose friends strategically
Alice wants to join a new social network, and influence its members to adopt a new product or idea. Each person v in the network has a certain threshold t(v) for activation, i.e. adoption of the product or idea. If v has at least t(v) activated neighbors, then v will also become activated. If Alice...
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| Published in | Theoretical computer science Vol. 811; pp. 99 - 111 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
02.04.2020
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Alice wants to join a new social network, and influence its members to adopt a new product or idea. Each person v in the network has a certain threshold t(v) for activation, i.e. adoption of the product or idea. If v has at least t(v) activated neighbors, then v will also become activated. If Alice wants to make k new friends in the network, and thereby activate the most number of people, how should she choose these friends? We study the problem of choosing the k people in the network to befriend, who will in turn activate the maximum number of people. This Maximum Influence with Links Problem has applications in viral marketing and the study of epidemics. We show that the solution can be quite different from the related and widely studied influence maximization problem where the objective is to choose a seed or target set with maximum influence. We prove that the Maximum Influence with Links problem is NP-complete even for bipartite graphs in which all nodes have threshold 1 or 2. In contrast, we give polynomial time algorithms that find optimal solutions for the problem for trees, paths, cycles, and cliques. |
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| AbstractList | Alice wants to join a new social network, and influence its members to adopt a new product or idea. Each person v in the network has a certain threshold t(v) for activation, i.e. adoption of the product or idea. If v has at least t(v) activated neighbors, then v will also become activated. If Alice wants to make k new friends in the network, and thereby activate the most number of people, how should she choose these friends? We study the problem of choosing the k people in the network to befriend, who will in turn activate the maximum number of people. This Maximum Influence with Links Problem has applications in viral marketing and the study of epidemics. We show that the solution can be quite different from the related and widely studied influence maximization problem where the objective is to choose a seed or target set with maximum influence. We prove that the Maximum Influence with Links problem is NP-complete even for bipartite graphs in which all nodes have threshold 1 or 2. In contrast, we give polynomial time algorithms that find optimal solutions for the problem for trees, paths, cycles, and cliques. |
| Author | Wu, Kangkang Narayanan, Lata |
| Author_xml | – sequence: 1 givenname: Lata surname: Narayanan fullname: Narayanan, Lata email: lata@cs.concordia.ca – sequence: 2 givenname: Kangkang surname: Wu fullname: Wu, Kangkang |
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| CitedBy_id | crossref_primary_10_1016_j_cor_2021_105441 crossref_primary_10_1007_s10878_024_01164_4 crossref_primary_10_1016_j_knosys_2023_110827 crossref_primary_10_1007_s12652_018_1002_1 |
| Cites_doi | 10.14778/2047485.2047492 10.1137/080714452 10.1109/TNET.2013.2290714 10.1023/A:1011122126881 10.1016/S0012-365X(97)00213-6 10.1007/s13278-012-0062-z 10.1016/j.disopt.2010.09.007 10.1086/209118 10.1007/s13278-012-0067-7 |
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| Keywords | Influence maximization Social networks NP-complete Polytime algorithms for trees, paths, and cliques Dynamic programming |
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| Snippet | Alice wants to join a new social network, and influence its members to adopt a new product or idea. Each person v in the network has a certain threshold t(v)... |
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| SubjectTerms | Dynamic programming Influence maximization NP-complete Polytime algorithms for trees, paths, and cliques Social networks |
| Title | How to choose friends strategically |
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