Research output: Contribution to journal › Conference paper › peer-review

**Maximizing Approximately k-Submodular Functions.** / Zheng, Leqian ; Chan, Hau; Loukidis, Grigorios; Li, Minming.

Research output: Contribution to journal › Conference paper › peer-review

Zheng, L, Chan, H, Loukidis, G & Li, M 2020, 'Maximizing Approximately k-Submodular Functions', *SIAM International Conference on Data Mining (SDM) 2021*.

Zheng, L., Chan, H., Loukidis, G., & Li, M. (Accepted/In press). Maximizing Approximately k-Submodular Functions. *SIAM International Conference on Data Mining (SDM) 2021*.

Zheng L, Chan H, Loukidis G, Li M. Maximizing Approximately k-Submodular Functions. SIAM International Conference on Data Mining (SDM) 2021. 2020 Dec 22.

@article{eaf7650442c540e2b3b7cb2382b4a9d6,

title = "Maximizing Approximately k-Submodular Functions",

abstract = "We introduce the problem of maximizing approximately k-submodular functions subject to size constraints. In this problem, one seeks to select k-disjoint subsets of a ground set with bounded total size or individual sizes, and maximum utility, given by a function that is “close” to being k-submodular. The problem finds applications in tasks such as sensor placement, where one wishes to install k types of sensors whose measurements are noisy, and influence maximization, where one seeks to advertise k topics to users of a social network whose level of influence is uncertain. To deal with the problem, we first provide two natural definitions for approximately k-submodular functions and establish a hierarchical relationship between them. Next, we show that simple greedy algorithms offer approximation guarantees for different types of size constraints. Last, we demonstrate experimentally that the greedy algorithms are effective in sensor placement and influence maximization problems.",

author = "Leqian Zheng and Hau Chan and Grigorios Loukidis and Minming Li",

year = "2020",

month = dec,

day = "22",

language = "English",

journal = "SIAM International Conference on Data Mining (SDM) 2021",

}

TY - JOUR

T1 - Maximizing Approximately k-Submodular Functions

AU - Zheng, Leqian

AU - Chan, Hau

AU - Loukidis, Grigorios

AU - Li, Minming

PY - 2020/12/22

Y1 - 2020/12/22

N2 - We introduce the problem of maximizing approximately k-submodular functions subject to size constraints. In this problem, one seeks to select k-disjoint subsets of a ground set with bounded total size or individual sizes, and maximum utility, given by a function that is “close” to being k-submodular. The problem finds applications in tasks such as sensor placement, where one wishes to install k types of sensors whose measurements are noisy, and influence maximization, where one seeks to advertise k topics to users of a social network whose level of influence is uncertain. To deal with the problem, we first provide two natural definitions for approximately k-submodular functions and establish a hierarchical relationship between them. Next, we show that simple greedy algorithms offer approximation guarantees for different types of size constraints. Last, we demonstrate experimentally that the greedy algorithms are effective in sensor placement and influence maximization problems.

AB - We introduce the problem of maximizing approximately k-submodular functions subject to size constraints. In this problem, one seeks to select k-disjoint subsets of a ground set with bounded total size or individual sizes, and maximum utility, given by a function that is “close” to being k-submodular. The problem finds applications in tasks such as sensor placement, where one wishes to install k types of sensors whose measurements are noisy, and influence maximization, where one seeks to advertise k topics to users of a social network whose level of influence is uncertain. To deal with the problem, we first provide two natural definitions for approximately k-submodular functions and establish a hierarchical relationship between them. Next, we show that simple greedy algorithms offer approximation guarantees for different types of size constraints. Last, we demonstrate experimentally that the greedy algorithms are effective in sensor placement and influence maximization problems.

M3 - Conference paper

JO - SIAM International Conference on Data Mining (SDM) 2021

JF - SIAM International Conference on Data Mining (SDM) 2021

ER -

King's College London - Homepage

© 2020 King's College London | Strand | London WC2R 2LS | England | United Kingdom | Tel +44 (0)20 7836 5454