Efficient algorithms for adaptive influence maximization

Abstract

Given a social network G, the profit maximization (PM) problem asks for a set of seed nodes to maximize the profit, i.e., revenue of influence spread less the cost of seed selection. The target profit maximization (TPM) problem, which generalizes the PM problem, aims to select a subset of seed nodes from a target user set T to maximize the profit. Existing algorithms for PM mostly consider the nonadaptive setting, where all seed nodes are selected in one batch without any knowledge on how they may influence other users. In this paper, we study TPM in adaptive setting, where the seed users are selected through multiple batches, such that the selection of a batch exploits the knowledge of actual influence in the previous batches. To acquire an overall understanding, we study the adaptive TPM problem under both the oracle model and the noise model, and propose ADG and ADDATP algorithms to address them with strong theoretical guarantees, respectively. In addition, to better handle the sampling errors under the noise model, we propose the idea of hybrid error based on which we design a novel algorithm HATP that boosts the efficiency of ADDATP significantly. We conduct extensive experiments on real social networks to evaluate the performance, and the experimental results strongly confirm the superiorities and effectiveness of our solutions.

A survey on influence maximization in a social network

“…In this case, if any one of the selected seeds does not perform up to expectation, then the number of influenced nodes will be lesser than expected. Considering this case, recently the framework of multiphase diffusion has been developed [141], [142]. Different variants of this problem can be studied in this framework.…”

Maximizing Influence Over Streaming Graphs with Query Sequence

“…Recently, there is another type of work called adaptive IM [25][26][27][28][29], which has attracted many researchers' attention. These works assume that the feedback in the real-world is available.…”

Efficient Approximation Algorithms for Adaptive Seed Minimization

“…that we can modify the adaptive influence maximization algorithms to solve the adaptive seed minimization problem, in the same way that existing work [19] transforms non-adaptive influence maximizing algorithms to address non-adaptive seed minimization. This approach, however, does not work because the algorithm in [23] is designed based on vanilla expected marginal spreads. Instead, ASM requires considering truncated expected marginal spreads, as we previously discussed.…”

“…Instead, ASM requires considering truncated expected marginal spreads, as we previously discussed. As a consequence, the algorithm in [23] cannot be adopted in our setting.…”

“…AdaptIM is modified from the AdaptIM-1 method proposed in [23] for the adaptive influence maximization problem. It iteratively runs a non-adaptive influence maximization algorithm (i.e., EPIC [23]) to select the node that maximizes the expected marginal influence spread on the residual graphs, until the desired threshold is reached. AdaptIM differs from our ASTI algorithm in that it greedily selects the node to maximize the influence spread instead of the truncated influence spread.…”