Efficient approximation algorithms for adaptive influence maximization

Huang, Keke; Tang, Jing; Han, Kai; Xiao, Xiaokui; Chen, Wei; Sun, Aixin; Tang, Xueyan; Lim, Andrew

doi:10.1007/s00778-020-00615-8

Cited by 35 publications

(25 citation statements)

References 31 publications

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“…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.…”

Section: Influence Maximization In Static Networkmentioning

confidence: 99%

Maximizing Influence Over Streaming Graphs with Query Sequence

Zhao

Yuan

et al. 2021

Data Sci. Eng.

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Now, with the prevalence of social media, such as Facebook, Weibo, how to maximize influence of individuals, products, actions in new media is of practical significance. Generally, maximizing influence first needs to identify the most influential individuals since they can spread their influence to most of others in the social media. Many studies on influence maximization aimed to select a subset of nodes in static graphs once. Actually, real graphs are evolving. So, influential individuals are also changing. In these scenarios, people tend to select influential individuals multiple times instead of once. Namely, selections are raised sequentially, forming a sequence (query sequence). It raises several new challenges due to changing influential individuals. In this paper, we explore the problem of Influence Maximization over Streaming Graph (SGIM). Then, we design a compact solution for storing and indexing streaming graphs and influential nodes that eliminates the redundant computation. The solution includes Influence-Increment-Index along with two sketch-centralized indices called Influence-Index and Reverse-Influence-Index. Computing influence set of nodes will incur a large number of redundant computations. So, these indices are designed to keep track of the nodes’ influence in sketches. Finally, with the indexing scheme, we present the algorithm to answer SGIM queries. Extensive experiments on several real-world datasets demonstrate that our method is competitive in terms of both efficiency and effectiveness owing to the design of index.

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Section: Influence Maximization In Static Networkmentioning

confidence: 99%

Maximizing Influence Over Streaming Graphs with Query Sequence

Zhao

Yuan

et al. 2021

Data Sci. Eng.

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“…Similar works focusing on improve the performance of influence maximization algorithms can be found also, such as [10][11][12] and so on. Recently, a series of sampling based influence maximization algorithms such as [13][14][15] are proposed and well developed, which have improved the practical performance greatly by involving a tiny loss on the approximation ratio. However, as shown by [16,17], the efficiency problem is still challenging for applying influence maximization algorithms in real applications.…”

Section: Related Workmentioning

confidence: 99%

“…The details of obtaining keys covering some predicate will be introduced later. (b) Using the pointers initialized above, the merge-style method works by counting the appearing times of the current smallest queryID and inserting the query appearing no less than i times to the candidate set (line [8][9][10][11][12][13][14][15][16][17][18][19]. At the end of each iteration, if the candidate is not empty, the iterations will stop.…”

Section: The Searchquerypool Proceduresmentioning

confidence: 99%

“…Then, the predicates of X maintained in the group with k 0 in queryPool will be scanned, and the results of the predicates having intersections with Q will be collected together into qres (line 9-12). Then, the items in qres will be checked one by one to obtain S Q(V) (line [13][14][15][16]. Finally, if |S| |qres| is smaller than a predefined threshold α, the query Q will be inserted into the index resultPool and queryPool (line [17][18], where the details are omitted here since it can be implemented trivially.…”

Section: Index Based Range Query Evaluationmentioning

confidence: 99%

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Efficient Targeted Influence Maximization Based on Multidimensional Selection in Social Networks

Jing

Liu

2021

Front. Phys.

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The influence maximization problem over social networks has become a popular research problem, since it has many important practical applications such as online advertising, virtual market, and so on. General influence maximization problem is defined over the whole network, whose intuitive aim is to find a seed node set with size at most k in order to affect as many as nodes in the network. However, in real applications, it is commonly required that only special nodes (target) in the network are expected to be influenced, which can use the same cost of placing seed nodes but influence more targeted nodes really needed. Some research efforts have provided solutions for the corresponding targeted influence maximization problem (TIM for short). However, there are two main drawbacks of previous works focusing on the TIM problem. First, some works focusing on the case the targets are given arbitrarily make it hard to achieve efficient performance guarantee required by real applications. Second, some previous works studying the TIM problems by specifying the target set in a probabilistic way is not proper for the case that only exact target set is required. In this paper, we study the Multidimensional Selection based Targeted Influence Maximization problem, MSTIM for short. First, the formal definition of the problem is given based on a brief and expressive fragment of general multi-dimensional queries. Then, a formal theoretical analysis about the computational hardness of the MSTIM problem shows that even for a very simple case that the target set specified is 1 larger than the seed node set, the MSTIM problem is still NP-hard. Then, the basic framework of RIS (short for Reverse Influence Sampling) is extended and shown to have a 1 − 1/e − ϵ approximation ratio when a sampling size is satisfied. To satisfy the efficiency requirements, an index-based method for the MSTIM problem is proposed, which utilizes the ideas of reusing previous results, exploits the covering relationship between queries and achieves an efficient solution for MSTIM. Finally, the experimental results on real datasets show that the proposed method is indeed rather efficient.

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“…In particular, Borgs et al [12] proposed Reverse-Reachable Sets that can efficiently estimate influence spread with accuracy guarantee, based on which several studies [34,59,64,66,67] propose more efficient algorithms for IM while still achieving (1 − 1/𝑒 − 𝜖)-approximation. Moreover, several variants of IM have been studied, such as topic-aware [9,16], competition [10,56], adaptive solutions [35,37,63]. However, these studies concentrate on seed selection for submodular optimization with a single cardinality or knapsack constraint.…”

Section: Related Workmentioning

confidence: 99%

Efficient and Effective Algorithms for Revenue Maximization in Social Advertising

Han

Tang

et al. 2021

Preprint

Self Cite

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We consider the revenue maximization problem in social advertising, where a social network platform owner needs to select seed users for a group of advertisers, each with a payment budget, such that the total expected revenue that the owner gains from the advertisers by propagating their ads in the network is maximized. Previous studies on this problem show that it is intractable and present approximation algorithms. We revisit this problem from a fresh perspective and develop novel efficient approximation algorithms, both under the setting where an exact influence oracle is assumed and under one where this assumption is relaxed. Our approximation ratios significantly improve upon the previous ones. Furthermore, we empirically show, using extensive experiments on four datasets, that our algorithms considerably outperform the existing methods on both the solution quality and computation efficiency.

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Efficient approximation algorithms for adaptive influence maximization

Cited by 35 publications

References 31 publications

Maximizing Influence Over Streaming Graphs with Query Sequence

Maximizing Influence Over Streaming Graphs with Query Sequence

Efficient Targeted Influence Maximization Based on Multidimensional Selection in Social Networks

Efficient and Effective Algorithms for Revenue Maximization in Social Advertising

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