IRIE: Scalable and Robust Influence Maximization in Social Networks

Jung, Kyomin; Heo, Wooram; Chen, Wei

doi:10.1109/icdm.2012.79

Cited by 355 publications

(248 citation statements)

References 18 publications

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“…We set the IRIE parameters α and θ to 0.7 and 1/320, respectively, and SIMPATH's parameters η and l to 10 −3 and 4, respectively. All the parameters have been set according to the recommendations by the authors of [29,31,46].…”

Section: Methodsmentioning

confidence: 99%

“…In addition to the above mentioned techniques, literature has witnessed many heuristic algorithms. Among them, IRIE and SIM-PATH [29,31] are considered state-of-the-art heuristics for the IC and LT models respectively. Although, these techniques build upon a similar idea as that of EaSyIM, its algorithm design and analysis is very different from them with an additional advantage of running in linear space and time.…”

Section: Related Workmentioning

confidence: 99%

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Holistic Influence Maximization

Galhotra¹,

Arora²,

Roy³

2016

Proceedings of the 2016 International Conference on Management of Data

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The steady growth of graph data from social networks has resulted in wide-spread research in finding solutions to the influence maximization problem. In this paper, we propose a holistic solution to the influence maximization (IM) problem. (1) We introduce an opinion-cum-interaction (OI) model that closely mirrors the real-world scenarios. Under the OI model, we introduce a novel problem of Maximizing the Effective Opinion (MEO) of influenced users. We prove that the MEO problem is NP-hard and cannot be approximated within a constant ratio unless P=NP. (2) We propose a heuristic algorithm OSIM to efficiently solve the MEO problem. To better explain the OSIM heuristic, we first introduce EaSyIMthe opinion-oblivious version of OSIM, a scalable algorithm capable of running within practical compute times on commodity hardware. In addition to serving as a fundamental building block for OSIM, EaSyIM is capable of addressing the scalability aspectmemory consumption and running time, of the IM problem as well.Empirically, our algorithms are capable of maintaining the deviation in the spread always within 5% of the best known methods in the literature. In addition, our experiments show that both OSIM and EaSyIM are effective, efficient, scalable and significantly enhance the ability to analyze real datasets. Categories and Subject Descriptors MOTIVATIONGrowth and pervasiveness of online social networks is no longer a new phenomenon. They have become an integral part of the dayto-day life of almost every Internet user. Their wide-spread reach * The first two authors have contributed equally to this work.Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. with its applicability in solving the above mentioned problems and beyond, has thus, been one of the most widely studied problems over the past decade. This problem is to identify a set of seed nodes so that the overall spread of information in a network, which is the potential collective impact of imparting that piece of information to these nodes, is maximized. Given that the objective of IM is to maximize the spread of information about a content, which can be a product, person, event and many more, an important aspect of this problem is the underlying information diffusion model. A diffusion model defines the dynamics of information propagation and also controls the way this information is perceived by the nodes in a network. Nevertheless, a substantially large fraction of the literature in this field has focussed on devising efficient and scalable algorithms [13, 17-19, 21-23, 28, 29, 31-33, 35, 42, 46] for IM using the classical information diffusion models [32,44]. The two fundamental diffusion models p...

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Holistic Influence Maximization

Galhotra¹,

Arora²,

Roy³

2016

Proceedings of the 2016 International Conference on Management of Data

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“…Algorithms. We compared with the state-of-the-art algorithms PMIA [2] and IRIE [9]. We obtained the source codes from the authors and extended them to support our problem as discussed in Section 2.2.…”

Section: Nvmentioning

confidence: 99%

“…The top three vertices in the priority queue are vertices 16, 10 and 0. We compute their incremental lower bounds, 16, 1.0 , 10, 1.0 , 0, 0.0 using Equation 9. Thus the lower bound is B l = 11.01 computed by seeds 14, 4.667 , 3, 4.344 and three lower bounds.…”

Section: Estimate Upper Bound Of P(s Vr)mentioning

confidence: 99%

Efficient location-aware influence maximization

Chen

Feng

et al. 2014

Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data

157

112

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Although influence maximization, which selects a set of users in a social network to maximize the expected number of users influenced by the selected users (called influence spread), has been extensively studied, existing works neglected the fact that the location information can play an important role in influence maximization. Many real-world applications such as location-aware word-of-mouth marketing have location-aware requirement. In this paper we study the location-aware influence maximization problem. One big challenge in location-aware influence maximization is to develop an efficient scheme that offers wide influence spread. To address this challenge, we propose two greedy algorithms with 1 − 1/e approximation ratio. To meet the instantspeed requirement, we propose two efficient algorithms with ǫ · (1 − 1/e) approximation ratio for any ǫ ∈ (0, 1]. Experimental results on real datasets show our method achieves high performance while keeping large influence spread and significantly outperforms state-of-the-art algorithms.

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“…Kim et al [16] proposed a parallelized approach with OpenMP meta-programming expressions. Jung et al [14] approximated the real influence with linear equations. Borgs et al [3] provided a fast algorithm to maximize social influence in nearly optimal time.…”

Section: Related Workmentioning

confidence: 99%

Online topic-aware influence maximization

et al. 2015

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Influence maximization, whose objective is to select k users (called seeds) from a social network such that the number of users influenced by the seeds (called influence spread) is maximized, has attracted significant attention due to its widespread applications, such as viral marketing and rumor control. However, in real-world social networks, users have their own interests (which can be represented as topics) and are more likely to be influenced by their friends (or friends' friends) with similar topics. We can increase the influence spread by taking into consideration topics. To address this problem, we study topic-aware influence maximization, which, given a topic-aware influence maximization (TIM) query, finds k seeds from a social network such that the topic-aware influence spread of the k seeds is maximized. Our goal is to enable online TIM queries. Since the topicaware influence maximization problem is NP-hard, we focus on devising efficient algorithms to achieve instant performance while keeping a high influence spread. We utilize a maximum influence arborescence (MIA) model to approximate the computation of influence spread. To efficiently find k seeds under the MIA model, we first propose a besteffort algorithm with 1 − 1 e approximation ratio, which estimates an upper bound of the topic-aware influence of each user and utilizes the bound to prune large numbers of users with small influence. We devise effective techniques to estimate tighter upper bounds. We then propose a faster topicsample-based algorithm with · (1 − 1 e ) approximation ratio for any ∈ (0, 1], which materializes the influence spread of some topic-distribution samples and utilizes the materialized information to avoid computing the actual influence of users with small influences. Experimental results show that our methods significantly outperform baseline approaches.

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IRIE: Scalable and Robust Influence Maximization in Social Networks

Cited by 355 publications

References 18 publications

Holistic Influence Maximization

Holistic Influence Maximization

Efficient location-aware influence maximization

Online topic-aware influence maximization

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