Efficient location-aware influence maximization

Li, Guoliang; Chen, Shuo; Feng, Jianhua; Tan, Kian-Lee; Li, Wen-syan

doi:10.1145/2588555.2588561

Cited by 158 publications

(113 citation statements)

References 19 publications

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“…In our work, we adopt a similar strategy introducing a restricted version of the IC model, termed MIAwoT, which is however significantly less restrictive than MIA. Finally, a very recent work [8] solves an IM problem in a similar context to ours, However, this work targets the fundamentally different problem of selecting a group of k users that collectively maximize influence within a region R, whereas k-RIL seeks to rank users. In addition, it makes some unrealistic assumption, e.g., each LASN user has a known fixed location, and the proposed solution relies on extensive pre-computations, which makes it unsuitable for k-RIL.…”

Section: Introductionmentioning

confidence: 99%

Regionally influential users in location-aware social networks

Bouros

Sacharidis

Bikakis³

2014

Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

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The ubiquity of mobile location aware devices and the proliferation of social networks have given rise to Location-Aware Social Networks (LASN), where users form social connections and make geo-referenced posts. The goal of this paper is to identify users that can influence a large number of important other users, within a given spatial region. Returning a ranked list of regionally influential LASN users is useful in viral marketing and in other per-region analytical scenarios. We show that under a general influence propagation model, the problem is #P-hard, while it becomes solvable in polynomial time in a more restricted model. Under the more restrictive model, we then show that the problem can be translated to computing a variant of the so-called closeness centrality of users in the social network, and devise an evaluation method.

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Section: Introductionmentioning

confidence: 99%

Regionally influential users in location-aware social networks

Bouros

Sacharidis

Bikakis³

2014

Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

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“…On the other hand, the marginal influence of u may be correlated with the previously selected seed set S, which is also known as co-influence [19]. In our example in Figure 2(a), given S = {u2}, the marginal influence of u1 on u3 is heavily correlated with the influence of u2.…”

Section: Bound Estimation For Marginal Influencementioning

confidence: 96%

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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“…K-means algorithm. Hence, the name genetic K-means algorithm (GKA).The location-aware influence maximization problem [6]. One big challenge in location-aware influence maximization is to develop an efficient scheme that offers wide influence spread.…”

Section: Related Workmentioning

confidence: 99%

KNN Search on Road Networks by Incorporating Social Influence

2016

IJAERD

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Efficient location-aware influence maximization

Cited by 158 publications

References 19 publications

Regionally influential users in location-aware social networks

Regionally influential users in location-aware social networks

Online topic-aware influence maximization

KNN Search on Road Networks by Incorporating Social Influence

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