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Efficient Topic-aware Influence Maximization Using Preprocessing.
Wei Chen,Tian Lin,Cheng Yang +2 more
- 01 Mar 2014
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TL;DR: In this paper, the authors focus on the topic-aware influence maximization task and study preprocessing methods for these topics to avoid redoing influence maximisation for each item from scratch.
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Abstract: Influence maximization is the task of finding a set of seed nodes in a social network such that the influence spread of these seed nodes based on certain influence diffusion model is maximized. Topic-aware influence diffusion models have been recently proposed to address the issue that influence between a pair of users are often topic-dependent and information, ideas, innovations etc. being propagated in networks (referred collectively as items in this paper) are typically mixtures of topics. In this paper, we focus on the topic-aware influence maximization task. In particular, we study preprocessing methods for these topics to avoid redoing influence maximization for each item from scratch. We explore two preprocessing algorithms with theoretical justifications. Our empirical results on data obtained in a couple of existing studies demonstrate that one of our algorithms stands out as a strong candidate providing microsecond online response time and competitive influence spread, with reasonable preprocessing effort.
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Citations
Online topic-aware influence maximization
Shuo Chen,Ju Fan,Guoliang Li,Jianhua Feng,Kian-Lee Tan,Jinhui Tang +5 more
- 01 Feb 2015
TL;DR: This work proposes a faster topic-sample-based algorithm with e · (1 − 1/e) approximation ratio for any e ∈ (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.
Community-diversified influence maximization in social networks
TL;DR: This work proposes a metric to measure the community-diversified influence and addresses a series of computational challenges that have been verified through extensive experimental studies on five real-world social network datasets.
208
Most Influential Community Search over Large Social Networks
Jianxin Li,Xinjue Wang,Ke Deng,Xiaochun Yang,Timos Sellis,Jeffrey Xu Yu +5 more
- 01 Apr 2017
TL;DR: A new community model is proposed, maximal kr-Clique community, which has desirable properties, i.e., society, cohesiveness, connectivity, and maximum, and a novel tree-based index structure is designed, denoted as C-Tree, to maintain the offline computed r-cliques.
94
Maximizing the spread of influence via the collective intelligence of discrete bat algorithm
Jianxin Tang,Jianxin Tang,Ruisheng Zhang,Yabing Yao,Zhili Zhao,Ping Wang,Huan Li,Jinliang Yuan +7 more
TL;DR: A metaheuristic discrete bat algorithm based on the collective intelligence of bat population is proposed and it is demonstrated that DBA outperforms other two metaheuristics and the Stop-and-Stair algorithm, and achieves competitive influence spread to CELF but has less time computation than CELF.
67
Geo-Social Influence Spanning Maximization
TL;DR: This paper proposes and investigates the problem of influence maximization in location-aware social networks, and proposes the OIR*-Tree index, which is a hybrid index combining ordered influential node lists with an R*-tree.
References
Maximizing the Spread of Influence through a Social Network
TL;DR: The problem of finding the most influential nodes in a social network is NP-hard as mentioned in this paper, and the first provable approximation guarantees for efficient algorithms were provided by Domingos et al. using an analysis framework based on submodular functions.
Mining the network value of customers
Pedro Domingos,Matthew Richardson +1 more
- 26 Aug 2001
TL;DR: It is proposed to model also the customer's network value: the expected profit from sales to other customers she may influence to buy, the customers those may influence, and so on recursively, taking advantage of the availability of large relevant databases.
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Cost-effective outbreak detection in networks
Jure Leskovec,Andreas Krause,Carlos Guestrin,Christos Faloutsos,Jeanne M. VanBriesen,Natalie S. Glance +5 more
- 12 Aug 2007
TL;DR: This work exploits submodularity to develop an efficient algorithm that scales to large problems, achieving near optimal placements, while being 700 times faster than a simple greedy algorithm and achieving speedups and savings in storage of several orders of magnitude.
Mining knowledge-sharing sites for viral marketing
Matthew Richardson,Pedro Domingos +1 more
- 23 Jul 2002
TL;DR: This research optimize the amount of marketing funds spent on each customer, rather than just making a binary decision on whether to market to him, and takes into account the fact that knowledge of the network is partial, and that gathering that knowledge can itself have a cost.
Learning influence probabilities in social networks
Amit Goyal,Francesco Bonchi,Laks V. S. Lakshmanan +2 more
- 04 Feb 2010
TL;DR: This paper proposes models and algorithms for learning the model parameters and for testing the learned models to make predictions, and develops techniques for predicting the time by which a user may be expected to perform an action.
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