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Scalable Methods for Adaptively Seeding a Social Network [article]

Thibaut Horel, Yaron Singer
2015 arXiv   pre-print
Despite the various complexities in such optimization problems, we show that scalable adaptive seeding is achievable.  ...  To show the effectiveness of our methods we collected data from various verticals social network users follow.  ...  Acknowledgement This research is supported in part by a Google Research Grant and NSF grant CCF-1301976.  ... 
arXiv:1503.01438v2 fatcat:i5h5pmzwd5ed3lxus2hk7xzdfq

Scalable Methods for Adaptively Seeding a Social Network

Thibaut Horel, Yaron Singer
2015 Proceedings of the 24th International Conference on World Wide Web - WWW '15  
Despite the various complexities in such optimization problems, we show that scalable adaptive seeding is achievable.  ...  To show the effectiveness of our methods we collected data from various verticals social network users follow, and applied our methods on it.  ...  This research is supported in part by a Google Research Grant and NSF grant CCF-1301976.  ... 
doi:10.1145/2736277.2741127 dblp:conf/www/HorelS15 fatcat:rt5xtfa2xfbf5bthjwio2zkxdi

Scalable Methods for Adaptively Seeding a Social Network

Thibaut Horel, Yaron Singer
2015 Proceedings of the 24th International Conference on World Wide Web - WWW '15 Companion  
Despite the various complexities in such optimization problems, we show that scalable adaptive seeding is achievable.  ...  To show the effectiveness of our methods we collected data from various verticals social network users follow, and applied our methods on it.  ...  This research is supported in part by a Google Research Grant and NSF grant CCF-1301976.  ... 
doi:10.1145/2740908.2744108 dblp:conf/www/HorelS15a fatcat:dkqqcn37eredllqmgfp2wr4pzq

Scalable Cost-Aware Multi-Way Influence Maximization [article]

Hong-Han Shuai
2015 arXiv   pre-print
According to USA Today, the sales of software to run corporate social networks will grow 61% a year and be a 6.4 billion business by 2016.  ...  To boost the growth of their sales, business is embracing social media in a big way.  ...  However, the enumerative approach is not scalable since there are 2 n combinations for seed selection.  ... 
arXiv:1303.0157v11 fatcat:hsxogeplf5ab7efw64vj4tdjem

IRIE: Scalable and Robust Influence Maximization in Social Networks

Kyomin Jung, Wooram Heo, Wei Chen
2012 2012 IEEE 12th International Conference on Data Mining  
Finding influential users in a social network is essential for viral marketing and social media marketing.  ...  Influence maximization problem is defined as finding a node set S of given size K in a social network to maximize their influence spread -the expected total number of activated nodes under a certain diffusion  ...  Finding influential users in a social network is essential for viral marketing and social media marketing.  ... 
doi:10.1109/icdm.2012.79 dblp:conf/icdm/JungHC12 fatcat:nvcuwwt4pfckvflk75tkj6lcgi

Multi-Round Influence Maximization

Lichao Sun, Weiran Huang, Philip S. Yu, Wei Chen
2018 Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining - KDD '18  
For the non-adaptive setting, we design two algorithms that exhibit an interesting tradeo between eciency and eectiveness: a cross-round greedy algorithm that selects seeds at a global level and achieves  ...  We conduct experiments on several real-world networks and demonstrate that our algorithms are eective for the MRIM task.  ...  The Flixster dataset is a network of American social movie discovery service (www.ixster.com).  ... 
doi:10.1145/3219819.3220101 dblp:conf/kdd/SunHYC18 fatcat:p5wmfzzf4rexlio3mc7urjykga

Scalable Bicriteria Algorithms for the Threshold Activation Problem in Online Social Networks [article]

Alan Kuhnle, Tianyi Pan, Md Abdul Alim, My T. Thai
2017 arXiv   pre-print
We consider the Threshold Activation Problem (TAP): given social network G and positive threshold T, find a minimum-size seed set A that can trigger expected activation of at least T.  ...  We introduce the first scalable, parallelizable algorithm with performance guarantee for TAP suitable for datasets with millions of nodes and edges; we exploit the bicriteria nature of solutions to TAP  ...  [24] , [25] studied methods to restrain propagation in social networks. VI.  ... 
arXiv:1701.08799v1 fatcat:35rqrlffnrcenogzdwyleov2ye

Efficient collective influence maximization in cascading processes with first-order transitions

Sen Pei, Xian Teng, Jeffrey Shaman, Flaviano Morone, Hernán A. Makse
2017 Scientific Reports  
The concept of subcritical path allows us to introduce a linearly scalable algorithm for massively large-scale networks.  ...  Results in both synthetic random graphs and real networks show that the proposed method can achieve larger collective influence given same number of seeds compared with other linearly scalable heuristic  ...  Acknowledgements This work was supported by NIH-NIBIB 1R01EB022720, NIH-NCI U54CA137788/ U54CA132378, NSF-PoLS PHY-1305476, NSF-IIS 1515022, and ARL Cooperative Agreement Number W911NF-09-2-0053, the ARL Network  ... 
doi:10.1038/srep45240 pmid:28349988 pmcid:PMC5368649 fatcat:hq4hjecu7rdhljkv3bgw73kcfq

A Survey on Information Diffusion in Online Social Networks: Models and Methods

2017 Information  
Therefore, online social networks can reflect the structure of offline human society. A piece of information can be exchanged or diffused between individuals in social networks.  ...  Finally, the issues of the social networks research are discussed and summarized, and directions for future study are proposed.  ...  A social network is a dynamic network, therefore the prediction models for a social network must be robust.  ... 
doi:10.3390/info8040118 fatcat:2wfp6uomnjg5bekiouqhacnzpm

Scalable Lattice Influence Maximization [article]

Wei Chen, Ruihan Wu, Zheng Yu
2019 arXiv   pre-print
We adapt the reverse influence sampling (RIS) approach and design scalable algorithms for LIM.  ...  Influence maximization is the task of finding k seed nodes in a social network such that the expected number of activated nodes in the network (under certain influence propagation model), referred to as  ...  When applying the strategy mix x to the social network, each node u in the social network has a probability of h u (x) to be activated as a seed.  ... 
arXiv:1802.04555v2 fatcat:alzointvwbbnpilk4en7skc554

Theories for influencer identification in complex networks [article]

Sen Pei, Flaviano Morone, Hernán A. Makse
2018 arXiv   pre-print
In social and biological systems, the structural heterogeneity of interaction networks gives rise to the emergence of a small set of influential nodes, or influencers, in a series of dynamical processes  ...  a collective point of view.  ...  As a result, the CI algorithm is scalable for massively large-scale networks in modern social network analysis.  ... 
arXiv:1707.01594v2 fatcat:j6je622mxffyxpgznciqdavo74

IRIE: Scalable and Robust Influence Maximization in Social Networks [article]

Kyomin Jung, Wooram Heo, Wei Chen
2012 arXiv   pre-print
Influence maximization is the problem of selecting top k seed nodes in a social network to maximize their influence coverage under certain influence diffusion models.  ...  In this paper, we propose a novel algorithm IRIE that integrates a new message passing based influence ranking (IR), and influence estimation (IE) methods for influence maximization in both the independent  ...  Formally, Influence Maximization problem is defined as follows : Given a directed social network G = (V, E) and P uv for each edge (u, v) ∈ E, influence maximization problem is to select a seed set S ⊆  ... 
arXiv:1111.4795v3 fatcat:muggqywgojhwhnjrjgl5mggna4

OONIS — Object-Oriented Network Infection Simulator

Artur Karczmarczyk, Jarosław Jankowski, Jarosław Wątróbski
2021 SoftwareX  
easy creation of experimental scenarios for studying information propagation in complex networks.  ...  It also supports new approaches, not available in other libraries, related to spreading seeds over the time in a form of sequential seeding, as well as coordinated execution, making it possible to compare  ...  As a result, a scalable, flexible and time-efficient software solution to simulation research on information spreading in complex networks was produced.  ... 
doi:10.1016/j.softx.2021.100675 fatcat:gggxnparazcv5hdnjvesznj6ie

Scalable influence maximization for prevalent viral marketing in large-scale social networks

Wei Chen, Chi Wang, Yajun Wang
2010 Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '10  
The scalability of influence maximization is a key factor for enabling prevalent viral marketing in largescale online social networks.  ...  Influence maximization, defined by Kempe, Kleinberg, and Tardos (2003) , is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under certain influence  ...  It is probably because Amazon is a product co-purchasing network, not a social network.  ... 
doi:10.1145/1835804.1835934 dblp:conf/kdd/ChenWW10 fatcat:pevsbtfmhvhovaiehsjbyxeati

Scalable Interpretable Multi-Response Regression via SEED [article]

Mohammad Taha Bahadori, Zemin Zheng, Yan Liu, Jinchi Lv
2016 arXiv   pre-print
In this paper, we suggest a scalable procedure called sequential estimation with eigen-decomposition (SEED) which needs only a single top-r singular value decomposition to find the optimal low-rank and  ...  Our suggested method is not only scalable but also performs simultaneous dimensionality reduction and variable selection.  ...  2012) because it helps improve social marketing by finding the influential users in a network.  ... 
arXiv:1608.03686v1 fatcat:q5wibugdsrh2dp4l4pxva77gbe
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