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Greedy Algorithm
Heuristic Algorithm
Large Scale
Online Social Network
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Viral Marketing
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Scalable influence maximization for prevalent viral marketing in largescale social networks
Scalable influence maximization for prevalent viral marketing in largescale social networks,10.1145/1835804.1835934,Wei Chen,Chi Wang,Yajun Wang
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Scalable influence maximization for prevalent viral marketing in largescale social networks
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Citations: 12
)
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Wei Chen
,
Chi Wang
,
Yajun Wang
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 cascade models. The scalability of influence maximization is a key factor for enabling prevalent
viral marketing
in largescale online social networks. Prior solutions, such as the
greedy algorithm
of Kempe et al. (2003) and its improvements are slow and not scalable, while other heuristic algorithms do not provide consistently good performance on influence spreads. In this paper, we design a new
heuristic algorithm
that is easily scalable to millions of nodes and edges in our experiments. Our algorithm has a simple tunable parameter for users to control the balance between the running time and the influence spread of the algorithm. Our results from extensive simulations on several realworld and synthetic networks demonstrate that our algorithm is currently the best scalable solution to the influence maximization problem: (a) our algorithm scales beyond millionsized graphs where the
greedy algorithm
becomes infeasible, and (b) in all size ranges, our algorithm performs consistently well in influence spread  it is always among the best algorithms, and in most cases it significantly outperforms all other scalable heuristics to as much as 100%260% increase in influence spread.
Conference:
Knowledge Discovery and Data Mining  KDD
, pp. 10291038, 2010
DOI:
10.1145/1835804.1835934
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Citation Context
(12)
...Based on the computed social influences, one can use algorithms developped by previous researchers([10,
4
]) to find the subset of users (e.g., George and Frank) who could maximize the spread of influence...
...Chen et al. have developped new heuristics [5,
4
] to accelerate the greedy algorithm...
...Figure 4a shows the solution found by several stateoftheart algorithms when we define the spread probability from vi to vj simply as 1 dj (referred as WC model), where dj is the indegree of vj. Beyond Greedy algorithm, we also test SP1M [11], using a simplified ICM model and MIA [
4
], a heuristic algorithm for general ICM...
...Chen et al. [5,
4
] further proposed a heuristicsbased method to improve the efficiency of influence maximization...
Chi Wang
,
et al.
Dynamic Social Influence Analysis through TimeDependent Factor Graphs
...The first source is tackled by estimating the spread using Monte Carlo simulation or by using heuristics [4, 6, 2, 5,
1
, 3]. Leskovec et al. [6] proposed the CELF algorithm for tackling the second...
...The problem of computing the spread under both IC and LT models is #Phard [
1
, 3]. As a result, MonteCarlo simulations are run by KKT...
...Considerable work has been done on tackling the first issue, by using efficient heuristics for estimating the spread [2, 5,
1
, 3] to register huge gains on this front...
...We consider the IC model and assign the influence probability to arcs using two different settings, following previous works (e.g., see [4, 2,
1
])...
Amit Goyal
,
et al.
CELF++: optimizing the greedy algorithm for influence maximization in ...
...Some greedy [6][10] and heuristic [
1
][2] methods are proposed to effectively and efficiently solve this problem...
...Problem Definition. (The kMediators Problem) Given (1) a social network G=(V, E, P), where V stands for individuals and each undirected edge (u,v) E is associated an influence probability p(u, v) [0,
1
] as weights, (2) a set of source nodes S, (3) a set of target nodes T, and (4) a budget (integer) k, find a set of k nodes (mediators) M with the highest mediation probability mp(S, T,M)...
ChengTe Li
,
et al.
Finding influential mediators in social networks
...Among these strategies, the influence maximization problem [9]–[11], [
14
] attracts much research interest...
...To address this issue, [
14
] proposes a scalable influence maximization scheme for viral marketing in largescale social networks...
Boying Zhang
,
et al.
P3coupon: A probabilistic system for Prompt and Privacypreserving ele...
...The problem of viral marketing was originally introduced to the field of computer science by Domingos and Richardson [
1
] and formalized by Kempe, Kleinberg, Tardos [3] who not only proved that the optimization problem is #NP hard but also provided a greedy approximation algorithm with a provable approximation guarantee ( 11/e� of the optimal solution) based on submodular property...
...Similarly out of this consideration, Wei Chen et al [
7
] proved that the problem of computing influence spread given a seed set is #PHard and presented a new heuristic scheme using a local arborescence structure which showed to be the most efficient and scalable algorithm in their experiment...
...In [
1
] Weiwei Yuan et al have proved that the datasets presented above were all small world networks...
Yin Guisheng
,
et al.
Intelligent Viral Marketing Algorithm over Online Social Network
References
(16)
A random graph model for massive graphs
(
Citations: 439
)
William Aiello
,
Fan R. K. Chung
,
Linyuan Lu
Conference:
ACM Symposium on Theory of Computing  STOC
, pp. 171180, 2000
The anatomy of a largescale hypertextual Web search engine
(
Citations: 4676
)
Sergey Brin
,
Lawrence Page
Journal:
Computer Networks and Isdn Systems  CN
, vol. 30, no. 17, pp. 107117, 1998
Efficient influence maximization in social networks
(
Citations: 35
)
Wei Chen
,
Yajun Wang
,
Siyu Yang
Conference:
Knowledge Discovery and Data Mining  KDD
, pp. 199208, 2009
Mining the network value of customers
(
Citations: 340
)
Pedro Domingos
,
Matthew Richardson
Conference:
Knowledge Discovery and Data Mining  KDD
, pp. 5766, 2001
A threshold of ln n for approximating set cover
(
Citations: 931
)
Uriel Feige
Journal:
Journal of The ACM  JACM
, vol. 45, no. 4, pp. 634652, 1998
Sort by:
Citations
(12)
Dynamic Social Influence Analysis through TimeDependent Factor Graphs
Chi Wang
,
Jie Tang
,
Jimeng Sun
,
Jiawei Han
Conference:
Advances in Social Network Analysis and Mining  ASONAM
, 2011
CELF++: optimizing the greedy algorithm for influence maximization in social networks
Amit Goyal
,
Wei Lu
,
Laks V. S. Lakshmanan
Conference:
World Wide Web Conference Series  WWW
, pp. 4748, 2011
Finding influential mediators in social networks
ChengTe Li
,
ShouDe Lin
,
ManKwan Shan
Conference:
World Wide Web Conference Series  WWW
, pp. 7576, 2011
P3coupon: A probabilistic system for Prompt and Privacypreserving electronic coupon distribution
Boying Zhang
,
Jin Teng
,
Xiaole Bai
,
Zhimin Yang
,
Dong Xuan
Conference:
IEEE International Conference on Pervasive Computing and Communications
, 2011
Intelligent Viral Marketing Algorithm over Online Social Network
Yin Guisheng
,
Wei Jijie
,
Dong Hongbin
,
Li Jia
Conference:
International Conference on Networking and Distributed Computing  ICNDC
, 2011