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

Scalable influence maximization for prevalent viral marketing in large-scale social networks,10.1145/1835804.1835934,Wei Chen,Chi Wang,Yajun Wang

Scalable influence maximization for prevalent viral marketing in large-scale social networks   (Citations: 12)
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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 large-scale 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 real-world and synthetic networks demonstrate that our algorithm is currently the best scalable solution to the influence maximization problem: (a) our algorithm scales beyond million-sized 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. 1029-1038, 2010
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    • ...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 state-ofthe-art algorithms when we define the spread probability from vi to vj simply as 1 dj (referred as WC model), where dj is the in-degree 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 heuristics-based method to improve the efficiency of influence maximization...

    Chi Wanget al. Dynamic Social Influence Analysis through Time-Dependent 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 #P-hard [1, 3]. As a result, Monte-Carlo 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 Goyalet 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 k-Mediators 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)...

    Cheng-Te Liet 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 large-scale social networks...

    Boying Zhanget al. P3coupon: A probabilistic system for Prompt and Privacy-preserving 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 #N-P hard but also provided a greedy approximation algorithm with a provable approximation guarantee ( 1-1/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 #P-Hard 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 Gui-shenget al. Intelligent Viral Marketing Algorithm over Online Social Network

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