In order to propagate information through the social network, how to find a seed set that can affect the maximum number of users is named as influence maximization problem. A lot of works have been done on this proble...
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In order to propagate information through the social network, how to find a seed set that can affect the maximum number of users is named as influence maximization problem. A lot of works have been done on this problem, mainly including two aspects: establishing a reasonable information diffusion model and putting forward the appropriate seeding strategy. However, there are few models in the existing ones that consider the acceptance probability of candidate seed nodes in social networks. So in this paper, we consider and solve this problem by introducing a more realistic model, which is the proposed Realistic Independent Cascade (RIC) model. Based on the RIC model, many state-of-the-art seeding algorithms perform not so well because there is no mechanism on dealing with the acceptance probability. So based on the RIC model, we propose a new seeding strategy which is called R-greedy. Furthermore, M-greedy algorithm is proposed to reduce the time complexity of R-greedy. Then, D-greedy algorithm which not only increased the performance but also reduced the time complexity of R-greedy is proposed by combining the advantages of R-greedy and M-greedy. Experiments on the real-world networks and synthetic networks demonstrate that the proposed R-greedy, M-greedy and D-greedy algorithms outperforms state-of-the-art algorithms. (C) 2019 Elsevier B.V. All rights reserved.
The fuzzy C-means problem belongs to soft clustering problem, where each given point has relationship to every center point. This problem is different from the k-means problem, where each point should belong to only o...
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ISBN:
(数字)9783030592677
ISBN:
(纸本)9783030592660;9783030592677
The fuzzy C-means problem belongs to soft clustering problem, where each given point has relationship to every center point. This problem is different from the k-means problem, where each point should belong to only one cluster. In this paper, we design one seeding algorithm for fuzzy C-means problem and obtain performance ratio O(klnk). We also give the performance guarantee O(k(2)lnk) of the seeding algorithm based on k-means++ for fuzzy C-means problem. At last, we present our numerical experiment to show the validity of the algorithms.
Active measurement studies show that the Peer-to-Peer (P2P) file sharing protocol BitTorrent is highly under attack. Moreover, malicious peers can easily exploit the original seeding algorithm and therefore reduce the...
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ISBN:
(纸本)9780769552491
Active measurement studies show that the Peer-to-Peer (P2P) file sharing protocol BitTorrent is highly under attack. Moreover, malicious peers can easily exploit the original seeding algorithm and therefore reduce the efficiency of this protocol. In this paper, we propose a novel seeding algorithm that requests peers to vote for their best sharing peers. Our results show that this incentive mechanism makes BitTorrent harder to exploit without losing performance. In some situations our algorithm even outperform other seeding algorithms. The peer exchange-that comes as a side effect-reduces the dependency on a centralized tracker and increases the robustness and the efficiency. We studied the effectiveness of our approach in a real testbed comprising 32 peers.
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