IDENTIFYING MAVENS IN SOCIAL NETWORKS

This thesis studies social influence from the perspective of users\u27 characteristics. The importance of users\u27 characteristics in word-of-mouth applications has been emphasized in economics and marketing fields. We model a category of users called mavens where their unique characteristics nominate them to be the preferable seeds in viral marketing applications. In addition, we develop some methods to learn their characteristics based on a real dataset. We also illustrate the ways to maximize information flow through mavens in social networks. Our experiments show that our model can successfully detect mavens as well as fulfill significant roles in maximizing the information flow in a social network where mavens considerably outperform general influential users for influence maximization. The results verify the compatibility of our model with real marketing applications

Blocking Negative Influential Node Set in Social Networks: From Host Perspective

Nowadays, social networks are considered as the very important medium for the spreading of information, innovations, ideas and influences among individuals. Viral marketing is a most prominent marketing strategy using word-of-mouth advertising in social networks. The key problem with the viral marketing is to find the set of influential users or seeds, who, when convinced to adopt an innovation or idea, shall influence other users in the network, leading to large number of adoptions. In our study, we propose and study the competitive viral marketing problem from the host perspective, where the host of the social network sells the viral marketing campaigns to its customers and keeps control of the allocation of seeds. Seeds are allocated in such a way that it creates the bang for the buck for each company. We propose a new diffusion model considering both negative and positive influences. Moreover, we propose a novel problem, named Blocking Negative Influential Node Set (BNINS) selection problem, to identify the positive node set such that the number of negatively activated nodes is minimized for all competitors. Then we proposed a solution to the BNINS problem and conducted simulations to validate the proposed solution. We also compare our work with the related work to check the performance of BNINS-GREEDY under different metrics and we observed that BNINS-GREEDY outperforms the others\u27 algorithm. For Random Graph, on average, BNINS-GREEDY blocks the negative influence 17.22% more than CLDAG. At the same time, it achieves 7.6% more positive influence propagation than CLDAG

Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback

We study the online influence maximization problem in social networks under the independent cascade model. Specifically, we aim to learn the set of "best influencers" in a social network online while repeatedly interacting with it. We address the challenges of (i) combinatorial action space, since the number of feasible influencer sets grows exponentially with the maximum number of influencers, and (ii) limited feedback, since only the influenced portion of the network is observed. Under a stochastic semi-bandit feedback, we propose and analyze IMLinUCB, a computationally efficient UCB-based algorithm. Our bounds on the cumulative regret are polynomial in all quantities of interest, achieve near-optimal dependence on the number of interactions and reflect the topology of the network and the activation probabilities of its edges, thereby giving insights on the problem complexity. To the best of our knowledge, these are the first such results. Our experiments show that in several representative graph topologies, the regret of IMLinUCB scales as suggested by our upper bounds. IMLinUCB permits linear generalization and thus is both statistically and computationally suitable for large-scale problems. Our experiments also show that IMLinUCB with linear generalization can lead to low regret in real-world online influence maximization.Comment: Compared with the previous version, this version has fixed a mistake. This version is also consistent with the NIPS camera-ready versio

Influence Maximization: Near-Optimal Time Complexity Meets Practical Efficiency

Given a social network G and a constant k, the influence maximization problem asks for k nodes in G that (directly and indirectly) influence the largest number of nodes under a pre-defined diffusion model. This problem finds important applications in viral marketing, and has been extensively studied in the literature. Existing algorithms for influence maximization, however, either trade approximation guarantees for practical efficiency, or vice versa. In particular, among the algorithms that achieve constant factor approximations under the prominent independent cascade (IC) model or linear threshold (LT) model, none can handle a million-node graph without incurring prohibitive overheads. This paper presents TIM, an algorithm that aims to bridge the theory and practice in influence maximization. On the theory side, we show that TIM runs in O((k+\ell) (n+m) \log n / \epsilon^2) expected time and returns a (1-1/e-\epsilon)-approximate solution with at least 1 - n^{-\ell} probability. The time complexity of TIM is near-optimal under the IC model, as it is only a \log n factor larger than the \Omega(m + n) lower-bound established in previous work (for fixed k, \ell, and \epsilon). Moreover, TIM supports the triggering model, which is a general diffusion model that includes both IC and LT as special cases. On the practice side, TIM incorporates novel heuristics that significantly improve its empirical efficiency without compromising its asymptotic performance. We experimentally evaluate TIM with the largest datasets ever tested in the literature, and show that it outperforms the state-of-the-art solutions (with approximation guarantees) by up to four orders of magnitude in terms of running time. In particular, when k = 50, \epsilon = 0.2, and \ell = 1, TIM requires less than one hour on a commodity machine to process a network with 41.6 million nodes and 1.4 billion edges.Comment: Revised Sections 1, 2.3, and 5 to remove incorrect claims about reference [3]. Updated experiments accordingly. A shorter version of the paper will appear in SIGMOD 201