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24
Scalable influence estimation in continuoustime diffusion networks
 In
, 2013
"... If a piece of information is released from a media site, can we predict whether it may spread to one million web pages, in a month? This influence estimation problem is very challenging since both the timesensitive nature of the task and the requirement of scalability need to be addressed simultane ..."
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Cited by 23 (6 self)
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If a piece of information is released from a media site, can we predict whether it may spread to one million web pages, in a month? This influence estimation problem is very challenging since both the timesensitive nature of the task and the requirement of scalability need to be addressed simultaneously. In this paper, we propose a randomized algorithm for influence estimation in continuoustime diffusion networks. Our algorithm can estimate the influence of every node in a network with V  nodes and E  edges to an accuracy of using n = O(1/2) randomizations and up to logarithmic factorsO(nE+nV) computations. When used as a subroutine in a greedy influence maximization approach, our proposed algorithm is guaranteed to find a set of C nodes with the influence of at least (1 − 1/e) OPT−2C, where OPT is the optimal value. Experiments on both synthetic and realworld data show that the proposed algorithm can easily scale up to networks of millions of nodes while significantly improves over previous stateofthearts in terms of the accuracy of the estimated influence and the quality of the selected nodes in maximizing the influence. 1
Structure and Dynamics of Information Pathways in Online Media
"... Diffusion of information, spread of rumors and infectious diseases are all instances of stochastic processes that occur over the edges of an underlying network. Many times networks over which contagions spread are unobserved, and such networks are often dynamic and change over time. In this paper, w ..."
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Cited by 22 (1 self)
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Diffusion of information, spread of rumors and infectious diseases are all instances of stochastic processes that occur over the edges of an underlying network. Many times networks over which contagions spread are unobserved, and such networks are often dynamic and change over time. In this paper, we investigate the problem of inferring dynamic networks based on information diffusion data. We assume there is an unobserved dynamic network that changes over time, while we observe the results of a dynamic process spreading over the edges of the network. The task then is to infer the edges and the dynamics of the underlying network. We develop an online algorithm that relies on stochastic convex optimization to efficiently solve the dynamic network inference problem. We apply our algorithm to information diffusion among 3.3 million mainstream media and blog sites and experiment with more than 179 million different pieces of information spreading over the network in a one year period. We study the evolution of information pathways in the online media space and find interesting insights. Information pathways for general recurrent topics are more stable across time than for ongoing news events. Clusters of news media sites and blogs often emerge and vanish in matter of days for ongoing news events. Major social movements and events involving civil population, such as the Libyan’s civil war or Syria’s uprise, lead to an increased amount of information pathways among blogs as well as in the overall increase in the network centrality of blogs and social media sites.
Shaping Social Activity by Incentivizing Users
"... Events in an online social network can be categorized roughly into endogenous events, where users just respond to the actions of their neighbors within the network, or exogenous events, where users take actions due to drives external to the network. How much external drive should be provided to each ..."
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Cited by 10 (7 self)
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Events in an online social network can be categorized roughly into endogenous events, where users just respond to the actions of their neighbors within the network, or exogenous events, where users take actions due to drives external to the network. How much external drive should be provided to each user, such that the network activity can be steered towards a target state? In this paper, we model social events using multivariate Hawkes processes, which can capture both endogenous and exogenous event intensities, and derive a time dependent linear relation between the intensity of exogenous events and the overall network activity. Exploiting this connection, we develop a convex optimization framework for determining the required level of external drive in order for the network to reach a desired activity level. We experimented with event data gathered from Twitter, and show that our method can steer the activity of the network more accurately than alternatives. 1
Influence function learning in information diffusion networks
, 2014
"... Can we learn the influence of a set of people in a social network from cascades of information diffusion? This question is often addressed by a twostage approach: first learn a diffusion model, and then calculate the influence based on the learned model. Thus, the success of this approach relies ..."
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Cited by 5 (1 self)
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Can we learn the influence of a set of people in a social network from cascades of information diffusion? This question is often addressed by a twostage approach: first learn a diffusion model, and then calculate the influence based on the learned model. Thus, the success of this approach relies heavily on the correctness of the diffusion model which is hard to verify for real world data. In this paper, we exploit the insight that the influence functions in many diffusion models are coverage functions, and propose a novel parameterization of such functions using a convex combination of random basis functions. Moreover, we propose an efficient maximum likelihood based algorithm to learn such functions directly from cascade data, and hence bypass the need to specify a particular diffusion model in advance. We provide both theoretical and empirical analysis for our approach, showing that the proposed approach can provably learn the influence function with low sample complexity, be robust to the unknown diffusion models, and significantly outperform existing approaches in both synthetic and real world data. 1.
Budgeted influence maximization for multiple products
 CoRR
"... The typical algorithmic problem in viral marketing aims to identify a set of influential users in a social network, who, when convinced to adopt a product, shall influence other users in the network and trigger a large cascade of adoptions. However, the host (the owner of an online social platform) ..."
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Cited by 2 (1 self)
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The typical algorithmic problem in viral marketing aims to identify a set of influential users in a social network, who, when convinced to adopt a product, shall influence other users in the network and trigger a large cascade of adoptions. However, the host (the owner of an online social platform) often faces more constraints than a single product, endless user attentions, unlimited budget and unbounded time; in reality, multiple products need to be advertised, each user can tolerate only a small number of recommendations, influencing user has a cost and advertisers have only limited budgets, and the adoptions need to be maximized within a short time window. Given theses myriads of user, monetary, and timing constraints, it is extremely challenging for the host to design principled and efficient viral market algorithms with provable guarantees. In this paper, we provide a novel solution by formulating the problem as a submodular maximization in a continuoustime diffusion model under an intersection of a matroid and multiple knapsack constraints. We also propose an adaptive threshold greedy algorithm which can be faster than the traditional greedy algorithm with lazy evaluation, and scalable to networks with million of nodes. Furthermore, our mathematical formulation allows us to prove that the algorithm can achieve an approximation factor of ka/(2 + 2k) when ka out of the k knapsack constraints are active, which also improves over previous guarantees from combinatorial optimization literature. In the case when influencing each user has uniform cost, the approximation becomes even better to a factor of 1/3. Extensive synthetic and real world experiments demonstrate that our budgeted influence maximization algorithm achieves thestateoftheart in terms of both effectiveness and scalability, often beating the next best by significant margins. 1
Diffusion of Networking Technologies
 ELECTRONIC COMMERCE (EC’12); VALENCIA, SPAIN
, 2012
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Efficient Algorithms to Optimize Diffusion Processes under the Independent Cascade Model
"... We study scalable algorithms to optimize diffusion processes under the Independent Cascade model. We consider a broad class of intervention actions, including selecting sources, raising the probability that the diffusion propagates from one node to another and changing the topology of networks to f ..."
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Cited by 1 (0 self)
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We study scalable algorithms to optimize diffusion processes under the Independent Cascade model. We consider a broad class of intervention actions, including selecting sources, raising the probability that the diffusion propagates from one node to another and changing the topology of networks to facilitate the diffusion. Optimizing the selection of such actions with a limited budget tends to be NPhard and is neither submodular nor supermodular. We provide scalable algorithms for three different problem settings that range in terms of the strength of the assumptions we make about the model. The algorithms are very efficient (faster than a baseline greedy algorithm), producing highquality solutions in several diffusion maximization problems in the area of computational sustainability and in some cases also have provable approximation guarantees. These techniques offer promising results that may be applied to diffusion optimization problems in social and information networks. 1