Results 1  10
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19
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
Modeling Information Propagation with Survival Theory
, 2013
"... Networks provide a ‘skeleton’ for the spread of contagions, like, information, ideas, behaviors and diseases. Many times networks over which contagions diffuse are unobserved and need to be inferred. Here we apply survival theory to develop general additive and multiplicative risk models under which ..."
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Cited by 14 (5 self)
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Networks provide a ‘skeleton’ for the spread of contagions, like, information, ideas, behaviors and diseases. Many times networks over which contagions diffuse are unobserved and need to be inferred. Here we apply survival theory to develop general additive and multiplicative risk models under which the network inference problems can be solved efficiently by exploiting their convexity. Our additive risk model generalizes several existing network inference models. We show all these models are particular cases of our more general model. Our multiplicative model allows for modeling scenarios in which a node can either increase or decrease the risk of activation of another node, in contrast with previous approaches, which consider only positive risk increments. We evaluate the performance of our network inference algorithms on large synthetic and real cascade datasets, and show that our models are able to predict the length and duration of cascades in real data.
Estimating diffusion network structures: Recovery conditions, sample complexity & softthresholding algorithm
 In Proc. of the 31st International Conference on Machine Learning (ICML
, 2014
"... Information spreads across social and technological networks, but often the network structures are hidden from us and we only observe the traces left by the diffusion processes, called cascades. Can we recover the hidden network structures from these observed cascades? What kind of cascades and ho ..."
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Cited by 6 (3 self)
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Information spreads across social and technological networks, but often the network structures are hidden from us and we only observe the traces left by the diffusion processes, called cascades. Can we recover the hidden network structures from these observed cascades? What kind of cascades and how many cascades do we need? Are there some network structures which are more difficult than others to recover? Can we design efficient inference algorithms with provable guarantees? Despite the increasing availability of cascadedata and methods for inferring networks from these data, a thorough theoretical understanding of the above questions remains largely unexplored in the literature. In this paper, we investigate the network structure inference problem for a general family of continuoustime diffusion models using an `1regularized likelihood maximization framework. We show that, as long as the cascade sampling process satisfies a natural incoherence condition, our framework can recover the correct network structure with high probability if we observe O(d3 logN) cascades, where d is the maximum number of parents of a node and N is the total number of nodes. Moreover, we develop a simple and efficient softthresholding inference algorithm, which we use to illustrate the consequences of our theoretical results, and show that our framework outperforms other alternatives in practice.
Trace Complexity of Network Inference
, 2013
"... The network inference problem consists of reconstructing the edge set of a network given traces representing the chronology of infection times as epidemics spread through the network. This problem is a paradigmatic representative of prediction tasks in machine learning that require deducing a latent ..."
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Cited by 5 (1 self)
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The network inference problem consists of reconstructing the edge set of a network given traces representing the chronology of infection times as epidemics spread through the network. This problem is a paradigmatic representative of prediction tasks in machine learning that require deducing a latent structure from observed patterns of activity in a network, which often require an unrealistically large number of resources (e.g., amount of available data, or computational time). A fundamental question is to understand which properties we can predict with a reasonable degree of accuracy with the available resources, and which we cannot. We define the trace complexity as the number of distinct traces required to achieve high fidelity in reconstructing the topology of the unobserved network or, more generally, some of its properties. We give algorithms that are competitive with, while being simpler and more efficient
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.
Modeling Adoption and Usage of Competing Products
"... Abstract—The emergence and widespread use of online social networks has led to a dramatic increase on the availability of social activity data. Importantly, this data can be exploited to investigate, at a microscopic level, some of the problems that have captured the attention of economists, market ..."
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Cited by 2 (1 self)
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Abstract—The emergence and widespread use of online social networks has led to a dramatic increase on the availability of social activity data. Importantly, this data can be exploited to investigate, at a microscopic level, some of the problems that have captured the attention of economists, marketers and sociologists for decades, such as, e.g., product adoption, usage and competition. In this paper, we propose a continuoustime probabilistic model, based on temporal point processes, for the adoption and frequency of use of competing products, where the frequency of use of one product can be modulated by those of others. This model allows us to efficiently simulate the adoption and recurrent usages of competing products, and generate traces in which we can easily recognize the effect of social influence, recency and competition. We then develop an inference method to efficiently fit the model parameters by solving a convex program. The problem decouples into a collection of smaller subproblems, thus scaling easily to networks with hundred of thousands of nodes. We validate our model over synthetic and real diffusion data gathered from Twitter, and show that the proposed model does not only provides a good fit to the data and more accurate predictions than alternatives but also provides interpretable model parameters, which allow us to gain insights into some of the factors driving product adoption and frequency of use. I.
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
Evolutionary Dynamics of Information Diffusion Over Social Networks
"... Abstract—Current social networks are of extremely largescale generating tremendous information flows at every moment. How information diffuses over social networks has attracted much attention from both industry and academics. Most of the existing works on information diffusion analysis are based o ..."
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Abstract—Current social networks are of extremely largescale generating tremendous information flows at every moment. How information diffuses over social networks has attracted much attention from both industry and academics. Most of the existing works on information diffusion analysis are based on machine learning methods focusing on social network structure analysis and empirical data mining. However, the network users ’ decisions, actions, and socioeconomic interactions are generally ignored by most of existing works. In this paper, we propose an evolutionary game theoretic framework to model the dynamic information diffusion process in social networks. Specifically, we derive the information diffusion dynamics in complete networks, uniform degree, and nonuniform degree networks, with the highlight of two special networks, the Erdős–Rényi random network and the Barabási–Albert scalefree network. We find that the dynamics of information diffusion over these three kinds of networks are scalefree and all the three dynamics are same with each other when the network scale is sufficiently large. To verify our theoretical analysis, we perform simulations for the information diffusion over synthetic networks and realworld Facebook networks. Moreover, we also conduct an experiment on a Twitter hashtags dataset, which shows that the proposed game theoretic model can well fit and predict the information diffusion over real social networks. Index Terms—Evolutionary game, game theory, information diffusion, information spreading, social networks. I.