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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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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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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.
Quantifying Information Overload in Social Media and its Impact on Social Contagions
"... Information overload has become an ubiquitous problem in modern society. Social media users and microbloggers receive an endless flow of information, often at a rate far higher than their cognitive abilities to process the information. In this paper, we conduct a large scale quantitative study of ..."
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Information overload has become an ubiquitous problem in modern society. Social media users and microbloggers receive an endless flow of information, often at a rate far higher than their cognitive abilities to process the information. In this paper, we conduct a large scale quantitative study of information overload and evaluate its impact on information dissemination in the Twitter social media site. We model social media users as information processing systems that queue incoming information according to some policies, process information from the queue at some unknown rates and decide to forward some of the incoming information to other users. We show how timestamped data about tweets received and forwarded by users can be used to uncover key properties of their queueing policies and estimate their information processing rates and limits. Such an understanding of users ’ information processing behaviors allows us to infer whether and to what extent users suffer from information overload. Our analysis provides empirical evidence of information processing limits for social media users and the prevalence of information overloading. The most active and popular social media users are often the ones that are overloaded. Moreover, we find that the rate at which users receive information impacts their processing behavior, including how they prioritize information from different sources, how much information they process, and how quickly they process information. Finally, the susceptibility of a social media user to social contagions depends crucially on the rate at which she receives information. An exposure to a piece of information, be it an idea, a convention or a product, is much less effective for users that receive information at higher rates, meaning they need more exposures to adopt a particular contagion.
Auxiliary gibbs sampling for inference in piecewiseconstant conditional intensity models
 In UAI
, 2015
"... A piecewiseconstant conditional intensity model (PCIM) is a nonMarkovian model of temporal stochastic dependencies in continuoustime event streams. It allows efficient learning and forecasting given complete trajectories. However, no general inference algorithm has been developed for PCIMs. We pr ..."
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A piecewiseconstant conditional intensity model (PCIM) is a nonMarkovian model of temporal stochastic dependencies in continuoustime event streams. It allows efficient learning and forecasting given complete trajectories. However, no general inference algorithm has been developed for PCIMs. We propose an effective and efficient auxiliary Gibbs sampler for inference in PCIM, based on the idea of thinning for inhomogeneous Poisson processes. The sampler alternates between sampling a finite set of auxiliary virtual events with adaptive rates, and performing an efficient forwardbackward pass at discrete times to generate samples. We show that our sampler can successfully perform inference tasks in both Markovian and nonMarkovian models, and can be employed in ExpectationMaximization PCIM parameter estimation and structural learning with partially observed 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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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.
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"... Uncovering the structure and temporal dynamics of information propagation ..."
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Uncovering the structure and temporal dynamics of information propagation
Back to the Past: Source Identification in Diffusion Networks from Partially Observed Cascades
"... When a piece of malicious information becomes rampant in an information diffusion network, can we identify the source node that originally introduced the piece into the network and infer the time when it initiated this? Being able to do so is critical for curtailing the spread of malicious informat ..."
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When a piece of malicious information becomes rampant in an information diffusion network, can we identify the source node that originally introduced the piece into the network and infer the time when it initiated this? Being able to do so is critical for curtailing the spread of malicious information, and reducing the potential losses incurred. This is a very challenging problem since typically only incomplete traces are observed and we need to unroll the incomplete traces into the past in order to pinpoint the source. In this paper, we tackle this problem by developing a twostage framework, which first learns a continuoustime diffusion network model based on historical diffusion traces and then identifies the source of an incomplete diffusion trace by maximizing the likelihood of the trace under the learned model. Experiments on both large synthetic and realworld data show that our framework can effectively “go back to the past”, and pinpoint the source node and its initiation time significantly more accurately than previous stateofthearts. 1
Minimizing Seed Set Selection with Probabilistic Coverage Guarantee in a Social Network
"... A topic propagating in a social network reaches its tipping point if the number of users discussing it in the network exceeds a critical threshold such that a wide cascade on the topic is likely to occur. In this paper, we consider the task of selecting initial seed users of a topic with minimum si ..."
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A topic propagating in a social network reaches its tipping point if the number of users discussing it in the network exceeds a critical threshold such that a wide cascade on the topic is likely to occur. In this paper, we consider the task of selecting initial seed users of a topic with minimum size so that with a guaranteed probability the number of users discussing the topic would reach a given threshold. We formulate the task as an optimization problem called seed minimization with probabilistic coverage guarantee (SMPCG). This problem departs from the previous studies on social influence maximization or seed minimization because it considers influence coverage with probabilistic guarantees instead of guarantees on expected influence coverage. We show that the problem is not submodular, and thus is harder than previously studied problems based on submodular function optimization. We provide an approximation algorithm and show that it approximates the optimal solution with both a multiplicative ratio and an additive error. The multiplicative ratio is tight while the additive error would be small if influence coverage distributions of certain seed sets are well concentrated. For oneway bipartite graphs we analytically prove the concentration condition and obtain an approximation algorithm with an O(logn) multiplicative ratio and an O( n) additive error, where n is the total number of nodes in the social graph. Moreover, we empirically verify the concentration condition in realworld networks and experimentally demonstrate the effectiveness of our proposed algorithm comparing to commonly adopted benchmark algorithms.
Diffusion Maximization in Evolving Social Networks
"... Diffusion in social networks has been studied extensively in the past few years. Most previous work assumes that the underlying network is a static object that remains unchanged as the diffusion process progresses. However, there are several reallife networks that change dynamically over time. In ..."
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Diffusion in social networks has been studied extensively in the past few years. Most previous work assumes that the underlying network is a static object that remains unchanged as the diffusion process progresses. However, there are several reallife networks that change dynamically over time. In this paper, we study diffusion on such evolving networks and extend the popular Independent Cascade and Linear Threshold models to account for network evolution. In particular, we introduce two natural variations, a persistent and a transient one, to capture diffusions of different types. We consider the problem of influence maximization where the goal is to select a few influential nodes to initiate a diffusion with maximum spread. We show that, surprisingly, when considering evolving networks the diffusion function is no longer submodular for the transient models, and not even monotone for the transient Independent Cascade model. We also show that, depending on the model, delaying the activation of the initiators may improve diffusion. Our experiments, using three real datasets, demonstrate the effect of network evolution on the diffusion process, and highlight the importance of timing in the selection process.