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The Bursty Dynamics of the Twitter Information Network
"... In online social media systems users are not only posting, consum-ing, and resharing content, but also creating new and destroying existing connections in the underlying social network. While each of these two types of dynamics has individually been studied in the past, much less is known about the ..."
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In online social media systems users are not only posting, consum-ing, and resharing content, but also creating new and destroying existing connections in the underlying social network. While each of these two types of dynamics has individually been studied in the past, much less is known about the connection between the two. How does user information posting and seeking behavior interact with the evolution of the underlying social network structure? Here, we study ways in which network structure reacts to users posting and sharing content. We examine the complete dynamics of the Twitter information network, where users post and reshare information while they also create and destroy connections. We find that the dynamics of network structure can be characterized by steady rates of change, interrupted by sudden bursts. Information diffusion in the form of cascades of post re-sharing often creates such sudden bursts of new connections, which significantly change users ’ local network structure. These bursts transform users ’ net-works of followers to become structurally more cohesive as well as more homogenous in terms of follower interests. We also explore the effect of the information content on the dynamics of the net-work and find evidence that the appearance of new topics and real-world events can lead to significant changes in edge creations and deletions. Lastly, we develop a model that quantifies the dynam-ics of the network and the occurrence of these bursts as a function of the information spreading through the network. The model can successfully predict which information diffusion events will lead to bursts in network dynamics.
Predicting Successful Memes using Network and Community Structure
"... We investigate the predictability of successful memes using their early spreading patterns in the underlying social net-works. We propose and analyze a comprehensive set of fea-tures and develop an accurate model to predict future popu-larity of a meme given its early spreading patterns. Our pa-per ..."
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We investigate the predictability of successful memes using their early spreading patterns in the underlying social net-works. We propose and analyze a comprehensive set of fea-tures and develop an accurate model to predict future popu-larity of a meme given its early spreading patterns. Our pa-per provides the first comprehensive comparison of existing predictive frameworks. We categorize our features into three groups: influence of early adopters, community concentra-tion, and characteristics of adoption time series. We find that features based on community structure are the most powerful predictors of future success. We also find that early popular-ity of a meme is not a good predictor of its future popularity, contrary to common belief. Our methods outperform other approaches, particularly in the task of detecting very popular or unpopular memes.
Scalable Methods for Adaptively Seeding a Social Network
"... In recent years, social networking platforms have developed into extraordinary channels for spreading and consuming in-formation. Along with the rise of such infrastructure, there is continuous progress on techniques for spreading informa-tion effectively through influential users. In many applica-t ..."
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In recent years, social networking platforms have developed into extraordinary channels for spreading and consuming in-formation. Along with the rise of such infrastructure, there is continuous progress on techniques for spreading informa-tion effectively through influential users. In many applica-tions, one is restricted to select influencers from a set of users who engaged with the topic being promoted, and due to the structure of social networks, these users often rank low in terms of their influence potential. An alternative ap-proach one can consider is an adaptive method which selects users in a manner which targets their influential neighbors. The advantage of such an approach is that it leverages the friendship paradox in social networks: while users are often not influential, they often know someone who is. Despite the various complexities in such optimization prob-lems, we show that scalable adaptive seeding is achievable. In particular, we develop algorithms for linear influence mod-els with provable approximation guarantees that can be grace-fully parallelized. To show the effectiveness of our methods we collected data from various verticals social network users follow. For each vertical, we collected data on the users who responded to a certain post as well as their neighbors, and applied our methods on this data. Our experiments show that adaptive seeding is scalable, and importantly, that it obtains dramatic improvements over standard approaches of information dissemination.
From Micro to Macro: Uncovering and Predicting Information Cascading Process with Behavioral Dynamics
"... Abstract—Cascades are ubiquitous in various network en-vironments. How to predict these cascades is highly nontrivial in several vital applications, such as viral marketing, epidemic prevention and traffic management. Most previous works mainly focus on predicting the final cascade sizes. As cascade ..."
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Abstract—Cascades are ubiquitous in various network en-vironments. How to predict these cascades is highly nontrivial in several vital applications, such as viral marketing, epidemic prevention and traffic management. Most previous works mainly focus on predicting the final cascade sizes. As cascades are typical dynamic processes, it is always interesting and important to predict the cascade size at any time, or predict the time when a cascade will reach a certain size (e.g. an threshold for outbreak). In this paper, we unify all these tasks into a fundamental problem: cascading process prediction. That is, given the early stage of a cascade, how to predict its cumulative cascade size of any later time? For such a challenging problem, how to understand the micro mechanism that drives and generates the macro phenomena (i.e. cascading process) is essential. Here we introduce behavioral dynamics as the micro mechanism to describe the dynamic process of a node’s neighbors getting infected by a cascade after this node getting infected (i.e. one-hop subcascades). Through data-driven analysis, we find out the common principles and patterns lying in behavioral dynamics and propose a novel Networked Weibull Regression model for behavioral dynamics modeling. After that we propose a novel method for predicting cascading processes by effectively aggregating behavioral dynam-ics, and present a scalable solution to approximate the cascading process with a theoretical guarantee. We extensively evaluate the proposed method on a large scale social network dataset. The results demonstrate that the proposed method can significantly outperform other state-of-the-art baselines in multiple tasks including cascade size prediction, outbreak time prediction and cascading process prediction.
Influence at Scale: Distributed Computation of Complex Contagion in Networks
"... We consider the task of evaluating the spread of influence in large networks in the well-studied independent cascade model. We describe a novel sampling approach that can be used to design scalable algorithms with provable perfor-mance guarantees. These algorithms can be implemented in distributed c ..."
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We consider the task of evaluating the spread of influence in large networks in the well-studied independent cascade model. We describe a novel sampling approach that can be used to design scalable algorithms with provable perfor-mance guarantees. These algorithms can be implemented in distributed computation frameworks such as MapReduce. We complement these results with a lower bound on the query complexity of influence estimation in this model. We validate the performance of these algorithms through exper-iments that demonstrate the efficacy of our methods and related heuristics. 1.
Event prediction with community leaders
- in Proc. 10th Conference on Availability, Reliability and Security. IEEE CS
"... Abstract—With the emerging of online social network services, quantitative studies on social influence become achievable. Lead-ership is one of the most intuitive and common forms for social influence; understanding it could result in appealing applications such as targeted advertising and viral mar ..."
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Abstract—With the emerging of online social network services, quantitative studies on social influence become achievable. Lead-ership is one of the most intuitive and common forms for social influence; understanding it could result in appealing applications such as targeted advertising and viral marketing. In this work, we focus on investigating leaders ’ influence for event prediction in social networks. We propose an algorithm based on events that users conduct to discover leaders in social communities. Analysis on the leaders that we found on a real-life social network dataset leads us to several interesting observations, such as that leaders do not have significantly higher number of friends but are more active than other community members. We demonstrate the effectiveness of leaders ’ influence on users ’ behaviors by learning tasks: given a leader has conducted one event, whether and when a user will perform the event. Experimental results show that with only a few leaders in a community the event predictions are always very effective. I.
SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity
"... Social networking websites allow users to create and share content. Big information cascades of post resharing can form as users of these sites reshare others ’ posts with their friends and followers. One of the central challenges in understanding such cascading be-haviors is in forecasting informat ..."
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Social networking websites allow users to create and share content. Big information cascades of post resharing can form as users of these sites reshare others ’ posts with their friends and followers. One of the central challenges in understanding such cascading be-haviors is in forecasting information outbreaks, where a single post becomes widely popular by being reshared by many users. In this paper, we focus on predicting the final number of reshares of a given post. We build on the theory of self-exciting point pro-cesses to develop a statistical model that allows us to make accu-rate predictions. Our model requires no training or expensive fea-ture engineering. It results in a simple and efficiently computable formula that allows us to answer questions, in real-time, such as: Given a post’s resharing history so far, what is our current estimate of its final number of reshares? Is the post resharing cascade past the initial stage of explosive growth? And, which posts will be the most reshared in the future? We validate our model using one month of complete Twitter data and demonstrate a strong improvement in predictive accuracy over existing approaches. Our model gives only 15 % relative error in predicting final size of an average information cascade after ob-serving it for just one hour.
Global Diffusion via Cascading Invitations: Structure, Growth, and Homophily
"... Many of the world’s most popular websites catalyze their growth through invitations from existing members. Newmembers can then in turn issue invitations, and so on, creating cascades of member signups that can spread on a global scale. Although these diffu-sive invitation processes are critical to t ..."
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Many of the world’s most popular websites catalyze their growth through invitations from existing members. Newmembers can then in turn issue invitations, and so on, creating cascades of member signups that can spread on a global scale. Although these diffu-sive invitation processes are critical to the popularity and growth of many websites, they have rarely been studied, and their properties remain elusive. For instance, it is not known how viral these cas-cades structures are, how cascades grow over time, or how diffusive growth affects the resulting distribution of member characteristics present on the site. In this paper, we study the diffusion of LinkedIn, an online pro-fessional network comprising over 332 million members, a large fraction of whom joined the site as part of a signup cascade. First we analyze the structural patterns of these signup cascades, and find them to be qualitatively different from previously studied in-formation diffusion cascades. We also examine how signup cas-cades grow over time, and observe that diffusion via invitations on LinkedIn occurs over much longer timescales than are typically as-sociated with other types of online diffusion. Finally, we connect the cascade structures with rich individual-level attribute data to investigate the interplay between the two. Using novel techniques to study the role of homophily in diffusion, we find striking dif-ferences between the local, edge-wise homophily and the global, cascade-level homophily we observe in our data, suggesting that signup cascades form surprisingly coherent groups of members.
The Lifecycles of Apps in a Social Ecosystem
"... Apps are emerging as an important form of on-line content, and they combine aspects of Web usage in interesting ways — they ex-hibit a rich temporal structure of user adoption and long-term en-gagement, and they exist in a broader social ecosystem that helps drive these patterns of adoption and enga ..."
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Apps are emerging as an important form of on-line content, and they combine aspects of Web usage in interesting ways — they ex-hibit a rich temporal structure of user adoption and long-term en-gagement, and they exist in a broader social ecosystem that helps drive these patterns of adoption and engagement. It has been dif-ficult, however, to study apps in their natural setting since this re-quires a simultaneous analysis of a large set of popular apps and the underlying social network they inhabit. In this work we address this challenge through an analysis of the collection of apps on Facebook Login, developing a novel frame-work for analyzing both temporal and social properties. At the tem-poral level, we develop a retention model that represents a user’s tendency to return to an app using a very small parameter set. At the social level, we organize the space of apps along two funda-mental axes — popularity and sociality — and we show how a user’s probability of adopting an app depends both on properties of the local network structure and on the match between the user’s attributes, his or her friends ’ attributes, and the dominant attributes within the app’s user population. We also devolop models that show the importance of different feature sets with strong performance in predicting app success.
Beyond Models: Forecasting Complex Network Processes Directly from Data
"... Complex network phenomena – such as information cascades in online social networks – are hard to fully observe, model, and forecast. In forecasting, a recent trend has been to forgo the use of parsimonious models in favor of models with in-creasingly large degrees of freedom that are trained to lear ..."
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Complex network phenomena – such as information cascades in online social networks – are hard to fully observe, model, and forecast. In forecasting, a recent trend has been to forgo the use of parsimonious models in favor of models with in-creasingly large degrees of freedom that are trained to learn the behavior of a process from historical data. Extrapolat-ing this trend into the future, eventually we would renounce models all together. But is it possible to forecast the evo-lution of a complex stochastic process directly from the data without a model? In this work we show that model-free fore-casting is possible. We present SED, an algorithm that fore-casts process statistics based on relationships of statistical equivalence using two general axioms and historical data. To the best of our knowledge, SED is the first method that can perform axiomatic, model-free forecasts of complex stochas-tic processes. Our simulations using simple and complex evolving processes and tests performed on a large real-world dataset show promising results.
