Uncovering Causality from Multivariate Hawkes Integrated Cumulants

We design a new nonparametric method that allows one to estimate the matrix of integrated kernels of a multivariate Hawkes process. This matrix not only encodes the mutual influences of each nodes of the process, but also disentangles the causality relationships between them. Our approach is the first that leads to an estimation of this matrix without any parametric modeling and estimation of the kernels themselves. A consequence is that it can give an estimation of causality relationships between nodes (or users), based on their activity timestamps (on a social network for instance), without knowing or estimating the shape of the activities lifetime. For that purpose, we introduce a moment matching method that fits the third-order integrated cumulants of the process. We show on numerical experiments that our approach is indeed very robust to the shape of the kernels, and gives appealing results on the MemeTracker database

Learning of networked spreading models from noisy and incomplete data

Recent years have seen a lot of progress in algorithms for learning parameters of spreading dynamics from both full and partial data. Some of the remaining challenges include model selection under the scenarios of unknown network structure, noisy data, missing observations in time, as well as an efficient incorporation of prior information to minimize the number of samples required for an accurate learning. Here, we introduce a universal learning method based on scalable dynamic message-passing technique that addresses these challenges often encountered in real data. The algorithm leverages available prior knowledge on the model and on the data, and reconstructs both network structure and parameters of a spreading model. We show that a linear computational complexity of the method with the key model parameters makes the algorithm scalable to large network instances.Comment: 22 pages, 10 figure

Cultural Diffusion and Trends in Facebook Photographs

Online social media is a social vehicle in which people share various moments of their lives with their friends, such as playing sports, cooking dinner or just taking a selfie for fun, via visual means, that is, photographs. Our study takes a closer look at the popular visual concepts illustrating various cultural lifestyles from aggregated, de-identified photographs. We perform analysis both at macroscopic and microscopic levels, to gain novel insights about global and local visual trends as well as the dynamics of interpersonal cultural exchange and diffusion among Facebook friends. We processed images by automatically classifying the visual content by a convolutional neural network (CNN). Through various statistical tests, we find that socially tied individuals more likely post images showing similar cultural lifestyles. To further identify the main cause of the observed social correlation, we use the Shuffle test and the Preference-based Matched Estimation (PME) test to distinguish the effects of influence and homophily. The results indicate that the visual content of each user's photographs are temporally, although not necessarily causally, correlated with the photographs of their friends, which may suggest the effect of influence. Our paper demonstrates that Facebook photographs exhibit diverse cultural lifestyles and preferences and that the social interaction mediated through the visual channel in social media can be an effective mechanism for cultural diffusion.Comment: 10 pages, To appear in ICWSM 2017 (Full Paper

From temporal network data to the dynamics of social relationships

Networks are well-established representations of social systems, and temporal networks are widely used to study their dynamics. Temporal network data often consist in a succession of static networks over consecutive time windows whose length, however, is arbitrary, not necessarily corresponding to any intrinsic timescale of the system. Moreover, the resulting view of social network evolution is unsatisfactory: short time windows contain little information, whereas aggregating over large time windows blurs the dynamics. Going from a temporal network to a meaningful evolving representation of a social network therefore remains a challenge. Here we introduce a framework to that purpose: transforming temporal network data into an evolving weighted network where the weights of the links between individuals are updated at every interaction. Most importantly, this transformation takes into account the interdependence of social relationships due to the finite attention capacities of individuals: each interaction between two individuals not only reinforces their mutual relationship but also weakens their relationships with others. We study a concrete example of such a transformation and apply it to several data sets of social interactions. Using temporal contact data collected in schools, we show how our framework highlights specificities in their structure and temporal organization. We then introduce a synthetic perturbation into a data set of interactions in a group of baboons to show that it is possible to detect a perturbation in a social group on a wide range of timescales and parameters. Our framework brings new perspectives to the analysis of temporal social networks

On the Convexity of Latent Social Network Inference

In many real-world scenarios, it is nearly impossible to collect explicit social network data. In such cases, whole networks must be inferred from underlying observations. Here, we formulate the problem of inferring latent social networks based on network diffusion or disease propagation data. We consider contagions propagating over the edges of an unobserved social network, where we only observe the times when nodes became infected, but not who infected them. Given such node infection times, we then identify the optimal network that best explains the observed data. We present a maximum likelihood approach based on convex programming with a l1-like penalty term that encourages sparsity. Experiments on real and synthetic data reveal that our method near-perfectly recovers the underlying network structure as well as the parameters of the contagion propagation model. Moreover, our approach scales well as it can infer optimal networks of thousands of nodes in a matter of minutes.Comment: NIPS, 201

Studying Paths of Participation in Viral Diffusion Process

Authors propose a conceptual model of participation in viral diffusion process composed of four stages: awareness, infection, engagement and action. To verify the model it has been applied and studied in the virtual social chat environment settings. The study investigates the behavioral paths of actions that reflect the stages of participation in the diffusion and presents shortcuts, that lead to the final action, i.e. the attendance in a virtual event. The results show that the participation in each stage of the process increases the probability of reaching the final action. Nevertheless, the majority of users involved in the virtual event did not go through each stage of the process but followed the shortcuts. That suggests that the viral diffusion process is not necessarily a linear sequence of human actions but rather a dynamic system.Comment: In proceedings of the 4th International Conference on Social Informatics, SocInfo 201

Link Prediction with Personalized Social Influence

Link prediction in social networks is to infer the new links likely to be formed next or to reconstruct the links that are currently missing. Link prediction is of great interest recently since one of the most important goals of social networks is to connect people, so that they can interact with their friends from real world or make new friend through Internet. So the predicted links in social networks can be helpful for people to have connections with each others. Other than the pure topological network structures, social networks also have rich information of social activities of each user, such as tweeting, retweeting, and replying activities. Social science theories, such as social influence, suggests that the social activities could have potential impacts on the neighbors, and links in social networks are the results of the impacts taking place between different users. It motivates us to perform link prediction by taking advantage of the activity information. There has been a lot of proposed methods to measure the social influence through user activity information. However, traditional methods assigned some social influence measures to users universally based on their social activities, such as number of retweets or mentions the users have. But the social influence of one user towards others may not always remain the same with respect to different neighbors, which demands a personalized learning schema. Moreover, learning social influence from heterogeneous social activities is a nontrivial problem, since the information carried in the social activities is implicit and sometimes even noisy. Motivated by time-series analysis, we investigate the potential of modeling influence patterns based on pure timestamps, i.e., we aim to simplify the problem of processing heterogeneous social activities to a sequence of timestamps. Then we use timestamps as an abstraction of each activity to calculate the reduction of uncertainty of one users social activities given the knowledge of another one. The key idea is that, if a user i has impact on another user j, then given the activity timestamps of user i, the uncertainty in user j’s activity timestamps could be reduced. The uncertainty is measured by entropy in information theory, which is proven useful to detect the significant influence flow in time-series signals in information-theoretic applications. By employing the proposed influence metric, we incorporate the social activity information into the network structure, and learn a unified low-dimensional representation for all users. Thus, we could perform link prediction effectively based on the learned representation. Through comprehensive experiments, we demonstrate that the proposed method can perform better than the state-of-the-art methods in different real-world link prediction tasks