1,382 research outputs found
Longitudinal Modeling of Social Media with Hawkes Process based on Users and Networks
Online social networks provide a platform for
sharing information at an unprecedented scale. Users generate
information which propagates across the network resulting in
information cascades. In this paper, we study the evolution of
information cascades in Twitter using a point process model
of user activity. We develop several Hawkes process models
considering various properties including conversational structure,
users’ connections and general features of users including the
textual information, and show how they are helpful in modeling
the social network activity. We consider low-rank embeddings
of users and user features, and learn the features helpful in
identifying the influence and susceptibility of users. Evaluation
on Twitter data sets associated with civil unrest shows that
incorporating richer properties improves the performance in
predicting future activity of users and memes
Latent Self-Exciting Point Process Model for Spatial-Temporal Networks
We propose a latent self-exciting point process model that describes
geographically distributed interactions between pairs of entities. In contrast
to most existing approaches that assume fully observable interactions, here we
consider a scenario where certain interaction events lack information about
participants. Instead, this information needs to be inferred from the available
observations. We develop an efficient approximate algorithm based on
variational expectation-maximization to infer unknown participants in an event
given the location and the time of the event. We validate the model on
synthetic as well as real-world data, and obtain very promising results on the
identity-inference task. We also use our model to predict the timing and
participants of future events, and demonstrate that it compares favorably with
baseline approaches.Comment: 20 pages, 6 figures (v3); 11 pages, 6 figures (v2); previous version
appeared in the 9th Bayesian Modeling Applications Workshop, UAI'1
Group Network Hawkes Process
In this work, we study the event occurrences of individuals interacting in a
network. To characterize the dynamic interactions among the individuals, we
propose a group network Hawkes process (GNHP) model whose network structure is
observed and fixed. In particular, we introduce a latent group structure among
individuals to account for the heterogeneous user-specific characteristics. A
maximum likelihood approach is proposed to simultaneously cluster individuals
in the network and estimate model parameters. A fast EM algorithm is
subsequently developed by utilizing the branching representation of the
proposed GNHP model. Theoretical properties of the resulting estimators of
group memberships and model parameters are investigated under both settings
when the number of latent groups is over-specified or correctly specified.
A data-driven criterion that can consistently identify the true under mild
conditions is derived. Extensive simulation studies and an application to a
data set collected from Sina Weibo are used to illustrate the effectiveness of
the proposed methodology.Comment: 35 page
Recurrent Poisson Factorization for Temporal Recommendation
Poisson factorization is a probabilistic model of users and items for
recommendation systems, where the so-called implicit consumer data is modeled
by a factorized Poisson distribution. There are many variants of Poisson
factorization methods who show state-of-the-art performance on real-world
recommendation tasks. However, most of them do not explicitly take into account
the temporal behavior and the recurrent activities of users which is essential
to recommend the right item to the right user at the right time. In this paper,
we introduce Recurrent Poisson Factorization (RPF) framework that generalizes
the classical PF methods by utilizing a Poisson process for modeling the
implicit feedback. RPF treats time as a natural constituent of the model and
brings to the table a rich family of time-sensitive factorization models. To
elaborate, we instantiate several variants of RPF who are capable of handling
dynamic user preferences and item specification (DRPF), modeling the
social-aspect of product adoption (SRPF), and capturing the consumption
heterogeneity among users and items (HRPF). We also develop a variational
algorithm for approximate posterior inference that scales up to massive data
sets. Furthermore, we demonstrate RPF's superior performance over many
state-of-the-art methods on synthetic dataset, and large scale real-world
datasets on music streaming logs, and user-item interactions in M-Commerce
platforms.Comment: Submitted to KDD 2017 | Halifax, Nova Scotia - Canada - sigkdd, Codes
are available at https://github.com/AHosseini/RP
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