The Mixed-Membership Stochastic Blockmodel~(MMSB) is proposed as one of the
state-of-the-art Bayesian relational methods suitable for learning the complex
hidden structure underlying the network data. However, the current formulation
of MMSB suffers from the following two issues: (1), the prior information~(e.g.
entities' community structural information) can not be well embedded in the
modelling; (2), community evolution can not be well described in the
literature. Therefore, we propose a non-parametric fragmentation coagulation
based Mixed Membership Stochastic Blockmodel (fcMMSB). Our model performs
entity-based clustering to capture the community information for entities and
linkage-based clustering to derive the group information for links
simultaneously. Besides, the proposed model infers the network structure and
models community evolution, manifested by appearances and disappearances of
communities, using the discrete fragmentation coagulation process (DFCP). By
integrating the community structure with the group compatibility matrix we
derive a generalized version of MMSB. An efficient Gibbs sampling scheme with
Polya Gamma (PG) approach is implemented for posterior inference. We validate
our model on synthetic and real world data.

本文提出了一种基于分裂聚合非参数方法的混合成员随机块模型，可以较好地应用于网络数据的学习和社区结构的建模，同时进行了合成和真实数据的验证。

基于片段聚合的混合成员随机块模型

Fragmentation Coagulation Based Mixed Membership Stochastic Blockmodel

Recent findings showed that users on Facebook tend to select information that
adhere to their system of beliefs and to form polarized groups -- i.e., echo
chambers. Such a tendency dominates information cascades and might affect
public debates on social relevant issues. In this work we explore the
structural evolution of communities of interest by accounting for users
emotions and engagement. Focusing on the Facebook pages reporting on scientific
and conspiracy content, we characterize the evolution of the size of the two
communities by fitting daily resolution data with three growth models -- i.e.
the Gompertz model, the Logistic model, and the Log-logistic model. Then, we
explore the interplay between emotional state and engagement of users in the
group dynamics. Our findings show that communities' emotional behavior is
affected by the users' involvement inside the echo chamber. Indeed, to an
higher involvement corresponds a more negative approach. Moreover, we observe
that, on average, more active users show a faster shift towards the negativity
than less active ones.

本文探讨了在 Facebook 平台上科学和阴谋论社群的演化和情感行为对社群动态的影响。研究结果显示，用户对社群的参与程度和使用者情感行为对社群的演化有显著影响，越参与社群的使用者更容易表现出负面情绪，并且更活跃的用户比不活跃的用户更容易表现出负面情绪。

回音室：情感传染和群体极化在 Facebook 上的表现

Echo Chambers: Emotional Contagion and Group Polarization on Facebook

Predicting the future direction of community evolution is a problem with high
theoretical and practical significance. It allows to determine which
characteristics describing communities have importance from the point of view
of their future behaviour. Knowledge about the probable future career of the
community aids in the decision concerning investing in contact with members of
a given community and carrying out actions to achieve a key position in it. It
also allows to determine effective ways of forming opinions or to protect group
participants against such activities. In the paper, a new approach to group
identification and prediction of future events is presented together with the
comparison to existing method. Performed experiments prove a high quality of
prediction results. Comparison to previous studies shows that using many
measures to describe the group profile, and in consequence as a classifier
input, can improve predictions.

本文介绍了一种新的小组识别和未来事件预测方法，并与现有方法进行了比较。所进行的实验证明了预测结果的高质量，并表明使用多种指标来描述群体轮廓可以提高预测的效果。