The static synaptic connectivity of neuronal circuits stands in direct
contrast to the dynamics of their function. As in changing community
interactions, different neurons can participate actively in various
combinations to effect behaviors at different times. We introduce an
unsupervised approach to learn the dynamic affinities between neurons in live,
behaving animals, and to reveal which communities form among neurons at
different times. The inference occurs in two major steps. First, pairwise
non-linear affinities between neuronal traces from brain-wide calcium activity
are organized by non-negative tensor factorization (NTF). Each factor specifies
which groups of neurons are most likely interacting for an inferred interval in
time, and for which animals. Finally, a generative model that allows for
weighted community detection is applied to the functional motifs produced by
NTF to reveal a dynamic functional connectome. Since time codes the different
experimental variables (e.g., application of chemical stimuli), this provides
an atlas of neural motifs active during separate stages of an experiment (e.g.,
stimulus application or spontaneous behaviors). Results from our analysis are
experimentally validated, confirming that our method is able to robustly
predict causal interactions between neurons to generate behavior. Code is
available at this https URL

使用无监督方法从脑内活体神经元跟踪中学习动态亲和力，揭示神经元在不同时间形成的群体互动，进而构建动态功能连接图。

学习神经生物网络中功能连接组的动态表示

Learning dynamic representations of the functional connectome in  neurobiological networks

Users organize themselves into communities on web platforms. These
communities can interact with one another, often leading to conflicts and toxic
interactions. However, little is known about the mechanisms of interactions
between communities and how they impact users.
Here we study intercommunity interactions across 36,000 communities on
Reddit, examining cases where users of one community are mobilized by negative
sentiment to comment in another community. We show that such conflicts tend to
be initiated by a handful of communities---less than 1% of communities start
74% of conflicts. While conflicts tend to be initiated by highly active
community members, they are carried out by significantly less active members.
We find that conflicts are marked by formation of echo chambers, where users
primarily talk to other users from their own community. In the long-term,
conflicts have adverse effects and reduce the overall activity of users in the
targeted communities.
Our analysis of user interactions also suggests strategies for mitigating the
negative impact of conflicts---such as increasing direct engagement between
attackers and defenders. Further, we accurately predict whether a conflict will
occur by creating a novel LSTM model that combines graph embeddings, user,
community, and text features. This model can be used toreate early-warning
systems for community moderators to prevent conflicts. Altogether, this work
presents a data-driven view of community interactions and conflict, and paves
the way towards healthier online communities.

本研究考察了网站 Reddit 上 36,000 个社区之间的互动和如何导致冲突和负面互动。研究者发现少数社区 (小于 1%) 导致了 74% 的冲突，而冲突往往由高活跃度的社区成员开始，但由低活跃度的成员执行。冲突导致了 “回声室” 的形成，也对目标社区用户的整体活跃产生了负面影响。分析用户互动数据还提出了缓解冲突负面影响的策略，以及如何通过使用结合图嵌入，用户，社区和文本特征的 LSTM 模型来预测冲突的发生，早期预警系统可以防止冲突的发生。