Temporal point processes (TPPs) have become ubiquitous in research in recent years, with applications ranging from analysis of online information diffusion to networking systems. These processes comprise a series of discrete events localized in continuous time. In several cases, it is observed that multiple processes are realized in the same time-span which are likely to influence each other, often in a competitive way. In the case of hashtag diffusion in Twitter, the mentions of several hashtags related to the same event constitute a set of mutually interacting processes which exhibit competitive dynamics. In the case of Amazon product reviews, the review streams of similar products constitute a set of related processes which may compete with each other due to brand competition between products. In the case of computer network traffic, the packet streams generated by hosts on the same network constitute mutually influencing processes which also exhibit competition due to link bandwidth constraints. Whereas several works have tried to model these processes individually, joint modeling of such processes along with their interactions has not been explored in the literature very well. In this work, we survey a few emerging techniques developed by the authors which deal with modeling such joint interactive processes. These techniques show that with simple modifications to known techniques of neural TPP modeling, it is possible to model the interactions between concurrent processes effectively. Such methods also yield substantial improvements over the existing methods which seek to model individual processes without considering the joint interactions between them.

近年来，时间点过程 (TPP) 在研究中无处不在，其应用范围从分析在线信息传播到网络系统。这些过程包括一系列在连续时间内局部化的离散事件。在几种情况下，可以观察到在同一时间跨度内实现的多个过程可能会相互影响，通常以竞争方式实现。在 Twitter 中的话题标签传播的情况下，与同一事件相关的几个话题标签的提及构成了一组相互交互的过程，这些过程表现出竞争动态。以亚马逊产品评论为例，同类产品的评论流构成了一组相关的流程，由于产品之间的品牌竞争，可能会相互竞争。在计算机网络流量的情况下，同一网络上的主机生成的数据包流构成相互影响的过程，由于链路带宽限制，这些过程也表现出竞争。尽管有几部作品试图单独对这些过程进行建模，但文献中尚未很好地探索这些过程及其相互作用的联合建模。在这项工作中，我们调查了一些由作者开发的新兴技术，用于处理此类联合交互过程的建模。这些技术表明，通过对已知的神经 TPP 建模技术的简单修改，可以有效地对并发过程之间的交互进行建模。与寻求对单个过程建模而不考虑它们之间的联合交互的现有方法相比，这些方法也产生了实质性的改进。