Social influence among users (e.g., collaboration on a project) creates
bursty behavior in the underlying high performance computing (HPC) workloads.
Using representative HPC and cluster workload logs, this paper identifies,
analyzes, and quantifies the level of social influence across HPC users. We
show the existence of a social graph that is characterized by a pattern of
dominant users and followers. This pattern also follows a power-law
distribution, which is consistent with those observed in mainstream social
networks. Given its potential impact on HPC workloads prediction and
scheduling, we propose a fast-converging, computationally-efficient online
learning algorithm for identifying social groups. Extensive evaluation shows
that our online algorithm can (1) quickly identify the social relationships by
using a small portion of incoming jobs and (2) can efficiently track group
evolution over time.

本文研究了高性能计算中用户之间的社会影响，发现其存在社会图谱和力图建立一个可以快速识别社会关系并有效跟踪时间演化的在线学习算法。

高性能计算工作负载中社交影响的分析与建模

Analysis and Modeling of Social Influence in High Performance Computing  Workloads

Social instant messaging services are emerging as a transformative form with
which people connect, communicate with friends in their daily life - they
catalyze the formation of social groups, and they bring people stronger sense
of community and connection. However, research community still knows little
about the formation and evolution of groups in the context of social messaging
- their lifecycles, the change in their underlying structures over time, and
the diffusion processes by which they develop new members. In this paper, we
analyze the daily usage logs from WeChat group messaging platform - the largest
standalone messaging communication service in China - with the goal of
understanding the processes by which social messaging groups come together,
grow new members, and evolve over time. Specifically, we discover a strong
dichotomy among groups in terms of their lifecycle, and develop a separability
model by taking into account a broad range of group-level features, showing
that long-term and short-term groups are inherently distinct. We also found
that the lifecycle of messaging groups is largely dependent on their social
roles and functions in users' daily social experiences and specific purposes.
Given the strong separability between the long-term and short-term groups, we
further address the problem concerning the early prediction of successful
communities. In addition to modeling the growth and evolution from group-level
perspective, we investigate the individual-level attributes of group members
and study the diffusion process by which groups gain new members. By
considering members' historical engagement behavior as well as the local social
network structure that they embedded in, we develop a membership cascade model
and demonstrate the effectiveness by achieving AUC of 95.31% in predicting
inviter, and an AUC of 98.66% in predicting invitee.

通过分析中国最大的独立消息通信服务 WeChat 的日常使用日志，本文旨在理解社交消息群组如何相互联系，增长新成员并随着时间的推移发展。我们发现组的生命周期存在很强的二分法，它们的演化在很大程度上取决于它们在用户每日社交体验和特定目的中的社交角色和功能。此外，本文还研究了群成员的个体属性，并研究了群获得新成员的扩散过程，开发了一个会员级联模型并证明了其在预测邀请者和邀请接受者方面的有效性。