This paper investigates causal influences between agents linked by a social
graph and interacting over time. In particular, the work examines the dynamics
of social learning models and distributed decision-making protocols, and
derives expressions that reveal the causal relations between pairs of agents
and explain the flow of influence over the network. The results turn out to be
dependent on the graph topology and the level of information that each agent
has about the inference problem they are trying to solve. Using these
conclusions, the paper proposes an algorithm to rank the overall influence
between agents to discover highly influential agents. It also provides a method
to learn the necessary model parameters from raw observational data. The
results and the proposed algorithm are illustrated by considering both
synthetic data and real Twitter data.

本文研究社交图中相互关联的代理之间的因果影响，并考察社交学习模型和分布式决策协议的动态性，推导出表达式以揭示代理之间的因果关系并解释网络中的影响流动。结果依赖于图的拓扑结构和每个代理对于所解决的推断问题的信息水平。基于这些结论，本文提出了一种算法来评估代理的整体影响力以发现高度有影响力的代理，并提供了一种从原始观测数据中学习必要模型参数的方法。通过综合考虑合成数据和真实的 Twitter 数据，验证了结果和所提算法的有效性。

社交学习网络中的因果影响

Causal Influences over Social Learning Networks

Influence maximization is the problem of selecting top $k$ seed nodes in a
social network to maximize their influence coverage under certain influence
diffusion models. In this paper, we propose a novel algorithm IRIE that
integrates a new message passing based influence ranking (IR), and influence
estimation (IE) methods for influence maximization in both the independent
cascade (IC) model and its extension IC-N that incorporates negative opinion
propagations. Through extensive experiments, we demonstrate that IRIE matches
the influence coverage of other algorithms while scales much better than all
other algorithms. Moreover IRIE is more robust and stable than other algorithms
both in running time and memory usage for various density of networks and
cascade size. It runs up to two orders of magnitude faster than other
state-of-the-art algorithms such as PMIA for large networks with tens of
millions of nodes and edges, while using only a fraction of memory comparing
with PMIA.

本文提出一种新的集成信息传递、影响排名和影响评估方法的算法 IRIE，用于在独立级联（IC）模型及其扩展中最大化影响力覆盖，并通过实验验证了 IRIE 在不同密度的网络和级联大小情况下比其他算法更具有鲁棒性和稳定性。