Bayesian Inference of a Social Graph with Trace Feasibility Guarantees
Effrosyni Papanastasiou, Anastasios Giovanidis
备注：
Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Nov 2021, The Hague, Netherlands
发表信息：
ASONAM '21: Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining - November 2021
Network inference is the process of deciding what is the true unknown graph underlying a set of interactions between nodes. There is a vast literature on the subject, but most known methods have an important drawback: the inferred graph is not guaranteed to explain every interaction from the input trace. We consider this an important issue since such inferred graph cannot be used as input for applications that require a reliable estimate of the true graph. On the other hand, a graph having trace feasibility guarantees can help us better understand the true (hidden) interactions that may have taken place between nodes of interest. The inference of such graph is the goal of this paper. Firstly, given an activity log from a social network, we introduce a set of constraints that take into consideration all the hidden paths that are possible between the nodes of the trace, given their timestamps of interaction. Then, we develop a nontrivial modification of the Expectation-Maximization algorithm by Newman [1], that we call Constrained-EM, which incorporates the constraints and a set of auxiliary variables into the inference process to guide it towards the feasibility of the trace. Experimental results on real-world data from Twitter confirm that Constrained-EM generates a posterior distribution of graphs that explains all the events observed in the trace while presenting the desired properties of a scale-free, small-world graph. Our method also outperforms established methods in terms of feasibility and quality of the inferred graph.
目前，大多数在线社交网络（OSN）以集中的方式保密其数据。研究人员被允许抓取底层的社交图谱（和数据），但速度有限，导致只能看到真实社交图谱的部分视图。为了克服这个限制，我们可以从用户的角度出发，OSN的贡献者。更准确地说，如果用户谨慎地彼此合作，他们可以使用OSN的基础设施与邻居进行多轮的嘈杂朋友列表交换。最后，他们可以构建本地子图，也称为真实社交图的本地视图。在本文中，我们提出了这样的协议的问题，\textit{private link exchange}在社会图。这个问题是独特的，因为通过链接传播的数据就是链接本身。然而，这一模式的可行性存在根本性问题。第一个问题是如何为链接交换过程定义简单有效的隐私概念。第二个问题来自于交换中的大量链接列表，这些列表可能会一轮又一轮地呈指数级增长。虽然存储和计算复杂性对于台式PC来说可能是负担得起的，但通信成本却不小。我们通过一个简单的$(α,β)$交换使用布隆过滤器来解决这两个问题。
